Using AI to Increase Your Intelligence & Enrich Humanity | Dr. Fei-Fei Li

I think the biggest thing humanity never I think the biggest thing humanity never learns is the older generation lamenting learns is the older generation lamenting learns is the older generation lamenting about the future generation as if the about the future generation as if the about the future generation as if the future generation doesn't know anything. future generation doesn't know anything. future generation doesn't know anything. They're rude. They're they're they're They're rude. They're they're they're They're rude. They're they're they're forgetting the past. But if you look at forgetting the past. But if you look at forgetting the past. But if you look at arc of history, of humanity, by and arc of history, of humanity, by and arc of history, of humanity, by and large, we advance for the better. Now, large, we advance for the better. Now, large, we advance for the better. Now, I'm not denying the atrocities. I'm not I'm not denying the atrocities. I'm not I'm not denying the atrocities. I'm not denying the setbacks. I'm not denying denying the setbacks. I'm not denying denying the setbacks. I'm not denying this. But fundamentally I'm a optimist this. But fundamentally I'm a optimist this. But fundamentally I'm a optimist in humanity. I look at kids, they're in humanity. I look at kids, they're in humanity. I look at kids, they're curious. Of course, they get massively curious. Of course, they get massively curious. Of course, they get massively entertained by this technology, but they entertained by this technology, but they entertained by this technology, but they also are starting to use it. What I also are starting to use it. What I also are starting to use it. What I worry about are teachers and some worry about are teachers and some worry about are teachers and some parents because I think our society parents because I think our society parents because I think our society today and especially Silicon Valley are today and especially Silicon Valley are today and especially Silicon Valley are not doing them a service. We're not doing them a service. We're not doing them a service. We're forgetting about them. forgetting about them. forgetting about them. Hey everyone. To celebrate the launch of Hey everyone. To celebrate the launch of Hey everyone. To celebrate the launch of my new book entitled Protocols, I'm my new book entitled Protocols, I'm my new book entitled Protocols, I'm pleased to share that I'll be hosting pleased to share that I'll be hosting pleased to share that I'll be hosting three live events very soon. The first three live events very soon. The first three live events very soon. The first live event is in New York City at Radio live event is in New York City at Radio live event is in New York City at Radio City Music Hall on September 17th. The City Music Hall on September 17th. The City Music Hall on September 17th. The second event is in Los Angeles at the second event is in Los Angeles at the second event is in Los Angeles at the Dolby Theater on October 8th. And the Dolby Theater on October 8th. And the Dolby Theater on October 8th. And the third live event is in San Francisco at third live event is in San Francisco at third live event is in San Francisco at the Masonic on October 28th. At each of the Masonic on October 28th. At each of the Masonic on October 28th. At each of these events, I'll be discussing topics these events, I'll be discussing topics these events, I'll be discussing topics from the book and my favorite part, from the book and my favorite part, from the book and my favorite part, taking questions directly from you, the taking questions directly from you, the taking questions directly from you, the audience. To get tickets, you can go to audience. To get tickets, you can go to audience. To get tickets, you can go to hubermanlab.com/events hubermanlab.com/events hubermanlab.com/events and use the code protocols to get early and use the code protocols to get early and use the code protocols to get early access. Again, that's access. Again, that's access. Again, that's hubermanlab.com/events hubermanlab.com/events hubermanlab.com/events and use the code protocols to get early and use the code protocols to get early and use the code protocols to get early access to tickets. Welcome to the access to tickets. Welcome to the access to tickets. Welcome to the Hubberman Lab podcast where we discuss Hubberman Lab podcast where we discuss Hubberman Lab podcast where we discuss science [music] and science-based tools science [music] and science-based tools science [music] and science-based tools for everyday life. for everyday life. for everyday life. [music] [music] [music] I'm Andrew Huberman and I'm a professor I'm Andrew Huberman and I'm a professor I'm Andrew Huberman and I'm a professor of neurobiology and opthalmology at of neurobiology and opthalmology at of neurobiology and opthalmology at Stanford School of Medicine. My guest Stanford School of Medicine. My guest Stanford School of Medicine. My guest today is Dr. Fay Lee, a computer today is Dr. Fay Lee, a computer today is Dr. Fay Lee, a computer scientist and professor at Stanford and scientist and professor at Stanford and scientist and professor at Stanford and one of the pioneers and luminaries of one of the pioneers and luminaries of one of the pioneers and luminaries of artificial intelligence and computer artificial intelligence and computer artificial intelligence and computer vision. As you all know, millions of vision. As you all know, millions of vision. As you all know, millions of people use AI chat bots to look up people use AI chat bots to look up people use AI chat bots to look up information every single day. And of information every single day. And of information every single day. And of course, many people are concerned about course, many people are concerned about course, many people are concerned about AI, where it's going, and how it might AI, where it's going, and how it might AI, where it's going, and how it might replace certain human jobs or degrade replace certain human jobs or degrade replace certain human jobs or degrade our experience of life in one way or our experience of life in one way or our experience of life in one way or another. Today we discuss from a another. Today we discuss from a another. Today we discuss from a neuroscience perspective what neuroscience perspective what neuroscience perspective what intelligence really is and the ways that intelligence really is and the ways that intelligence really is and the ways that AI can and is being used for good AI can and is being used for good AI can and is being used for good meaning to truly enhance learning health meaning to truly enhance learning health meaning to truly enhance learning health and to enrich rather than diminish the and to enrich rather than diminish the and to enrich rather than diminish the human experience. We start off by human experience. We start off by human experience. We start off by talking about how human brains of all talking about how human brains of all talking about how human brains of all ages learn new information. What rules ages learn new information. What rules ages learn new information. What rules the brain follows in that process and the brain follows in that process and the brain follows in that process and how AI because it is based on the how AI because it is based on the how AI because it is based on the content of the internet both resembles content of the internet both resembles content of the internet both resembles and falls short of what human brains can and falls short of what human brains can and falls short of what human brains can learn. and we discuss exciting uses of learn. and we discuss exciting uses of learn. and we discuss exciting uses of AI and robotics in medicine. To be AI and robotics in medicine. To be AI and robotics in medicine. To be clear, FFE acknowledges and addresses clear, FFE acknowledges and addresses clear, FFE acknowledges and addresses the many valid concerns about AI. But as the many valid concerns about AI. But as the many valid concerns about AI. But as the director of the Stanford Institute the director of the Stanford Institute the director of the Stanford Institute for Human- Centered Artificial for Human- Centered Artificial for Human- Centered Artificial Intelligence, her goal is to make sure Intelligence, her goal is to make sure Intelligence, her goal is to make sure that humans and humanity at large are that humans and humanity at large are that humans and humanity at large are represented in where AI goes next. As represented in where AI goes next. As represented in where AI goes next. As you'll soon hear, Dr. Fa Lee is an you'll soon hear, Dr. Fa Lee is an you'll soon hear, Dr. Fa Lee is an extraordinary scientist and educator. extraordinary scientist and educator. extraordinary scientist and educator. She has been called the godmother of AI She has been called the godmother of AI She has been called the godmother of AI for her ushering in of AI technologies, for her ushering in of AI technologies, for her ushering in of AI technologies, but also for her insistence that the but also for her insistence that the but also for her insistence that the ethics and benevolent uses of AI stay ethics and benevolent uses of AI stay ethics and benevolent uses of AI stay central to AI and robotics. So whether central to AI and robotics. So whether central to AI and robotics. So whether you are young or old, today's you are young or old, today's you are young or old, today's conversation will inform and empower you conversation will inform and empower you conversation will inform and empower you to understand and use AI in ways that to understand and use AI in ways that to understand and use AI in ways that truly benefit you and enrich your life. truly benefit you and enrich your life. truly benefit you and enrich your life. Before we begin, I'd like to emphasize Before we begin, I'd like to emphasize Before we begin, I'd like to emphasize that this podcast is separate from my that this podcast is separate from my that this podcast is separate from my teaching and research roles at Stanford. teaching and research roles at Stanford. teaching and research roles at Stanford. It is however part of my desire and It is however part of my desire and It is however part of my desire and effort to bring zero cost to consumer effort to bring zero cost to consumer effort to bring zero cost to consumer information about science and science information about science and science information about science and science related tools to the general public. In related tools to the general public. In related tools to the general public. In keeping with that theme, today's episode keeping with that theme, today's episode keeping with that theme, today's episode does include sponsors. And now for my does include sponsors. And now for my does include sponsors. And now for my discussion with Dr. Fay Lee. Dr. Fay discussion with Dr. Fay Lee. Dr. Fay discussion with Dr. Fay Lee. Dr. Fay Lee, welcome. Lee, welcome. Lee, welcome. >> Thank you. I'm excited to be here, >> Thank you. I'm excited to be here, >> Thank you. I'm excited to be here, Andrew. Andrew. Andrew. >> Yeah, this is a long time coming. And >> Yeah, this is a long time coming. And >> Yeah, this is a long time coming. And yes, yes, yes, >> you are a luminary in this AI field, but >> you are a luminary in this AI field, but >> you are a luminary in this AI field, but I also consider you a neuroscientist and I also consider you a neuroscientist and I also consider you a neuroscientist and computer scientist, and we share a computer scientist, and we share a computer scientist, and we share a common path through vision science. And common path through vision science. And common path through vision science. And so I'd like so I'd like so I'd like >> and fellow colleagues >> and fellow colleagues >> and fellow colleagues >> and fellow colleagues at Stanford. So >> and fellow colleagues at Stanford. So >> and fellow colleagues at Stanford. So I'd like to start in vision. What is so I'd like to start in vision. What is so I'd like to start in vision. What is so special about vision and seeing and special about vision and seeing and special about vision and seeing and light as it pertains to AI and where light as it pertains to AI and where light as it pertains to AI and where it's all going? Because I think for most it's all going? Because I think for most it's all going? Because I think for most people those probably sound like very people those probably sound like very people those probably sound like very divorced themes but actually that's divorced themes but actually that's divorced themes but actually that's where it all starts. where it all starts. where it all starts. >> Yeah. I see vision as a cornerstone of >> Yeah. I see vision as a cornerstone of >> Yeah. I see vision as a cornerstone of intelligence in almost two parallel way. intelligence in almost two parallel way. intelligence in almost two parallel way. One is what evolution has taught us. You One is what evolution has taught us. You One is what evolution has taught us. You know what's the evolution of vision and know what's the evolution of vision and know what's the evolution of vision and animal intelligence and human animal intelligence and human animal intelligence and human intelligence. The other one is computer intelligence. The other one is computer intelligence. The other one is computer vision and AI what that relationship is. vision and AI what that relationship is. vision and AI what that relationship is. So I'll go into each evolution. I always So I'll go into each evolution. I always So I'll go into each evolution. I always say that 540 million years ago animals say that 540 million years ago animals say that 540 million years ago animals saw the first light. These are simple saw the first light. These are simple saw the first light. These are simple sea ocean animals, trilobytes and and sea ocean animals, trilobytes and and sea ocean animals, trilobytes and and the the cousins. And before that there the the cousins. And before that there the the cousins. And before that there was very little sensing. Uh around that was very little sensing. Uh around that was very little sensing. Uh around that same time tactile and haptics was same time tactile and haptics was same time tactile and haptics was starting also to emerge in animal bodies starting also to emerge in animal bodies starting also to emerge in animal bodies but but there was no hearing. There's no but but there was no hearing. There's no but but there was no hearing. There's no you know smelling there's no but there's you know smelling there's no but there's you know smelling there's no but there's absolutely no nervous system. But the absolutely no nervous system. But the absolutely no nervous system. But the first photoreceptive first photoreceptive first photoreceptive cells created a evolutionary force that cells created a evolutionary force that cells created a evolutionary force that propelled animals to evolve because propelled animals to evolve because propelled animals to evolve because sensing the external world changes your sensing the external world changes your sensing the external world changes your self-perception changes the way your self-perception changes the way your self-perception changes the way your relationship with the external world. To relationship with the external world. To relationship with the external world. To put it simply, if you seek you can see put it simply, if you seek you can see put it simply, if you seek you can see food, it changes your your life, right? food, it changes your your life, right? food, it changes your your life, right? from a evolution point of view and you from a evolution point of view and you from a evolution point of view and you become someone else's food and also become someone else's food and also become someone else's food and also you're actively seeking food. You're you're actively seeking food. You're you're actively seeking food. You're actively seeking mates and and and all actively seeking mates and and and all actively seeking mates and and and all that. So really because of sensing and that. So really because of sensing and that. So really because of sensing and perception evolution took a incredibly perception evolution took a incredibly perception evolution took a incredibly accelerated pace in terms of uh animal accelerated pace in terms of uh animal accelerated pace in terms of uh animal speciation. Fossil studies have told us speciation. Fossil studies have told us speciation. Fossil studies have told us that 10 million years after the first uh that 10 million years after the first uh that 10 million years after the first uh light for animals light for animals light for animals was what we call the the big ban of was what we call the the big ban of was what we call the the big ban of evolution or Cambrian explosion of evolution or Cambrian explosion of evolution or Cambrian explosion of animal speciation. And fast forward I animal speciation. And fast forward I animal speciation. And fast forward I think vision has always played a huge think vision has always played a huge think vision has always played a huge role in not only in the early evolution role in not only in the early evolution role in not only in the early evolution of animals but as well as um advanced of animals but as well as um advanced of animals but as well as um advanced intelligence and how that emerged. You intelligence and how that emerged. You intelligence and how that emerged. You and I are both vision student and and and I are both vision student and and and I are both vision student and and scientists. It is estimated half of the scientists. It is estimated half of the scientists. It is estimated half of the cortical AC activities in human brain is cortical AC activities in human brain is cortical AC activities in human brain is involved in visual function. involved in visual function. involved in visual function. Children were first visual before they Children were first visual before they Children were first visual before they were verbal in development. So vision were verbal in development. So vision were verbal in development. So vision really to this day plays a central role really to this day plays a central role really to this day plays a central role in both the evolution of animal in both the evolution of animal in both the evolution of animal intelligence as well as in the daily intelligence as well as in the daily intelligence as well as in the daily life of human human life. Now in life of human human life. Now in life of human human life. Now in parallel, vision as a uh as a discipline parallel, vision as a uh as a discipline parallel, vision as a uh as a discipline or as a area of uh artificial or as a area of uh artificial or as a area of uh artificial intelligence was really played a pivotal intelligence was really played a pivotal intelligence was really played a pivotal role in what we see as this modern AI role in what we see as this modern AI role in what we see as this modern AI moment in a couple of ways. First of all moment in a couple of ways. First of all moment in a couple of ways. First of all is the the uh algorithms the neuronet is the the uh algorithms the neuronet is the the uh algorithms the neuronet network algorithms. Neural network network algorithms. Neural network network algorithms. Neural network algorithms were first computer algorithms were first computer algorithms were first computer scientists start dabbling that in the scientists start dabbling that in the scientists start dabbling that in the early 1950s. early 1950s. early 1950s. And [snorts] Andrew, you might remember And [snorts] Andrew, you might remember And [snorts] Andrew, you might remember what's happening on the neuros side in what's happening on the neuros side in what's happening on the neuros side in the early 1950s the early 1950s the early 1950s is that neuroscientists like Hubo and is that neuroscientists like Hubo and is that neuroscientists like Hubo and Viso were starting to record visual Viso were starting to record visual Viso were starting to record visual cells in malian brain and starting to cells in malian brain and starting to cells in malian brain and starting to realize there is a hierarchical realize there is a hierarchical realize there is a hierarchical structure of nervous cells that stack structure of nervous cells that stack structure of nervous cells that stack against each other and pass against each other and pass against each other and pass neuroinformation neuroinformation neuroinformation across these hierarchy. And it goes from across these hierarchy. And it goes from across these hierarchy. And it goes from you know collecting light from retina you know collecting light from retina you know collecting light from retina all the way to recognizing there is a all the way to recognizing there is a all the way to recognizing there is a shape in front of you. And that very shape in front of you. And that very shape in front of you. And that very neuro architecture that we see in neuro architecture that we see in neuro architecture that we see in mamalian brain is also part of the mamalian brain is also part of the mamalian brain is also part of the inspiration of neuronet network inspiration of neuronet network inspiration of neuronet network algorithm. Now today's neuronet network algorithm. Now today's neuronet network algorithm. Now today's neuronet network algorithm runs on hundreds of billions algorithm runs on hundreds of billions algorithm runs on hundreds of billions and even trillion of parameters. It has and even trillion of parameters. It has and even trillion of parameters. It has the complexity that departs from what we the complexity that departs from what we the complexity that departs from what we recorded in the mamalio uh brain or the recorded in the mamalio uh brain or the recorded in the mamalio uh brain or the visual pathway but the origin is very visual pathway but the origin is very visual pathway but the origin is very close to each other about half a century close to each other about half a century close to each other about half a century ago um a little more than half a century ago um a little more than half a century ago um a little more than half a century ago. That's one aspect of uh vision's ago. That's one aspect of uh vision's ago. That's one aspect of uh vision's contribution to AI. There is another contribution to AI. There is another contribution to AI. There is another aspect of vision's contribution to AI aspect of vision's contribution to AI aspect of vision's contribution to AI that is also pivotal which is through that is also pivotal which is through that is also pivotal which is through big data is that that comes closer to my big data is that that comes closer to my big data is that that comes closer to my own work is that AI around the century own work is that AI around the century own work is that AI around the century was a field of machine learning a lot of was a field of machine learning a lot of was a field of machine learning a lot of different labs different research different labs different research different labs different research scientists were were trying out scientists were were trying out scientists were were trying out different algorithms and it's not just different algorithms and it's not just different algorithms and it's not just neuronet network there are other methods neuronet network there are other methods neuronet network there are other methods jargon words like Beijian methods, jargon words like Beijian methods, jargon words like Beijian methods, support vector machine methods. It support vector machine methods. It support vector machine methods. It doesn't matter what these methods are, doesn't matter what these methods are, doesn't matter what these methods are, but it's a explorative phase that we're but it's a explorative phase that we're but it's a explorative phase that we're trying to get these algorithms to work trying to get these algorithms to work trying to get these algorithms to work so that we can empower the machine to so that we can empower the machine to so that we can empower the machine to read or to see. A group of us computer read or to see. A group of us computer read or to see. A group of us computer vision scientists vision scientists vision scientists were struggling with these algorithms were struggling with these algorithms were struggling with these algorithms and uh I was a very young faculty and uh I was a very young faculty and uh I was a very young faculty um first year faculty 2006 at Princeton um first year faculty 2006 at Princeton um first year faculty 2006 at Princeton and my students and I are looking at and my students and I are looking at and my students and I are looking at these algorithms and how little data these algorithms and how little data these algorithms and how little data were fed into these algorithms to learn. were fed into these algorithms to learn. were fed into these algorithms to learn. So I turned to cognitive neuroscience So I turned to cognitive neuroscience So I turned to cognitive neuroscience literature per namely vision literature literature per namely vision literature literature per namely vision literature and started to study how much humans and started to study how much humans and started to study how much humans learn, how much humans can see and the learn, how much humans can see and the learn, how much humans can see and the numbers were incredible. Humans were by numbers were incredible. Humans were by numbers were incredible. Humans were by age six can learn tens of thousands of age six can learn tens of thousands of age six can learn tens of thousands of different object categories and the different object categories and the different object categories and the exposure to visual world is also exposure to visual world is also exposure to visual world is also massive. Right? babies can see the mo massive. Right? babies can see the mo massive. Right? babies can see the mo most of the time the moment they're most of the time the moment they're most of the time the moment they're born. So they're inundated with this big born. So they're inundated with this big born. So they're inundated with this big data. So we conjectured that the lack of data. So we conjectured that the lack of data. So we conjectured that the lack of data was a huge part of the reason data was a huge part of the reason data was a huge part of the reason that's the lack of progress in AI. So we that's the lack of progress in AI. So we that's the lack of progress in AI. So we took a departure from everybody else who took a departure from everybody else who took a departure from everybody else who are really focusing only on algorithm are really focusing only on algorithm are really focusing only on algorithm and said that we need data. we need data and said that we need data. we need data and said that we need data. we need data to drive these algorithms. So long story to drive these algorithms. So long story to drive these algorithms. So long story short, we led this um image that project short, we led this um image that project short, we led this um image that project that collected the first ever internet that collected the first ever internet that collected the first ever internet scale large data set for the field of scale large data set for the field of scale large data set for the field of artificial intelligence, but really artificial intelligence, but really artificial intelligence, but really through the field of vision because through the field of vision because through the field of vision because imageet is a collection of 15 million imageet is a collection of 15 million imageet is a collection of 15 million images. And the goal of imageet was to images. And the goal of imageet was to images. And the goal of imageet was to drive machines to recognize everyday drive machines to recognize everyday drive machines to recognize everyday objects, you know, microphones, cups, objects, you know, microphones, cups, objects, you know, microphones, cups, chairs. And that work converged with the chairs. And that work converged with the chairs. And that work converged with the advances in neuronet network algorithm advances in neuronet network algorithm advances in neuronet network algorithm as well as in GPU computing. And by 2012 as well as in GPU computing. And by 2012 as well as in GPU computing. And by 2012 that work uh that the convergence of the that work uh that the convergence of the that work uh that the convergence of the three elements of modern AI became the three elements of modern AI became the three elements of modern AI became the defining moment defining moment defining moment of what um what modern AI is. I recall of what um what modern AI is. I recall of what um what modern AI is. I recall somewhere around 2012 it seems there was somewhere around 2012 it seems there was somewhere around 2012 it seems there was this debate at this vision course at this debate at this vision course at this debate at this vision course at Cold Spring Harbor that was held every Cold Spring Harbor that was held every Cold Spring Harbor that was held every other summer like could a computer learn other summer like could a computer learn other summer like could a computer learn to recognize specific faces as well as to recognize specific faces as well as to recognize specific faces as well as humans. Now I think most people would humans. Now I think most people would humans. Now I think most people would say computers are actually much better say computers are actually much better say computers are actually much better at it than humans are even though you at it than humans are even though you at it than humans are even though you have these super super recognizer people have these super super recognizer people have these super super recognizer people who are exceptional at this. who are exceptional at this. who are exceptional at this. >> Could you tell us how is it that this >> Could you tell us how is it that this >> Could you tell us how is it that this technology went from technology went from technology went from a state basically where it would confuse a state basically where it would confuse a state basically where it would confuse you and maybe a a a cousin or or even you and maybe a a a cousin or or even you and maybe a a a cousin or or even someone that looks somewhat like you someone that looks somewhat like you someone that looks somewhat like you could could could >> or to the point where uh to the point >> or to the point where uh to the point >> or to the point where uh to the point where now it is exquisitely precise. where now it is exquisitely precise. where now it is exquisitely precise. >> How do we get here? I want to definitely >> How do we get here? I want to definitely >> How do we get here? I want to definitely double triple click on the convergence double triple click on the convergence double triple click on the convergence of this technology. I think around the of this technology. I think around the of this technology. I think around the second decade of 21st century. So like second decade of 21st century. So like second decade of 21st century. So like you said around 2012 you said around 2012 you said around 2012 the the huge convergence was the the the huge convergence was the the the huge convergence was the capability of GPU computing which capability of GPU computing which capability of GPU computing which basically accelerated or parallelized basically accelerated or parallelized basically accelerated or parallelized computing so that you can have more computing so that you can have more computing so that you can have more flops going through algorithms right you flops going through algorithms right you flops going through algorithms right you need that speed then you also have a um need that speed then you also have a um need that speed then you also have a um after many decades of research neuronet after many decades of research neuronet after many decades of research neuronet network algorithm them is getting more network algorithm them is getting more network algorithm them is getting more mature. Um you know starting as we said mature. Um you know starting as we said mature. Um you know starting as we said 1950s people start to um create these 1950s people start to um create these 1950s people start to um create these very simple algorithm that behaves very simple algorithm that behaves very simple algorithm that behaves similarly to neurons but much simpler. similarly to neurons but much simpler. similarly to neurons but much simpler. Neurons as you know are very complex but Neurons as you know are very complex but Neurons as you know are very complex but here the idea is that you have one unit here the idea is that you have one unit here the idea is that you have one unit of node that takes some some input and of node that takes some some input and of node that takes some some input and outputs another input and within it it's outputs another input and within it it's outputs another input and within it it's just a function a very simple function. just a function a very simple function. just a function a very simple function. So you stack them together. That's what So you stack them together. That's what So you stack them together. That's what neuronet network is. But by by the time neuronet network is. But by by the time neuronet network is. But by by the time it's in the um after you know around 20 it's in the um after you know around 20 it's in the um after you know around 20 uh 2010ish the maturity of these uh 2010ish the maturity of these uh 2010ish the maturity of these algorithms have have gotten to a level algorithms have have gotten to a level algorithms have have gotten to a level that it's it's becoming really good. But that it's it's becoming really good. But that it's it's becoming really good. But also last but not the least the also last but not the least the also last but not the least the recognition of big data. Internet recognition of big data. Internet recognition of big data. Internet definitely fueled that. It made data definitely fueled that. It made data definitely fueled that. It made data more available. But the reckoning moment more available. But the reckoning moment more available. But the reckoning moment of wow big data needs to be part of that of wow big data needs to be part of that of wow big data needs to be part of that equation. We need to use big data to equation. We need to use big data to equation. We need to use big data to drive these algorithm to learn these drive these algorithm to learn these drive these algorithm to learn these patterns. So this convergence of these patterns. So this convergence of these patterns. So this convergence of these three things really set off um the the three things really set off um the the three things really set off um the the the the the revolution of AI. The specific moment is revolution of AI. The specific moment is revolution of AI. The specific moment is also worth mentioning because you also worth mentioning because you also worth mentioning because you mentioned face recognition is this image mentioned face recognition is this image mentioned face recognition is this image net challenge. My lab put forward that net challenge. My lab put forward that net challenge. My lab put forward that starting 2010 after we collected this starting 2010 after we collected this starting 2010 after we collected this humongous data set, we at that point GPU humongous data set, we at that point GPU humongous data set, we at that point GPU was not yet mature uh and and uh we put was not yet mature uh and and uh we put was not yet mature uh and and uh we put out a uh public challenge for the out a uh public challenge for the out a uh public challenge for the research community uh for for multiple research community uh for for multiple research community uh for for multiple years in a row and invited people to years in a row and invited people to years in a row and invited people to solve this major computer vision problem solve this major computer vision problem solve this major computer vision problem called object recognition. The task was called object recognition. The task was called object recognition. The task was very easy. We have a data set of a very easy. We have a data set of a very easy. We have a data set of a thousand different categories of objects thousand different categories of objects thousand different categories of objects and this data set is more than a million and this data set is more than a million and this data set is more than a million images large. It's what we call the images large. It's what we call the images large. It's what we call the testing data set and uh the task for the testing data set and uh the task for the testing data set and uh the task for the algorithm is I'll show you a picture. algorithm is I'll show you a picture. algorithm is I'll show you a picture. You have to name the the main objects You have to name the the main objects You have to name the the main objects inside and if you guess right you're you inside and if you guess right you're you inside and if you guess right you're you you get a point. If you guess wrong you you get a point. If you guess wrong you you get a point. If you guess wrong you don't get a point. So that image that don't get a point. So that image that don't get a point. So that image that challenge uh we later a couple of years challenge uh we later a couple of years challenge uh we later a couple of years later benchmarked human performance by a later benchmarked human performance by a later benchmarked human performance by a very smart graduate student at Stanford very smart graduate student at Stanford very smart graduate student at Stanford and that was roughly 4%. So random and that was roughly 4%. So random and that was roughly 4%. So random chance will be one over a thousand chance will be one over a thousand chance will be one over a thousand >> right? So 4% for humans is not that bad. >> right? So 4% for humans is not that bad. >> right? So 4% for humans is not that bad. The first few years machines were not as The first few years machines were not as The first few years machines were not as good as humans. The turning point was good as humans. The turning point was good as humans. The turning point was 2012 the convergence of neuronet network 2012 the convergence of neuronet network 2012 the convergence of neuronet network image net data set and GPU even that image net data set and GPU even that image net data set and GPU even that year even though the error rate was was year even though the error rate was was year even though the error rate was was cut um to oh by the way the human cut um to oh by the way the human cut um to oh by the way the human performance error rate was 4%. Sorry I I performance error rate was 4%. Sorry I I performance error rate was 4%. Sorry I I need to correct that the error rate was need to correct that the error rate was need to correct that the error rate was cut down to to the teens. It wasn't cut down to to the teens. It wasn't cut down to to the teens. It wasn't where human performance was. So this is where human performance was. So this is where human performance was. So this is looking at images and and assigning a a looking at images and and assigning a a looking at images and and assigning a a a a a >> one out of a thousand labels. >> one out of a thousand labels. >> one out of a thousand labels. >> Got it. >> Got it. >> Got it. >> Yeah. But 2012 was so momentous that >> Yeah. But 2012 was so momentous that >> Yeah. But 2012 was so momentous that year because the error rate from year because the error rate from year because the error rate from previous algorithm dropped a lot by this previous algorithm dropped a lot by this previous algorithm dropped a lot by this neuronet network algorithm. And we know neuronet network algorithm. And we know neuronet network algorithm. And we know in the research community when something in the research community when something in the research community when something this drastic happens it it means a this drastic happens it it means a this drastic happens it it means a inflection point. But it still took inflection point. But it still took inflection point. But it still took another another another three years I remember by 2012 2016 for three years I remember by 2012 2016 for three years I remember by 2012 2016 for the algorithm to beat humans in in the algorithm to beat humans in in the algorithm to beat humans in in naming a thousand objects. naming a thousand objects. naming a thousand objects. >> Could I ask you where this 4% error is >> Could I ask you where this 4% error is >> Could I ask you where this 4% error is coming from in this very smart graduate coming from in this very smart graduate coming from in this very smart graduate student? Is it that they don't recognize student? Is it that they don't recognize student? Is it that they don't recognize the objects or it's a recognition the objects or it's a recognition the objects or it's a recognition against time pressure? like they have to against time pressure? like they have to against time pressure? like they have to they're being fed images fast enough they're being fed images fast enough they're being fed images fast enough that occasionally they do an incorrect that occasionally they do an incorrect that occasionally they do an incorrect assignment. assignment. assignment. >> I don't think the time pressure was the >> I don't think the time pressure was the >> I don't think the time pressure was the main issue even though for a graduate main issue even though for a graduate main issue even though for a graduate student to do this I don't think they student to do this I don't think they student to do this I don't think they want to do this forever. Um but I think want to do this forever. Um but I think want to do this forever. Um but I think you know the the human brain as you know you know the the human brain as you know you know the the human brain as you know has limited memory whether it's has limited memory whether it's has limited memory whether it's long-term or short-term right so long-term or short-term right so long-term or short-term right so retaining the patterns of a thousand retaining the patterns of a thousand retaining the patterns of a thousand object classes even if some classes object classes even if some classes object classes even if some classes you're you're familiar is is not that you're you're familiar is is not that you're you're familiar is is not that easy easy easy >> you know so so I think there is the >> you know so so I think there is the >> you know so so I think there is the confusion and and also for example confusion and and also for example confusion and and also for example different species of dogs gets really different species of dogs gets really different species of dogs gets really close. close. close. >> Mhm. >> Mhm. >> Mhm. >> And that that's a challenge. >> And that that's a challenge. >> And that that's a challenge. >> I'd like to take a quick break and >> I'd like to take a quick break and >> I'd like to take a quick break and acknowledge our sponsor, Lingo. Lingo is acknowledge our sponsor, Lingo. Lingo is acknowledge our sponsor, Lingo. Lingo is an everyday wearable that tracks your an everyday wearable that tracks your an everyday wearable that tracks your glucose 24/7. Glucose drives a lot of glucose 24/7. Glucose drives a lot of glucose 24/7. Glucose drives a lot of key processes that support energy, body key processes that support energy, body key processes that support energy, body composition, and long-term health. When composition, and long-term health. When composition, and long-term health. When glucose is constantly spiking and glucose is constantly spiking and glucose is constantly spiking and crashing, that's where we can start to crashing, that's where we can start to crashing, that's where we can start to see metabolic dysfunction. And over see metabolic dysfunction. And over see metabolic dysfunction. And over time, that can even progress to time, that can even progress to time, that can even progress to pre-diabetes. Right now, about 115 pre-diabetes. Right now, about 115 pre-diabetes. Right now, about 115 million adults in the US have million adults in the US have million adults in the US have pre-diabetes. Most don't know it, and a pre-diabetes. Most don't know it, and a pre-diabetes. Most don't know it, and a higher percentage of men have it than higher percentage of men have it than higher percentage of men have it than women do. Often, there aren't clear women do. Often, there aren't clear women do. Often, there aren't clear symptoms of pre-diabetes early on, so symptoms of pre-diabetes early on, so symptoms of pre-diabetes early on, so people don't tend to look into it. But people don't tend to look into it. But people don't tend to look into it. But the fact is that metabolic health is the fact is that metabolic health is the fact is that metabolic health is shaping how your body functions every shaping how your body functions every shaping how your body functions every day, whether you feel it or not. day, whether you feel it or not. day, whether you feel it or not. Tracking your glucose with Lingo can Tracking your glucose with Lingo can Tracking your glucose with Lingo can help you see how food, activity, and help you see how food, activity, and help you see how food, activity, and stress impact your glucose throughout stress impact your glucose throughout stress impact your glucose throughout the day. I personally have used Lingo the day. I personally have used Lingo the day. I personally have used Lingo and it's been an invaluable tool for and it's been an invaluable tool for and it's been an invaluable tool for improving my metabolic health. If you improving my metabolic health. If you improving my metabolic health. If you would like to try Lingo, Hubberman Lab would like to try Lingo, Hubberman Lab would like to try Lingo, Hubberman Lab listeners in the US and UK can save 10% listeners in the US and UK can save 10% listeners in the US and UK can save 10% on a four-week plan. Just visit on a four-week plan. Just visit on a four-week plan. Just visit hellolingo.com/huberman hellolingo.com/huberman hellolingo.com/huberman for more information. Terms and for more information. Terms and for more information. Terms and conditions apply. Again, that's conditions apply. Again, that's conditions apply. Again, that's hellingo.com/huberman. hellingo.com/huberman. hellingo.com/huberman. Today's episode is also brought to us by Today's episode is also brought to us by Today's episode is also brought to us by Wealthfront. In today's financial Wealthfront. In today's financial Wealthfront. In today's financial landscape of constant market shifts and landscape of constant market shifts and landscape of constant market shifts and chaotic news, it's easy to feel chaotic news, it's easy to feel chaotic news, it's easy to feel uncertain about how to save and invest uncertain about how to save and invest uncertain about how to save and invest your money. Wealthfront is the solution your money. Wealthfront is the solution your money. Wealthfront is the solution that helps you take control of your that helps you take control of your that helps you take control of your money while managing risk. For nearly a money while managing risk. For nearly a money while managing risk. For nearly a decade, I've trusted Wealthfront to decade, I've trusted Wealthfront to decade, I've trusted Wealthfront to navigate this volatility. With the navigate this volatility. With the navigate this volatility. With the Wealthfront cash account, I can earn Wealthfront cash account, I can earn Wealthfront cash account, I can earn 3.3% annual percentage yield or APY on 3.3% annual percentage yield or APY on 3.3% annual percentage yield or APY on my cash from program banks. And I know my cash from program banks. And I know my cash from program banks. And I know my money is growing until I'm ready to my money is growing until I'm ready to my money is growing until I'm ready to spend it or invest it. One of the spend it or invest it. One of the spend it or invest it. One of the features I love about Wealthfront is features I love about Wealthfront is features I love about Wealthfront is that I have access to instant no fee that I have access to instant no fee that I have access to instant no fee withdrawals to eligible accounts 24/7. withdrawals to eligible accounts 24/7. withdrawals to eligible accounts 24/7. That means I can move my money where I That means I can move my money where I That means I can move my money where I need it without waiting. And when I'm need it without waiting. And when I'm need it without waiting. And when I'm ready to transition from saving to ready to transition from saving to ready to transition from saving to investing, Wealthfront lets me investing, Wealthfront lets me investing, Wealthfront lets me seamlessly transfer my funds into one of seamlessly transfer my funds into one of seamlessly transfer my funds into one of their expert-built portfolios. For a their expert-built portfolios. For a their expert-built portfolios. For a limited time, Wealthfront is offering limited time, Wealthfront is offering limited time, Wealthfront is offering the Huberman Lab audience an exclusive the Huberman Lab audience an exclusive the Huberman Lab audience an exclusive 75% APY boost over the base rate for 3 75% APY boost over the base rate for 3 75% APY boost over the base rate for 3 months, meaning you can get up to 4.05% months, meaning you can get up to 4.05% months, meaning you can get up to 4.05% 05% variable APY on up to $150,000 in 05% variable APY on up to $150,000 in 05% variable APY on up to $150,000 in deposits. Over 1 million people already deposits. Over 1 million people already deposits. Over 1 million people already trust Wealthfront to save more, earn trust Wealthfront to save more, earn trust Wealthfront to save more, earn more, and build long-term wealth with more, and build long-term wealth with more, and build long-term wealth with confidence. If you'd like to try confidence. If you'd like to try confidence. If you'd like to try Wealthfront, you can go to Wealthfront, you can go to Wealthfront, you can go to wealthfront.com/huberman wealthfront.com/huberman wealthfront.com/huberman to receive the boost offer and start to receive the boost offer and start to receive the boost offer and start earning 4.05% variable APY today. That's earning 4.05% variable APY today. That's earning 4.05% variable APY today. That's wealthfront.com/huberman wealthfront.com/huberman wealthfront.com/huberman to get started. This is a paid to get started. This is a paid to get started. This is a paid testimonial of Wealthfront. Client testimonial of Wealthfront. Client testimonial of Wealthfront. Client experiences will vary. Wealthfront experiences will vary. Wealthfront experiences will vary. Wealthfront brokerage is not a bank. The base APY is brokerage is not a bank. The base APY is brokerage is not a bank. The base APY is as of January 30th, 2026 and subject to as of January 30th, 2026 and subject to as of January 30th, 2026 and subject to change. For more information, please see change. For more information, please see change. For more information, please see the episode description. the episode description. the episode description. I can see the rationale for doing this I can see the rationale for doing this I can see the rationale for doing this in the vision domain. But has a similar in the vision domain. But has a similar in the vision domain. But has a similar thing been explored with hearing with thing been explored with hearing with thing been explored with hearing with sounds? I mean, it's, you know, as sounds? I mean, it's, you know, as sounds? I mean, it's, you know, as humans, we we are amazing at recognizing humans, we we are amazing at recognizing humans, we we are amazing at recognizing speech inflection, emotional tone, speech inflection, emotional tone, speech inflection, emotional tone, things like that. But if I had to things like that. But if I had to things like that. But if I had to discriminate, you know, even 15 discriminate, you know, even 15 discriminate, you know, even 15 different sound frequencies, I can tell different sound frequencies, I can tell different sound frequencies, I can tell you as a non-m musician, um, it would be you as a non-m musician, um, it would be you as a non-m musician, um, it would be very difficult for me. very difficult for me. very difficult for me. >> Absolutely. I think that what you see is >> Absolutely. I think that what you see is >> Absolutely. I think that what you see is the floodgate got open and every sub the floodgate got open and every sub the floodgate got open and every sub area of AI whether it's speech area of AI whether it's speech area of AI whether it's speech recognition sound recognition uh natural recognition sound recognition uh natural recognition sound recognition uh natural language processing which more than language processing which more than language processing which more than recognition uh vision all areas got recognition uh vision all areas got recognition uh vision all areas got really a boost in terms of the really a boost in terms of the really a boost in terms of the technology we have colleagues at uh technology we have colleagues at uh technology we have colleagues at uh Stanford who are studying whale sound Stanford who are studying whale sound Stanford who are studying whale sound right uh whale songs using machine right uh whale songs using machine right uh whale songs using machine learning and AI AI now and speech learning and AI AI now and speech learning and AI AI now and speech recognition is another area that did so recognition is another area that did so recognition is another area that did so well in the early days of this AI well in the early days of this AI well in the early days of this AI revolution and of course the technology revolution and of course the technology revolution and of course the technology continues to um advance by the time the continues to um advance by the time the continues to um advance by the time the transformer paper was uh published transformer paper was uh published transformer paper was uh published around 2016 2017 it quickly showed that around 2016 2017 it quickly showed that around 2016 2017 it quickly showed that it is even more powerful than the early it is even more powerful than the early it is even more powerful than the early imageet AlexNet algorithm there it was imageet AlexNet algorithm there it was imageet AlexNet algorithm there it was not the field of computer vision that not the field of computer vision that not the field of computer vision that made the next big uh progress. It's the made the next big uh progress. It's the made the next big uh progress. It's the field of natural language processing. So field of natural language processing. So field of natural language processing. So because the recipe hasn't changed now we because the recipe hasn't changed now we because the recipe hasn't changed now we have a even more powerful neuronet have a even more powerful neuronet have a even more powerful neuronet network algorithm called transformer but network algorithm called transformer but network algorithm called transformer but we have even more data on the internet we have even more data on the internet we have even more data on the internet from at least more readily available from at least more readily available from at least more readily available data on the internet in the form of data on the internet in the form of data on the internet in the form of texts and now we have more powerful texts and now we have more powerful texts and now we have more powerful GPUs. So companies like Open AI and GPUs. So companies like Open AI and GPUs. So companies like Open AI and Google quickly rallied beyond this this Google quickly rallied beyond this this Google quickly rallied beyond this this very important technology and um it very important technology and um it very important technology and um it still took still took still took about 5 years from about 5 years from about 5 years from 2017 to 2022 to get to the chat GPT 2017 to 2022 to get to the chat GPT 2017 to 2022 to get to the chat GPT moment in natural language. But that's moment in natural language. But that's moment in natural language. But that's yet another step forward. So I think for yet another step forward. So I think for yet another step forward. So I think for people who are not computer scientists people who are not computer scientists people who are not computer scientists nor neuroscientists, the um natural nor neuroscientists, the um natural nor neuroscientists, the um natural human uh experience will perhaps human uh experience will perhaps human uh experience will perhaps resonate with them and and maybe I can resonate with them and and maybe I can resonate with them and and maybe I can just frame my question through that just frame my question through that just frame my question through that lens. So when a child learns that lens. So when a child learns that lens. So when a child learns that there's something called a kitty cat, there's something called a kitty cat, there's something called a kitty cat, they go, "Oh, cat." Then they usually they go, "Oh, cat." Then they usually they go, "Oh, cat." Then they usually drop the kitty part. They may say kitty drop the kitty part. They may say kitty drop the kitty part. They may say kitty and then they learn cat. and then they learn cat. and then they learn cat. >> And if they have enough interactions >> And if they have enough interactions >> And if they have enough interactions with a cat, they'll realize what a cat with a cat, they'll realize what a cat with a cat, they'll realize what a cat is. Even if they see it from the side, is. Even if they see it from the side, is. Even if they see it from the side, from the back, and eventually if they from the back, and eventually if they from the back, and eventually if they see a tail that looks a little bit like see a tail that looks a little bit like see a tail that looks a little bit like a cat and it's, you know, behind some a cat and it's, you know, behind some a cat and it's, you know, behind some books, you say, "What is that?" They're books, you say, "What is that?" They're books, you say, "What is that?" They're very likely to say cat. Even if they've very likely to say cat. Even if they've very likely to say cat. Even if they've also seen foxes and other animals with also seen foxes and other animals with also seen foxes and other animals with tails, just based on their experience, tails, just based on their experience, tails, just based on their experience, they're making a probability judgment. they're making a probability judgment. they're making a probability judgment. And that's essentially what uh AI can And that's essentially what uh AI can And that's essentially what uh AI can do. That's essentially what machine do. That's essentially what machine do. That's essentially what machine learning can do. Mhm. learning can do. Mhm. learning can do. Mhm. >> But it seems to me that there's a key >> But it seems to me that there's a key >> But it seems to me that there's a key moment that had to happen in the moment that had to happen in the moment that had to happen in the progression of, you know, from progression of, you know, from progression of, you know, from calculators to the AI we have now to be calculators to the AI we have now to be calculators to the AI we have now to be able to see an image of a tail and make able to see an image of a tail and make able to see an image of a tail and make the reasonable assumption that it's most the reasonable assumption that it's most the reasonable assumption that it's most likely a cat if it's indoors or likely a cat if it's indoors or likely a cat if it's indoors or something like that because foxes something like that because foxes something like that because foxes generally aren't indoors. This sort of generally aren't indoors. This sort of generally aren't indoors. This sort of thing. So, at what point did machine thing. So, at what point did machine thing. So, at what point did machine learning and AI gain the ability to do learning and AI gain the ability to do learning and AI gain the ability to do kind of contextual learning and come up kind of contextual learning and come up kind of contextual learning and come up with the most likely assignment of what with the most likely assignment of what with the most likely assignment of what something is? Because it's one thing to something is? Because it's one thing to something is? Because it's one thing to show apples and bananas and oranges, show apples and bananas and oranges, show apples and bananas and oranges, they're all fruit. Okay, you could they're all fruit. Okay, you could they're all fruit. Okay, you could distinguish them. You could distinguish distinguish them. You could distinguish distinguish them. You could distinguish those from cars and trucks, etc. But those from cars and trucks, etc. But those from cars and trucks, etc. But this object constancy piece this object constancy piece this object constancy piece >> that if something is moving, you're only >> that if something is moving, you're only >> that if something is moving, you're only getting a partial image. This isn't what getting a partial image. This isn't what getting a partial image. This isn't what most people think of in terms of most people think of in terms of most people think of in terms of intelligence, but it's part of what intelligence, but it's part of what intelligence, but it's part of what makes our brains and the brains of other makes our brains and the brains of other makes our brains and the brains of other animals, but especially our brains so animals, but especially our brains so animals, but especially our brains so remarkable remarkable remarkable >> and why we consider ourselves >> and why we consider ourselves >> and why we consider ourselves >> probably the smartest species on earth >> probably the smartest species on earth >> probably the smartest species on earth and if not the smartest and certainly and if not the smartest and certainly and if not the smartest and certainly the best at technology development. the best at technology development. the best at technology development. >> Yeah. >> Yeah. >> Yeah. >> So when did AI achieve this and how was >> So when did AI achieve this and how was >> So when did AI achieve this and how was that scripted into these computers to that scripted into these computers to that scripted into these computers to allow them to do that? So let's just allow them to do that? So let's just allow them to do that? So let's just take the problem very you you have take the problem very you you have take the problem very you you have described it so well this problem of described it so well this problem of described it so well this problem of seeing a glimpse of a cat tail and being seeing a glimpse of a cat tail and being seeing a glimpse of a cat tail and being able to recognize cat right or or or a able to recognize cat right or or or a able to recognize cat right or or or a sign of high likelihood there is a cat. sign of high likelihood there is a cat. sign of high likelihood there is a cat. The interesting thing is Andrew The interesting thing is Andrew The interesting thing is Andrew generations of generations of generations of machine learning computer scientists machine learning computer scientists machine learning computer scientists have tried this problem. So before today have tried this problem. So before today have tried this problem. So before today that machines can reliably do it there that machines can reliably do it there that machines can reliably do it there were different algorithm you know you were different algorithm you know you were different algorithm you know you can imagine a common sense way of can imagine a common sense way of can imagine a common sense way of thinking about this is oh maybe we thinking about this is oh maybe we thinking about this is oh maybe we should recognize all the furniture to should recognize all the furniture to should recognize all the furniture to know it's a indoor so it's unlikely to know it's a indoor so it's unlikely to know it's a indoor so it's unlikely to be a fox. So though there are rules like be a fox. So though there are rules like be a fox. So though there are rules like that that it was built into uh previous that that it was built into uh previous that that it was built into uh previous generations of algorithms, there are generations of algorithms, there are generations of algorithms, there are also rules like well let's only instead also rules like well let's only instead also rules like well let's only instead of guess it's a cat, let's only guess of guess it's a cat, let's only guess of guess it's a cat, let's only guess one out of the 10 potential animals, you one out of the 10 potential animals, you one out of the 10 potential animals, you know, cat being one of them. That limits know, cat being one of them. That limits know, cat being one of them. That limits the the the the search or guess uh space the the the the search or guess uh space the the the the search or guess uh space and that would help. So many ideas were and that would help. So many ideas were and that would help. So many ideas were tried. tried. tried. So when was the moment it became much So when was the moment it became much So when was the moment it became much more reliable is this current era when more reliable is this current era when more reliable is this current era when the huge data that these algorithms have the huge data that these algorithms have the huge data that these algorithms have learned let's take Gemini or GPD uh have learned let's take Gemini or GPD uh have learned let's take Gemini or GPD uh have learned really created the capability in learned really created the capability in learned really created the capability in the machines uh uh learned space so much the machines uh uh learned space so much the machines uh uh learned space so much knowledge so much pattern that when knowledge so much pattern that when knowledge so much pattern that when presented with this more or less maybe a presented with this more or less maybe a presented with this more or less maybe a newish newish newish photo of a cat's tail sticking outside photo of a cat's tail sticking outside photo of a cat's tail sticking outside of a bookshelf. That pattern activated of a bookshelf. That pattern activated of a bookshelf. That pattern activated the learned what we call learned weights the learned what we call learned weights the learned what we call learned weights or learned parameters that put put the or learned parameters that put put the or learned parameters that put put the machine's um assessment or or guess of machine's um assessment or or guess of machine's um assessment or or guess of this this object closer to what it has this this object closer to what it has this this object closer to what it has seen which is likely to be a cattail or seen which is likely to be a cattail or seen which is likely to be a cattail or or just tail because there's just so or just tail because there's just so or just tail because there's just so much data. Got it. This is where Andrew much data. Got it. This is where Andrew much data. Got it. This is where Andrew as neuroscientists I think we depart as neuroscientists I think we depart as neuroscientists I think we depart from human brain because that child who from human brain because that child who from human brain because that child who learns about what you say kitty cat learns about what you say kitty cat learns about what you say kitty cat would not have the chance to download would not have the chance to download would not have the chance to download the internet of images of cat. They the internet of images of cat. They the internet of images of cat. They likely have seen three cats, 10 cats at likely have seen three cats, 10 cats at likely have seen three cats, 10 cats at most, most, most, but yet they're able to identify that but yet they're able to identify that but yet they're able to identify that tail as a cattail instead of a fox tail tail as a cattail instead of a fox tail tail as a cattail instead of a fox tail through a different kind of learning through a different kind of learning through a different kind of learning pathway. These are the mysteries we pathway. These are the mysteries we pathway. These are the mysteries we haven't fully solved. But I I do want to haven't fully solved. But I I do want to haven't fully solved. But I I do want to point out that departure between today's point out that departure between today's point out that departure between today's AI algorithm that is learned with the AI algorithm that is learned with the AI algorithm that is learned with the humongous amount of data versus how uh humongous amount of data versus how uh humongous amount of data versus how uh humans have evolved. If we continue to humans have evolved. If we continue to humans have evolved. If we continue to um ascend the kind of hierarchy from um ascend the kind of hierarchy from um ascend the kind of hierarchy from simple object recognition to what you simple object recognition to what you simple object recognition to what you and I would call higher order brain and I would call higher order brain and I would call higher order brain functions like moving more towards what functions like moving more towards what functions like moving more towards what most people they hear the word most people they hear the word most people they hear the word intelligence and they just think oh it intelligence and they just think oh it intelligence and they just think oh it must be some higher order thing must be some higher order thing must be some higher order thing creativity imagination. Let's go to um a creativity imagination. Let's go to um a creativity imagination. Let's go to um a middle step and then a and then a much middle step and then a and then a much middle step and then a and then a much further step out. So staying with the further step out. So staying with the further step out. So staying with the cat example, if a computer or a child cat example, if a computer or a child cat example, if a computer or a child learns to recognize a cat through the learns to recognize a cat through the learns to recognize a cat through the tail, the whole thing, whatever, and tail, the whole thing, whatever, and tail, the whole thing, whatever, and they've seen a cat move, it's a very new they've seen a cat move, it's a very new they've seen a cat move, it's a very new world at that point for that brain, that world at that point for that brain, that world at that point for that brain, that child or that computer child or that computer child or that computer >> because now >> because now >> because now they know that the cat generally moves they know that the cat generally moves they know that the cat generally moves in the direction of its head, not its in the direction of its head, not its in the direction of its head, not its tail. These are simple simple learning tail. These are simple simple learning tail. These are simple simple learning rules, right? It might go after mice, rules, right? It might go after mice, rules, right? It might go after mice, but it might run from dogs. Maybe yes, but it might run from dogs. Maybe yes, but it might run from dogs. Maybe yes, maybe no, and on and on. And so it seems maybe no, and on and on. And so it seems maybe no, and on and on. And so it seems that the next layer up in terms of quote that the next layer up in terms of quote that the next layer up in terms of quote unquote intelligence is to assign unquote intelligence is to assign unquote intelligence is to assign likelihoods of direction to move, likelihoods of direction to move, likelihoods of direction to move, directions not to move, other objects directions not to move, other objects directions not to move, other objects that that object is likely to interact that that object is likely to interact that that object is likely to interact with. This all sounds very basic to with. This all sounds very basic to with. This all sounds very basic to people, but like this is how brains people, but like this is how brains people, but like this is how brains learn and this is how machines learn. So learn and this is how machines learn. So learn and this is how machines learn. So when was the next sort of big inflection when was the next sort of big inflection when was the next sort of big inflection in terms of like giving a computer AI um in terms of like giving a computer AI um in terms of like giving a computer AI um a picture of a cat and saying um uh a picture of a cat and saying um uh a picture of a cat and saying um uh animate this cat for me, make it move animate this cat for me, make it move animate this cat for me, make it move like a cat without giving it any like a cat without giving it any like a cat without giving it any specific instructions about how to move specific instructions about how to move specific instructions about how to move its limbs etc. But I would imagine that its limbs etc. But I would imagine that its limbs etc. But I would imagine that was a pretty quick but a but a was a pretty quick but a but a was a pretty quick but a but a remarkably important transformation in remarkably important transformation in remarkably important transformation in this whole thing that we call AI because this whole thing that we call AI because this whole thing that we call AI because that's what a brain does. that's what a brain does. that's what a brain does. >> Yeah. So it's it's really funny you >> Yeah. So it's it's really funny you >> Yeah. So it's it's really funny you asked this and you put it beautifully. I asked this and you put it beautifully. I asked this and you put it beautifully. I never thought it to put it in this way never thought it to put it in this way never thought it to put it in this way for a uh public audience but that moment for a uh public audience but that moment for a uh public audience but that moment came when video become part of the came when video become part of the came when video become part of the training data. So see again I'm going training data. So see again I'm going training data. So see again I'm going back to the training data. So around back to the training data. So around back to the training data. So around 2023 very shortly after uh chatbt moment 2023 very shortly after uh chatbt moment 2023 very shortly after uh chatbt moment multiple research teams start to put multiple research teams start to put multiple research teams start to put video into the training data. Of course, video into the training data. Of course, video into the training data. Of course, I'm not going to get into the nuance I'm not going to get into the nuance I'm not going to get into the nuance stuff, the algorithm. There's a little stuff, the algorithm. There's a little stuff, the algorithm. There's a little bit of uh changes and variations. So, bit of uh changes and variations. So, bit of uh changes and variations. So, remember tw January 2024, Sora remember tw January 2024, Sora remember tw January 2024, Sora was released and that's where people see was released and that's where people see was released and that's where people see a video can be generated literally what a video can be generated literally what a video can be generated literally what you just said. People can then type and you just said. People can then type and you just said. People can then type and say say say a cat running towards a mouse and then a a cat running towards a mouse and then a a cat running towards a mouse and then a a a few second clip would be generated a a few second clip would be generated a a few second clip would be generated and there would be a cat moving its leg and there would be a cat moving its leg and there would be a cat moving its leg in a plausible way running towards the in a plausible way running towards the in a plausible way running towards the mouse. At that time there were still mouse. At that time there were still mouse. At that time there were still mistakes still to even today it's not mistakes still to even today it's not mistakes still to even today it's not perfect but things gotten have gotten a perfect but things gotten have gotten a perfect but things gotten have gotten a lot better but that opened the floodgate lot better but that opened the floodgate lot better but that opened the floodgate of video generation as you described it. of video generation as you described it. of video generation as you described it. So what happened there? What happened So what happened there? What happened So what happened there? What happened there is actually not as revolutionary there is actually not as revolutionary there is actually not as revolutionary as you might think because the bottom as you might think because the bottom as you might think because the bottom line is it's still data. As a scientist, line is it's still data. As a scientist, line is it's still data. As a scientist, I can tell you there are all kinds of I can tell you there are all kinds of I can tell you there are all kinds of algorithm tweaks and changes and algorithm tweaks and changes and algorithm tweaks and changes and improvements and and all that. But improvements and and all that. But improvements and and all that. But overall, if you zoom out, it's still overall, if you zoom out, it's still overall, if you zoom out, it's still part of this great neuronet network era, part of this great neuronet network era, part of this great neuronet network era, right? But what happened is that we're right? But what happened is that we're right? But what happened is that we're now able to um process video data in a now able to um process video data in a now able to um process video data in a way again some clever engineering way again some clever engineering way again some clever engineering tokenize it whatever you call it. And tokenize it whatever you call it. And tokenize it whatever you call it. And now we can generate these short clips of now we can generate these short clips of now we can generate these short clips of videos which is frames put together that videos which is frames put together that videos which is frames put together that look like plausible cat movement. Now look like plausible cat movement. Now look like plausible cat movement. Now you might ask does the algorithm know you might ask does the algorithm know you might ask does the algorithm know the muscle structure of a cat's legs so the muscle structure of a cat's legs so the muscle structure of a cat's legs so that when the algorithm shows that the that when the algorithm shows that the that when the algorithm shows that the cat is mo moving in a plausible way with cat is mo moving in a plausible way with cat is mo moving in a plausible way with the paws you know in a sequence I would the paws you know in a sequence I would the paws you know in a sequence I would say the algorithm doesn't but what it say the algorithm doesn't but what it say the algorithm doesn't but what it does have is so many video especially does have is so many video especially does have is so many video especially cat on the internet so many videos of cat on the internet so many videos of cat on the internet so many videos of cat so it learned what it should look cat so it learned what it should look cat so it learned what it should look like so in a way humans do that most of like so in a way humans do that most of like so in a way humans do that most of us without education would not know the us without education would not know the us without education would not know the how muscles move in cats I still don't how muscles move in cats I still don't how muscles move in cats I still don't know you know our colleagues in medical know you know our colleagues in medical know you know our colleagues in medical school might know but we have just got school might know but we have just got school might know but we have just got so used to seeing cats moving this ways so used to seeing cats moving this ways so used to seeing cats moving this ways that we have a plausible idea of how that we have a plausible idea of how that we have a plausible idea of how cats move so that is similar that's how cats move so that is similar that's how cats move so that is similar that's how similar AI is the it's the statistics similar AI is the it's the statistics similar AI is the it's the statistics It's the large amount of data that It's the large amount of data that It's the large amount of data that showed you what is the plausible um showed you what is the plausible um showed you what is the plausible um generation of cat movements. generation of cat movements. generation of cat movements. >> Yeah. So when people have heard almost >> Yeah. So when people have heard almost >> Yeah. So when people have heard almost certainly that the brain is a prediction certainly that the brain is a prediction certainly that the brain is a prediction machine, it's a learning machine, this machine, it's a learning machine, this machine, it's a learning machine, this is exactly what Yes. you're referring is exactly what Yes. you're referring is exactly what Yes. you're referring to. to. to. >> Yeah. >> Yeah. >> Yeah. >> Let's go to a really far out there >> Let's go to a really far out there >> Let's go to a really far out there aspect of brain function that we know aspect of brain function that we know aspect of brain function that we know exists in humans, which is exists in humans, which is exists in humans, which is >> thoughts >> thoughts >> thoughts >> and creativity. M now there are probably >> and creativity. M now there are probably >> and creativity. M now there are probably rules for thoughts and creativity. rules for thoughts and creativity. rules for thoughts and creativity. They're a little bit harder to tack down They're a little bit harder to tack down They're a little bit harder to tack down than um examples from the visual system. than um examples from the visual system. than um examples from the visual system. Like if it's a tail and it's indoors is Like if it's a tail and it's indoors is Like if it's a tail and it's indoors is likely a cat. This kind of thing, but likely a cat. This kind of thing, but likely a cat. This kind of thing, but they're there. The rules are there. they're there. The rules are there. they're there. The rules are there. >> If you use apple as an example, >> If you use apple as an example, >> If you use apple as an example, >> we could have gone from lowle seeing an >> we could have gone from lowle seeing an >> we could have gone from lowle seeing an apple apple apple >> to midle seeing apple always drop, not >> to midle seeing apple always drop, not >> to midle seeing apple always drop, not fly off. at highest level what is the fly off. at highest level what is the fly off. at highest level what is the equation that governs the Apple's equation that governs the Apple's equation that governs the Apple's movement movement movement >> right so that's ascending to like a >> right so that's ascending to like a >> right so that's ascending to like a higher order more reductionist analysis higher order more reductionist analysis higher order more reductionist analysis >> what do you think about the idea that >> what do you think about the idea that >> what do you think about the idea that while AI is indeed intelligent it can do while AI is indeed intelligent it can do while AI is indeed intelligent it can do things that brains can do maybe even things that brains can do maybe even things that brains can do maybe even >> well certainly things that individual >> well certainly things that individual >> well certainly things that individual human brains can't do we know this by human brains can't do we know this by human brains can't do we know this by virtue of beating humans at chess and virtue of beating humans at chess and virtue of beating humans at chess and this sort of thing this sort of thing this sort of thing the idea right now as I understand it is the idea right now as I understand it is the idea right now as I understand it is that AI is trained on the internet, that AI is trained on the internet, that AI is trained on the internet, >> images, discussions, videos, songs, but >> images, discussions, videos, songs, but >> images, discussions, videos, songs, but that's not all of human cognition, that's not all of human cognition, that's not all of human cognition, right? So, are there aspects of AI that right? So, are there aspects of AI that right? So, are there aspects of AI that are whether or not it's chat or it's are whether or not it's chat or it's are whether or not it's chat or it's claude or even the most powerful not yet claude or even the most powerful not yet claude or even the most powerful not yet released machine learning and and AI released machine learning and and AI released machine learning and and AI tools that don't have access to features tools that don't have access to features tools that don't have access to features of human brain function yet because of human brain function yet because of human brain function yet because they've never been uploaded to the they've never been uploaded to the they've never been uploaded to the internet, at least not in a way that the internet, at least not in a way that the internet, at least not in a way that the AI can pull out. So, for instance, you AI can pull out. So, for instance, you AI can pull out. So, for instance, you know, you could put a symphony there and know, you could put a symphony there and know, you could put a symphony there and it follows certain rules of music and it follows certain rules of music and it follows certain rules of music and mathematics and sound like that that mathematics and sound like that that mathematics and sound like that that makes sense, but you have thoughts all makes sense, but you have thoughts all makes sense, but you have thoughts all day long and I have thoughts all day day long and I have thoughts all day day long and I have thoughts all day long that don't quite mesh with language long that don't quite mesh with language long that don't quite mesh with language in a way that I can just type them out in a way that I can just type them out in a way that I can just type them out on the internet. Stay with me here. I on the internet. Stay with me here. I on the internet. Stay with me here. I know this is a long question, but I feel know this is a long question, but I feel know this is a long question, but I feel like this is the one thing you are like this is the one thing you are like this is the one thing you are perfectly poised to answer, and I've perfectly poised to answer, and I've perfectly poised to answer, and I've been waiting to ask you this for a year been waiting to ask you this for a year been waiting to ask you this for a year and a half since I saw you in Utah. In and a half since I saw you in Utah. In and a half since I saw you in Utah. In the world of art, we have this thing the world of art, we have this thing the world of art, we have this thing called abstraction, right? And called abstraction, right? And called abstraction, right? And occasionally somebody will come up with occasionally somebody will come up with occasionally somebody will come up with a painting or a drawing that it doesn't a painting or a drawing that it doesn't a painting or a drawing that it doesn't look like anything specific. This look like anything specific. This look like anything specific. This happens in music, too, where you just happens in music, too, where you just happens in music, too, where you just feel something like there's like a feel something like there's like a feel something like there's like a fundamental rule or an emotion fundamental rule or an emotion fundamental rule or an emotion associated with it. Like they've tapped associated with it. Like they've tapped associated with it. Like they've tapped into some aspect of brain function, but into some aspect of brain function, but into some aspect of brain function, but you can't say what it is. you can't say what it is. you can't say what it is. I feel like this is the sort of thing I feel like this is the sort of thing I feel like this is the sort of thing that is complicated for AI or for me to that is complicated for AI or for me to that is complicated for AI or for me to understand how AI could do because you understand how AI could do because you understand how AI could do because you can put that piece of art into AI and can put that piece of art into AI and can put that piece of art into AI and say, you know, what fundamental feature say, you know, what fundamental feature say, you know, what fundamental feature of human uh experience does this reveal of human uh experience does this reveal of human uh experience does this reveal and it only has access to what's on the and it only has access to what's on the and it only has access to what's on the internet. So, h how can you capture a a internet. So, h how can you capture a a internet. So, h how can you capture a a complex constellation of feelings and complex constellation of feelings and complex constellation of feelings and experience with AI? That seems to be the experience with AI? That seems to be the experience with AI? That seems to be the gap for me. And I'm sure we'll get there gap for me. And I'm sure we'll get there gap for me. And I'm sure we'll get there with AI, but I'm not seeing from with AI, but I'm not seeing from with AI, but I'm not seeing from neuroscience to AI in any kind of direct neuroscience to AI in any kind of direct neuroscience to AI in any kind of direct way. The same way we could ratchet way. The same way we could ratchet way. The same way we could ratchet through visual motion, sadness, through visual motion, sadness, through visual motion, sadness, happiness. You could pull out a lot of happiness. You could pull out a lot of happiness. You could pull out a lot of things, but it's hard to get to these things, but it's hard to get to these things, but it's hard to get to these higher order abstract representations higher order abstract representations higher order abstract representations that can't be spoken or written down or that can't be spoken or written down or that can't be spoken or written down or drawn. If I just say, give me your drawn. If I just say, give me your drawn. If I just say, give me your example of whatever nostalgia for your example of whatever nostalgia for your example of whatever nostalgia for your childhood home. You could write about childhood home. You could write about childhood home. You could write about it, but those are just words. It's not I it, but those are just words. It's not I it, but those are just words. It's not I can't understand your experience at a can't understand your experience at a can't understand your experience at a first person level. first person level. first person level. >> Totally. Andrew, I I know you put a lot >> Totally. Andrew, I I know you put a lot >> Totally. Andrew, I I know you put a lot of thoughts into this question and I of thoughts into this question and I of thoughts into this question and I think it's a very important question and think it's a very important question and think it's a very important question and let's let's peel this one step at a let's let's peel this one step at a let's let's peel this one step at a time. First of all, TLDDR short answer time. First of all, TLDDR short answer time. First of all, TLDDR short answer is I agree with you that we do have to is I agree with you that we do have to is I agree with you that we do have to be very careful recognizing what AI can be very careful recognizing what AI can be very careful recognizing what AI can do, is likely to do, not conjecturing do, is likely to do, not conjecturing do, is likely to do, not conjecturing over a 100 years or or whatever. I over a 100 years or or whatever. I over a 100 years or or whatever. I recognize what you just said are these recognize what you just said are these recognize what you just said are these extremely nuanced personalized extremely nuanced personalized extremely nuanced personalized hard to characterize or not even hard to characterize or not even hard to characterize or not even captured human cognitive behaviors and captured human cognitive behaviors and captured human cognitive behaviors and because they were not captured they then because they were not captured they then because they were not captured they then they were not uploaded on the internet they were not uploaded on the internet they were not uploaded on the internet and we don't have today's AI doesn't and we don't have today's AI doesn't and we don't have today's AI doesn't have a way to do that. So when you call have a way to do that. So when you call have a way to do that. So when you call internet internet internet which is the source of AI's data, let's which is the source of AI's data, let's which is the source of AI's data, let's be very clear what is internet. be very clear what is internet. be very clear what is internet. Internet is not some random thing. Internet is not some random thing. Internet is not some random thing. Internet is the biggest collection of Internet is the biggest collection of Internet is the biggest collection of human behavior human behavior human behavior in multimodal forms. Let's break it down in multimodal forms. Let's break it down in multimodal forms. Let's break it down further. Internet has the world's further. Internet has the world's further. Internet has the world's population typing on it for many many at population typing on it for many many at population typing on it for many many at this point multiple decades. That typing this point multiple decades. That typing this point multiple decades. That typing is a sensing mechanism that captured is a sensing mechanism that captured is a sensing mechanism that captured everything from teenager chit chitchat everything from teenager chit chitchat everything from teenager chit chitchat all the way to deep scientific articles all the way to deep scientific articles all the way to deep scientific articles who dig got digitized and get uploaded. who dig got digitized and get uploaded. who dig got digitized and get uploaded. Right? So that capturing human language Right? So that capturing human language Right? So that capturing human language is what internet is super good at. is what internet is super good at. is what internet is super good at. Then internet captures Then internet captures Then internet captures images. How? Because we now have digital images. How? Because we now have digital images. How? Because we now have digital cameras. That's so prevalent in cameras. That's so prevalent in cameras. That's so prevalent in smartphones and digital cameras. So that smartphones and digital cameras. So that smartphones and digital cameras. So that humans love taking photos from, you humans love taking photos from, you humans love taking photos from, you know, the cat in your house to selfies know, the cat in your house to selfies know, the cat in your house to selfies to beautiful, you know, BBC captured to beautiful, you know, BBC captured to beautiful, you know, BBC captured photos. Those also got uploaded in our photos. Those also got uploaded in our photos. Those also got uploaded in our digital sphere. On top of that, there's digital sphere. On top of that, there's digital sphere. On top of that, there's videos. Videos now has sound, has videos. Videos now has sound, has videos. Videos now has sound, has movements that also got uploaded to our movements that also got uploaded to our movements that also got uploaded to our digital sphere. On top of that, there's digital sphere. On top of that, there's digital sphere. On top of that, there's music. We're not even getting into the music. We're not even getting into the music. We're not even getting into the legal discussion of copyrights, but legal discussion of copyrights, but legal discussion of copyrights, but let's just table that aside. I'm just let's just table that aside. I'm just let's just table that aside. I'm just talking about the forms of data. The talking about the forms of data. The talking about the forms of data. The speeches and and singing and music and speeches and and singing and music and speeches and and singing and music and orchestra that also got uploaded into orchestra that also got uploaded into orchestra that also got uploaded into the digital sphere. So now we have the digital sphere. So now we have the digital sphere. So now we have created this humongous library of human created this humongous library of human created this humongous library of human knowledge in words, human behavior in knowledge in words, human behavior in knowledge in words, human behavior in videos, human expressions or even videos, human expressions or even videos, human expressions or even nature's whatever in in sound and now AI nature's whatever in in sound and now AI nature's whatever in in sound and now AI gets trained on that. That is why it's gets trained on that. That is why it's gets trained on that. That is why it's so powerful. This is why especially in so powerful. This is why especially in so powerful. This is why especially in the words front that AI can recognize the words front that AI can recognize the words front that AI can recognize patterns can can synthesize patterns patterns can can synthesize patterns patterns can can synthesize patterns because so much of this is already because so much of this is already because so much of this is already there. But the thing that you just there. But the thing that you just there. But the thing that you just talked about that when let's say Picasso talked about that when let's say Picasso talked about that when let's say Picasso had that incredibly profound thought had that incredibly profound thought had that incredibly profound thought about that particular way of expressing about that particular way of expressing about that particular way of expressing that that portrait of the of the young that that portrait of the of the young that that portrait of the of the young woman that thought has never been woman that thought has never been woman that thought has never been captured. captured. captured. In fact, as neuroscientists, if I ask In fact, as neuroscientists, if I ask In fact, as neuroscientists, if I ask you which brain area did that thought you which brain area did that thought you which brain area did that thought come from, you don't know, right? Is it come from, you don't know, right? Is it come from, you don't know, right? Is it Broa? Is it V1? Is it motor? Is it Broa? Is it V1? Is it motor? Is it Broa? Is it V1? Is it motor? Is it preffrontal? We don't know. Maybe it's preffrontal? We don't know. Maybe it's preffrontal? We don't know. Maybe it's diffused everywhere because that thought diffused everywhere because that thought diffused everywhere because that thought is so personalized, so special. You can is so personalized, so special. You can is so personalized, so special. You can call it creativity, you can call it call it creativity, you can call it call it creativity, you can call it emotion, you can call it whatever you emotion, you can call it whatever you emotion, you can call it whatever you want. You can call it cat 231 whatever want. You can call it cat 231 whatever want. You can call it cat 231 whatever name you can give it that thought is not name you can give it that thought is not name you can give it that thought is not captured therefore it's not on the captured therefore it's not on the captured therefore it's not on the internet therefore AI has not seen it internet therefore AI has not seen it internet therefore AI has not seen it so that is where humans still remain so so that is where humans still remain so so that is where humans still remain so unique but we also need to give credit unique but we also need to give credit unique but we also need to give credit to AI because AI has learned so many to AI because AI has learned so many to AI because AI has learned so many things it can combine information in things it can combine information in things it can combine information in highly creative way did you Remember highly creative way did you Remember highly creative way did you Remember move 37? move 37? move 37? >> This is Alph Go. Right. >> This is Alph Go. Right. >> This is Alph Go. Right. >> Right. Move 37 has symbolized AI's >> Right. Move 37 has symbolized AI's >> Right. Move 37 has symbolized AI's creativity. I think it's both true but creativity. I think it's both true but creativity. I think it's both true but can be taken out of context because that can be taken out of context because that can be taken out of context because that was a game when Alph Go was plain Lisa was a game when Alph Go was plain Lisa was a game when Alph Go was plain Lisa doll and in I think it's a third game doll and in I think it's a third game doll and in I think it's a third game out of the five games that Alph Go as a out of the five games that Alph Go as a out of the five games that Alph Go as a computer algorithm made a move that the computer algorithm made a move that the computer algorithm made a move that the human masters of Go never thought about human masters of Go never thought about human masters of Go never thought about and that is an incredible move right and that is an incredible move right and that is an incredible move right because it really humans collect because it really humans collect because it really humans collect collectively these are the masters never collectively these are the masters never collectively these are the masters never thought about it. But if you really go thought about it. But if you really go thought about it. But if you really go deep into what AI did there [snorts] it deep into what AI did there [snorts] it deep into what AI did there [snorts] it was because first of all go is a highly was because first of all go is a highly was because first of all go is a highly mathematical game. It it has very clear mathematical game. It it has very clear mathematical game. It it has very clear mathematical objective very clear mathematical objective very clear mathematical objective very clear mathematical rules in terms of move. So mathematical rules in terms of move. So mathematical rules in terms of move. So when AI having a bigger compute and um when AI having a bigger compute and um when AI having a bigger compute and um ways to retain how many moves it can uh ways to retain how many moves it can uh ways to retain how many moves it can uh it can remember it was able to do things it can remember it was able to do things it can remember it was able to do things that human brains don't typically do. So that human brains don't typically do. So that human brains don't typically do. So is that called creativity? I think it is is that called creativity? I think it is is that called creativity? I think it is but we do have to recognize that's a but we do have to recognize that's a but we do have to recognize that's a special kind of creativity. I was special kind of creativity. I was special kind of creativity. I was talking to a incredible mathematician of talking to a incredible mathematician of talking to a incredible mathematician of our time and I was asking him about the our time and I was asking him about the our time and I was asking him about the unsolved problem of mathematics and how unsolved problem of mathematics and how unsolved problem of mathematics and how AI can contribute to that and he was AI can contribute to that and he was AI can contribute to that and he was very positive. He said there are many very positive. He said there are many very positive. He said there are many problems in today's mathematics. problems in today's mathematics. problems in today's mathematics. As hard as they are, even as say a field As hard as they are, even as say a field As hard as they are, even as say a field mentalist, I probably have forgotten mentalist, I probably have forgotten mentalist, I probably have forgotten there are known methods in math that can there are known methods in math that can there are known methods in math that can solve these problem because I have a solve these problem because I have a solve these problem because I have a human brain. I don't remember I I don't human brain. I don't remember I I don't human brain. I don't remember I I don't know all of math's, you know, solutions know all of math's, you know, solutions know all of math's, you know, solutions in the past hundreds of years. even if I in the past hundreds of years. even if I in the past hundreds of years. even if I were a a field medalist. So AI can help were a a field medalist. So AI can help were a a field medalist. So AI can help us to solve these problems. But as a us to solve these problems. But as a us to solve these problems. But as a mathematician, he was also telling me he mathematician, he was also telling me he mathematician, he was also telling me he said, I don't know if AI can solve all said, I don't know if AI can solve all said, I don't know if AI can solve all of math problems because some of these of math problems because some of these of math problems because some of these math problems require solutions that math problems require solutions that math problems require solutions that have not been invented have not been invented have not been invented that will push creativity to a whole that will push creativity to a whole that will push creativity to a whole different level. And this is where you different level. And this is where you different level. And this is where you you know I'm we should be curious is it you know I'm we should be curious is it you know I'm we should be curious is it going to be a human creativity or AI going to be a human creativity or AI going to be a human creativity or AI would go through its iterations of uh of would go through its iterations of uh of would go through its iterations of uh of improvement and get to a point of improvement and get to a point of improvement and get to a point of creativity that humans don't have or is creativity that humans don't have or is creativity that humans don't have or is it a combined creativity. My current it a combined creativity. My current it a combined creativity. My current conjecture is hybrid is that humans conjecture is hybrid is that humans conjecture is hybrid is that humans working alongside AI would help us to working alongside AI would help us to working alongside AI would help us to solve these problems whose solutions solve these problems whose solutions solve these problems whose solutions have yet to be invented. And then what have yet to be invented. And then what have yet to be invented. And then what you said especially you touched on you said especially you touched on you said especially you touched on emotion is even more personalized. This emotion is even more personalized. This emotion is even more personalized. This is not necessarily logic. This is not is not necessarily logic. This is not is not necessarily logic. This is not necessarily deduct deductive reasoning. necessarily deduct deductive reasoning. necessarily deduct deductive reasoning. This is maybe Andrew you look at this This is maybe Andrew you look at this This is maybe Andrew you look at this cup and say it's a great cup. What if it cup and say it's a great cup. What if it cup and say it's a great cup. What if it evoked an emotion in me, a childhood evoked an emotion in me, a childhood evoked an emotion in me, a childhood moment that a gray cup might mean moment that a gray cup might mean moment that a gray cup might mean something that only me and my best something that only me and my best something that only me and my best friend share? That is a completely friend share? That is a completely friend share? That is a completely inaccessible inaccessible inaccessible piece of information in my brain that is piece of information in my brain that is piece of information in my brain that is never uploaded on the internet and no never uploaded on the internet and no never uploaded on the internet and no matter how mighty AI is today cannot matter how mighty AI is today cannot matter how mighty AI is today cannot access that. So that my reaction to this access that. So that my reaction to this access that. So that my reaction to this cup and potentially what I would do with cup and potentially what I would do with cup and potentially what I would do with it because of that piece of memory can it because of that piece of memory can it because of that piece of memory can be completely different. You can call it be completely different. You can call it be completely different. You can call it creativity. You can call it expression. creativity. You can call it expression. creativity. You can call it expression. You can call it storytelling. You can You can call it storytelling. You can You can call it storytelling. You can call it in many ways. But that's where call it in many ways. But that's where call it in many ways. But that's where it AI cannot access. it AI cannot access. it AI cannot access. >> I feel like at some point in the not too >> I feel like at some point in the not too >> I feel like at some point in the not too distant future uh computers will have distant future uh computers will have distant future uh computers will have access to our brain activity in access to our brain activity in access to our brain activity in non-invasive ways. M non-invasive ways. M non-invasive ways. M >> so you know like I might even imagine in >> so you know like I might even imagine in >> so you know like I might even imagine in 5 10 years I'm wearing something on my 5 10 years I'm wearing something on my 5 10 years I'm wearing something on my head right now you can't see it head right now you can't see it head right now you can't see it >> it's a very very fine hairet makes it >> it's a very very fine hairet makes it >> it's a very very fine hairet makes it sound like it whatever like some sound like it whatever like some sound like it whatever like some electrodes that are just there on the electrodes that are just there on the electrodes that are just there on the outside of my skull not bothering me outside of my skull not bothering me outside of my skull not bothering me sensing my activity inside the brain sensing my activity inside the brain sensing my activity inside the brain maybe also sensing my heart rate maybe also sensing my heart rate maybe also sensing my heart rate autonomic activity how alert I am and autonomic activity how alert I am and autonomic activity how alert I am and comparing that yes to what I'm saying comparing that yes to what I'm saying comparing that yes to what I'm saying and what I'm doing this is all totally and what I'm doing this is all totally and what I'm doing this is all totally within reach and it's going to happen within reach and it's going to happen within reach and it's going to happen you and I both know this and it's you and I both know this and it's you and I both know this and it's probably already starting to scare probably already starting to scare probably already starting to scare people, but let's let's let's keep it people, but let's let's let's keep it people, but let's let's let's keep it benevolent, right? There's this world benevolent, right? There's this world benevolent, right? There's this world where a computer that I own and I'm not where a computer that I own and I'm not where a computer that I own and I'm not worried about data getting out or worried about data getting out or worried about data getting out or anything like that. We've can manage anything like that. We've can manage anything like that. We've can manage that problem is sensing all these that problem is sensing all these that problem is sensing all these aspects of me and is picking up on the aspects of me and is picking up on the aspects of me and is picking up on the fact that yes, what I say might be fact that yes, what I say might be fact that yes, what I say might be important, but there are aspects of my important, but there are aspects of my important, but there are aspects of my internal state and brain activity that internal state and brain activity that internal state and brain activity that I'm not even aware of. I'm not even aware of. I'm not even aware of. >> Yeah. and I can decide to collaborate >> Yeah. and I can decide to collaborate >> Yeah. and I can decide to collaborate with this aspect of me and say, let's with this aspect of me and say, let's with this aspect of me and say, let's let's come up with a really interesting let's come up with a really interesting let's come up with a really interesting uh picture that I've never seen before, uh picture that I've never seen before, uh picture that I've never seen before, but comes from some experience of mine but comes from some experience of mine but comes from some experience of mine that's important based on whatever like that's important based on whatever like that's important based on whatever like and and it could reveal that to me and and it could reveal that to me and and it could reveal that to me because it has access to my because it has access to my because it has access to my >> to unconscious features of my brain >> to unconscious features of my brain >> to unconscious features of my brain activity. I think this is very likely to activity. I think this is very likely to activity. I think this is very likely to happen in in the not too distant future. happen in in the not too distant future. happen in in the not too distant future. And perhaps if people thought about it And perhaps if people thought about it And perhaps if people thought about it within the bubble of their own within the bubble of their own within the bubble of their own experience, like this isn't immediately experience, like this isn't immediately experience, like this isn't immediately going to the internet or it's not going going to the internet or it's not going going to the internet or it's not going to be used against them, you're actually to be used against them, you're actually to be used against them, you're actually learning about yourself, learning about yourself, learning about yourself, >> of course, >> of course, >> of course, >> and and I feel most people have an >> and and I feel most people have an >> and and I feel most people have an inherent interest in what's going on for inherent interest in what's going on for inherent interest in what's going on for them also with other people, thank them also with other people, thank them also with other people, thank goodness. But goodness. But goodness. But >> they're I think like amazing. Like I >> they're I think like amazing. Like I >> they're I think like amazing. Like I would love to know why would love to know why would love to know why >> I trip up in certain ways and don't have >> I trip up in certain ways and don't have >> I trip up in certain ways and don't have the best day or why some days I have the the best day or why some days I have the the best day or why some days I have the best day or where ideas come from in me. best day or where ideas come from in me. best day or where ideas come from in me. What states I could, you know, kind of What states I could, you know, kind of What states I could, you know, kind of elaborate on, but I'm not going to know elaborate on, but I'm not going to know elaborate on, but I'm not going to know how to do that except okay, one cup of how to do that except okay, one cup of how to do that except okay, one cup of coffee good, one and a half a little coffee good, one and a half a little coffee good, one and a half a little better, two is too much. If I like right better, two is too much. If I like right better, two is too much. If I like right now, if you think about how primitively now, if you think about how primitively now, if you think about how primitively we go about this, it's kind of crazy. we go about this, it's kind of crazy. we go about this, it's kind of crazy. It's crazy. And everyone has a different It's crazy. And everyone has a different It's crazy. And everyone has a different method and we all try and get this right method and we all try and get this right method and we all try and get this right and then you've aged enough by the time and then you've aged enough by the time and then you've aged enough by the time you get it right that then you have to you get it right that then you have to you get it right that then you have to update it. And like we're probably not update it. And like we're probably not update it. And like we're probably not getting the most out of our biology and getting the most out of our biology and getting the most out of our biology and our brains at all right now. our brains at all right now. our brains at all right now. >> No, we're not. And this is why I keep >> No, we're not. And this is why I keep >> No, we're not. And this is why I keep saying this is why it bothers me when saying this is why it bothers me when saying this is why it bothers me when people talk about AI. Some people make people talk about AI. Some people make people talk about AI. Some people make it sound like it's replacing humanity. it sound like it's replacing humanity. it sound like it's replacing humanity. But what we really what you describe is But what we really what you describe is But what we really what you describe is about enhancing and augmenting humanity. about enhancing and augmenting humanity. about enhancing and augmenting humanity. Right. This is where it doesn't even Right. This is where it doesn't even Right. This is where it doesn't even have to go as sci-fi as a smart hairet have to go as sci-fi as a smart hairet have to go as sci-fi as a smart hairet uh accessing your brain waves. Just AI uh accessing your brain waves. Just AI uh accessing your brain waves. Just AI learning your patterns of writing learning your patterns of writing learning your patterns of writing >> can already help you to be you know a >> can already help you to be you know a >> can already help you to be you know a better communicator, a more effective better communicator, a more effective better communicator, a more effective communicator, a more efficient communicator, a more efficient communicator, a more efficient communicator and that is an empowering communicator and that is an empowering communicator and that is an empowering capability that we could unleash in capability that we could unleash in capability that we could unleash in today's AI. I think one of the most today's AI. I think one of the most today's AI. I think one of the most important thing Andrew that as a important thing Andrew that as a important thing Andrew that as a neuroscientist neuroscientist neuroscientist and also faculty we know is agency is so and also faculty we know is agency is so and also faculty we know is agency is so important for humanity. You know that important for humanity. You know that important for humanity. You know that boils down to motivation, agency and boils down to motivation, agency and boils down to motivation, agency and dignity at every individual level. And I dignity at every individual level. And I dignity at every individual level. And I think we need to recognize that we need think we need to recognize that we need think we need to recognize that we need to think about AI as a tool that helps to think about AI as a tool that helps to think about AI as a tool that helps us in our agency. It does it should not us in our agency. It does it should not us in our agency. It does it should not take away our agency and people who lead take away our agency and people who lead take away our agency and people who lead in today's AI should not try to talk in today's AI should not try to talk in today's AI should not try to talk like that this this work will take away like that this this work will take away like that this this work will take away agency from people. agency from people. agency from people. >> Yeah. I think people who are very >> Yeah. I think people who are very >> Yeah. I think people who are very familiar with the technology whether familiar with the technology whether familiar with the technology whether it's computers or it's biology or any it's computers or it's biology or any it's computers or it's biology or any technology cars for that matter technology cars for that matter technology cars for that matter we they become such nerds of that thing we they become such nerds of that thing we they become such nerds of that thing that we forget that that we forget that that we forget that >> it can be scary to people >> it can be scary to people >> it can be scary to people >> and that the languaging around it is >> and that the languaging around it is >> and that the languaging around it is essential it is. essential it is. essential it is. >> And I remember a time in the early 90s >> And I remember a time in the early 90s >> And I remember a time in the early 90s I'm sure you remember this too when I'm sure you remember this too when I'm sure you remember this too when genetic testing was viewed as this thing genetic testing was viewed as this thing genetic testing was viewed as this thing like would you want to have it? Would like would you want to have it? Would like would you want to have it? Would you want to do a blood test? Because oh you want to do a blood test? Because oh you want to do a blood test? Because oh my goodness, you might see something my goodness, you might see something my goodness, you might see something that could really scare you. And that that could really scare you. And that that could really scare you. And that discussion is happening now around, you discussion is happening now around, you discussion is happening now around, you know, self-elected MRIs and things like know, self-elected MRIs and things like know, self-elected MRIs and things like that. None of which people have to do. that. None of which people have to do. that. None of which people have to do. >> But I come from the stance like more >> But I come from the stance like more >> But I come from the stance like more information is better. But I've come to information is better. But I've come to information is better. But I've come to understand that not everyone feels that understand that not everyone feels that understand that not everyone feels that way. Some people don't want to know. way. Some people don't want to know. way. Some people don't want to know. They don't want to know. They don't want to know. They don't want to know. >> Yeah. But they should have the choice. >> Yeah. But they should have the choice. >> Yeah. But they should have the choice. In the meantime, we should have enough In the meantime, we should have enough In the meantime, we should have enough public education and communication to public education and communication to public education and communication to let people know the pros and cons, but let people know the pros and cons, but let people know the pros and cons, but not to deny them the choice and also not not to deny them the choice and also not not to deny them the choice and also not to take away, you know, um, and and and to take away, you know, um, and and and to take away, you know, um, and and and [clears throat] [clears throat] [clears throat] say, well, since you don't understand say, well, since you don't understand say, well, since you don't understand this, let me decide for you what's good. this, let me decide for you what's good. this, let me decide for you what's good. That is not good, you know, and and the That is not good, you know, and and the That is not good, you know, and and the rhetoric around AI right now is getting rhetoric around AI right now is getting rhetoric around AI right now is getting really skewed because people who know really skewed because people who know really skewed because people who know what this is tend to talk down at the what this is tend to talk down at the what this is tend to talk down at the public. It tend to talk whether the public. It tend to talk whether the public. It tend to talk whether the motivation is a positive one or negative motivation is a positive one or negative motivation is a positive one or negative one. it there there's a rhetoric of you one. it there there's a rhetoric of you one. it there there's a rhetoric of you guys don't know what this is and I will guys don't know what this is and I will guys don't know what this is and I will tell you allow will make you whether tell you allow will make you whether tell you allow will make you whether happy safe whatever it is and I will happy safe whatever it is and I will happy safe whatever it is and I will decide for you these are not healthy and decide for you these are not healthy and decide for you these are not healthy and not helpful. Yeah, I agree. And I think, not helpful. Yeah, I agree. And I think, not helpful. Yeah, I agree. And I think, you know, one of the reasons for you know, one of the reasons for you know, one of the reasons for starting this podcast was to showcase starting this podcast was to showcase starting this podcast was to showcase the scientists and physicians who really the scientists and physicians who really the scientists and physicians who really have a benevolence about them and they have a benevolence about them and they have a benevolence about them and they have no interest in dumbing things down, have no interest in dumbing things down, have no interest in dumbing things down, but they do have an interest in people but they do have an interest in people but they do have an interest in people understanding things and many people understanding things and many people understanding things and many people would feel that, you know, health would feel that, you know, health would feel that, you know, health information is among the more important information is among the more important information is among the more important things to understand. Absolutely. things to understand. Absolutely. things to understand. Absolutely. >> Well, thankfully you're um you're >> Well, thankfully you're um you're >> Well, thankfully you're um you're breaking the mold of of the, you know, breaking the mold of of the, you know, breaking the mold of of the, you know, the phenotype you just described. um and the phenotype you just described. um and the phenotype you just described. um and and there are a few others but you've and there are a few others but you've and there are a few others but you've really uh you've been doing this at at really uh you've been doing this at at really uh you've been doing this at at the highest levels really encouraging the highest levels really encouraging the highest levels really encouraging people to think about the collaboration people to think about the collaboration people to think about the collaboration that is AI the the agency that exists that is AI the the agency that exists that is AI the the agency that exists and whether to use it or not to use it and whether to use it or not to use it and whether to use it or not to use it and so forth and so forth and so forth >> one of the agency I do think it's >> one of the agency I do think it's >> one of the agency I do think it's important for individual humans whether important for individual humans whether important for individual humans whether you're a student a teacher doctor a you're a student a teacher doctor a you're a student a teacher doctor a policy maker is learn about this not policy maker is learn about this not policy maker is learn about this not necessarily learn about how to code I necessarily learn about how to code I necessarily learn about how to code I don't think that's it's necessary depend don't think that's it's necessary depend don't think that's it's necessary depend on your job right So for example, if on your job right So for example, if on your job right So for example, if you're artist or if you're a teacher or you're artist or if you're a teacher or you're artist or if you're a teacher or doctor, you don't necessarily need to doctor, you don't necessarily need to doctor, you don't necessarily need to code, but learn about what this l uh code, but learn about what this l uh code, but learn about what this l uh this technology is, learn about how you this technology is, learn about how you this technology is, learn about how you can use it yourself to empower yourself, can use it yourself to empower yourself, can use it yourself to empower yourself, your [snorts] learning or your work or your [snorts] learning or your work or your [snorts] learning or your work or your expression. By learning, your expression. By learning, your expression. By learning, one feels more in control. By learning, one feels more in control. By learning, one feels more in control. By learning, you're less scared of trying. And by you're less scared of trying. And by you're less scared of trying. And by learning, you retain that agency. and learning, you retain that agency. and learning, you retain that agency. and that dignity because at the end of the that dignity because at the end of the that dignity because at the end of the day, no matter how advanced technology day, no matter how advanced technology day, no matter how advanced technology is or medicine is, as humans, we want is or medicine is, as humans, we want is or medicine is, as humans, we want that benevolence that helps us to live that benevolence that helps us to live that benevolence that helps us to live better, keep our dignity, and and make better, keep our dignity, and and make better, keep our dignity, and and make our community better. our community better. our community better. >> I'd like to take a quick break and >> I'd like to take a quick break and >> I'd like to take a quick break and acknowledge our sponsor, AG1. I'm acknowledge our sponsor, AG1. I'm acknowledge our sponsor, AG1. I'm excited to share that AG1 has just excited to share that AG1 has just excited to share that AG1 has just launched their newest formulation, AG1 launched their newest formulation, AG1 launched their newest formulation, AG1 Pro. AG1 Pro takes the clinically backed Pro. AG1 Pro takes the clinically backed Pro. AG1 Pro takes the clinically backed AG1 formula, which is a blend of AG1 formula, which is a blend of AG1 formula, which is a blend of vitamins, minerals, probiotics, and vitamins, minerals, probiotics, and vitamins, minerals, probiotics, and adaptogens, and adds three important new adaptogens, and adds three important new adaptogens, and adds three important new ingredients. Creatine monohydrate, ingredients. Creatine monohydrate, ingredients. Creatine monohydrate, calcium HMBB, and zinc carnosine. Each calcium HMBB, and zinc carnosine. Each calcium HMBB, and zinc carnosine. Each serving has 5 grams of creatine serving has 5 grams of creatine serving has 5 grams of creatine monohydrate to support muscle strength monohydrate to support muscle strength monohydrate to support muscle strength and performance, as well as brain and performance, as well as brain and performance, as well as brain health. Calcium HMBB to support muscle health. Calcium HMBB to support muscle health. Calcium HMBB to support muscle recovery and reduce muscle breakdown, recovery and reduce muscle breakdown, recovery and reduce muscle breakdown, and zinc carnosine to support and and zinc carnosine to support and and zinc carnosine to support and improve the lining of your gut. All improve the lining of your gut. All improve the lining of your gut. All three of these ingredients have three of these ingredients have three of these ingredients have compelling science to support them, and compelling science to support them, and compelling science to support them, and therefore, I love seeing them added to therefore, I love seeing them added to therefore, I love seeing them added to the existing AG1 formula. As most of you the existing AG1 formula. As most of you the existing AG1 formula. As most of you know, I've been taking AG1 every day for know, I've been taking AG1 every day for know, I've been taking AG1 every day for nearly 14 years now. I started taking it nearly 14 years now. I started taking it nearly 14 years now. I started taking it long before I even knew what a podcast long before I even knew what a podcast long before I even knew what a podcast was. It's a great product, and it's now was. It's a great product, and it's now was. It's a great product, and it's now made even better with the new AG1 Pro made even better with the new AG1 Pro made even better with the new AG1 Pro formula. If you would like to try AG1 formula. If you would like to try AG1 formula. If you would like to try AG1 Pro, you can go to drinkag1.com/huberman Pro, you can go to drinkag1.com/huberman Pro, you can go to drinkag1.com/huberman to get a special offer. AG1 is giving to get a special offer. AG1 is giving to get a special offer. AG1 is giving away a free bottle of Omega-3 co-enzyme away a free bottle of Omega-3 co-enzyme away a free bottle of Omega-3 co-enzyme Q10 with your first subscription. Again, Q10 with your first subscription. Again, Q10 with your first subscription. Again, go to drinkag1.com/huberman go to drinkag1.com/huberman go to drinkag1.com/huberman to get a free bottle of omega-3 to get a free bottle of omega-3 to get a free bottle of omega-3 co-enzyme Q10 with your first AG1 co-enzyme Q10 with your first AG1 co-enzyme Q10 with your first AG1 subscription. Today's episode is also subscription. Today's episode is also subscription. Today's episode is also brought to us by Element. Element is an brought to us by Element. Element is an brought to us by Element. Element is an electrolyte drink that has everything electrolyte drink that has everything electrolyte drink that has everything you need and nothing you don't. That you need and nothing you don't. That you need and nothing you don't. That means the electrolytes, sodium, means the electrolytes, sodium, means the electrolytes, sodium, magnesium, and potassium, all in the magnesium, and potassium, all in the magnesium, and potassium, all in the correct ratios, but no sugar. Proper correct ratios, but no sugar. Proper correct ratios, but no sugar. Proper hydration is critical for brain and body hydration is critical for brain and body hydration is critical for brain and body function. Even a slight degree of function. Even a slight degree of function. Even a slight degree of dehydration can diminish your cognitive dehydration can diminish your cognitive dehydration can diminish your cognitive and physical performance. It's also and physical performance. It's also and physical performance. It's also important that you get adequate important that you get adequate important that you get adequate electrolytes. The electrolytes, sodium, electrolytes. The electrolytes, sodium, electrolytes. The electrolytes, sodium, magnesium, and potassium are vital for magnesium, and potassium are vital for magnesium, and potassium are vital for the functioning of all cells in your the functioning of all cells in your the functioning of all cells in your body, especially your neurons or your body, especially your neurons or your body, especially your neurons or your nerve cells. Drinking element makes it nerve cells. Drinking element makes it nerve cells. Drinking element makes it very easy to ensure that you're getting very easy to ensure that you're getting very easy to ensure that you're getting adequate hydration and adequate adequate hydration and adequate adequate hydration and adequate electrolytes. My days tend to start electrolytes. My days tend to start electrolytes. My days tend to start really fast, meaning I have to jump really fast, meaning I have to jump really fast, meaning I have to jump right into work or right into exercise. right into work or right into exercise. right into work or right into exercise. So, to make sure that I'm hydrated and I So, to make sure that I'm hydrated and I So, to make sure that I'm hydrated and I have sufficient electrolytes when I have sufficient electrolytes when I have sufficient electrolytes when I first wake up in the morning, I drink 16 first wake up in the morning, I drink 16 first wake up in the morning, I drink 16 to 32 ounces of water with an element to 32 ounces of water with an element to 32 ounces of water with an element packet dissolved in it. I also drink packet dissolved in it. I also drink packet dissolved in it. I also drink Element dissolved in water during any Element dissolved in water during any Element dissolved in water during any kind of physical exercise that I'm kind of physical exercise that I'm kind of physical exercise that I'm doing, especially on hot days when I'm doing, especially on hot days when I'm doing, especially on hot days when I'm sweating a lot and losing water and sweating a lot and losing water and sweating a lot and losing water and electrolytes. Element has a bunch of electrolytes. Element has a bunch of electrolytes. Element has a bunch of great tasting flavors. In fact, I love great tasting flavors. In fact, I love great tasting flavors. In fact, I love them all. I love the watermelon, the them all. I love the watermelon, the them all. I love the watermelon, the raspberry, the citrus, and I really love raspberry, the citrus, and I really love raspberry, the citrus, and I really love the lemonade flavor. So, if you'd like the lemonade flavor. So, if you'd like the lemonade flavor. So, if you'd like to try Element, you can go to to try Element, you can go to to try Element, you can go to drinkelement.com/huberman drinkelement.com/huberman drinkelement.com/huberman to claim a free element sample pack with to claim a free element sample pack with to claim a free element sample pack with any purchase. Again, that's any purchase. Again, that's any purchase. Again, that's drinkelement.com/huberman drinkelement.com/huberman drinkelement.com/huberman to claim a free sample pack. The idea to claim a free sample pack. The idea to claim a free sample pack. The idea that technologies can be connectors as that technologies can be connectors as that technologies can be connectors as opposed to separators, I think, has to opposed to separators, I think, has to opposed to separators, I think, has to sit at the center of the discussion. sit at the center of the discussion. sit at the center of the discussion. Yes. And we all know who they are that Yes. And we all know who they are that Yes. And we all know who they are that they're they're several of them. But the they're they're several of them. But the they're they're several of them. But the big names in this field, you know, they big names in this field, you know, they big names in this field, you know, they they are also in a developmental process they are also in a developmental process they are also in a developmental process where they're learning how to be public where they're learning how to be public where they're learning how to be public facing and it happens very fast. Like, facing and it happens very fast. Like, facing and it happens very fast. Like, you know, the the microscope is on them you know, the the microscope is on them you know, the the microscope is on them and the cameras are on them and and so and the cameras are on them and and so and the cameras are on them and and so every every subtle dysfunction is every every subtle dysfunction is every every subtle dysfunction is magnified. So I like to think that they magnified. So I like to think that they magnified. So I like to think that they will mature quickly enough to realize will mature quickly enough to realize will mature quickly enough to realize that and I think they are that some are that and I think they are that some are that and I think they are that some are that the public needs to hear the that the public needs to hear the that the public needs to hear the correct the true message but in a way correct the true message but in a way correct the true message but in a way that makes them understand. That's the that makes them understand. That's the that makes them understand. That's the the kind of dirty secret of medicine and the kind of dirty secret of medicine and the kind of dirty secret of medicine and academia that you break this mold. I academia that you break this mold. I academia that you break this mold. I like to think I break this mold is that like to think I break this mold is that like to think I break this mold is that there's a power in not sharing how there's a power in not sharing how there's a power in not sharing how things work. Yep. things work. Yep. things work. Yep. >> But it doesn't serve anybody well at the >> But it doesn't serve anybody well at the >> But it doesn't serve anybody well at the end of the day. Like you pull back the end of the day. Like you pull back the end of the day. Like you pull back the veil and let people in and people feel veil and let people in and people feel veil and let people in and people feel safer. safer. safer. >> Yeah. There's a power in in not sharing. >> Yeah. There's a power in in not sharing. >> Yeah. There's a power in in not sharing. There's also a power to say just trust There's also a power to say just trust There's also a power to say just trust me I will tell you and the neither as me I will tell you and the neither as me I will tell you and the neither as educators that is we don't go to our educators that is we don't go to our educators that is we don't go to our lectures and say just trust me you know lectures and say just trust me you know lectures and say just trust me you know 2 plus 2 equals four. We actually say 2 plus 2 equals four. We actually say 2 plus 2 equals four. We actually say here's how you break it down and learn here's how you break it down and learn here's how you break it down and learn about it so next time you can do it about it so next time you can do it about it so next time you can do it yourself. Right. I also think that yourself. Right. I also think that yourself. Right. I also think that especially you are your podcast is so especially you are your podcast is so especially you are your podcast is so important as part of public important as part of public important as part of public communication education of knowledge. I communication education of knowledge. I communication education of knowledge. I also think that we need to hear voices also think that we need to hear voices also think that we need to hear voices of different different background, of different different background, of different different background, right? So because there are plenty of right? So because there are plenty of right? So because there are plenty of scholars, technologists, builders, uh scholars, technologists, builders, uh scholars, technologists, builders, uh thinkers out there who have been dealing thinkers out there who have been dealing thinkers out there who have been dealing with AI, using AI, thinking hard about with AI, using AI, thinking hard about with AI, using AI, thinking hard about how to use AI to empower people, and how to use AI to empower people, and how to use AI to empower people, and these voices are so important. Well, these voices are so important. Well, these voices are so important. Well, certainly I'll take names of people to certainly I'll take names of people to certainly I'll take names of people to to host in addition to you, but since uh to host in addition to you, but since uh to host in addition to you, but since uh you're here, I'm going to go next to you're here, I'm going to go next to you're here, I'm going to go next to something that I think most everybody something that I think most everybody something that I think most everybody would agree would be a wonderful thing would agree would be a wonderful thing would agree would be a wonderful thing if it existed and it's already starting if it existed and it's already starting if it existed and it's already starting to happen, which is the use of AI to to happen, which is the use of AI to to happen, which is the use of AI to augment health discovery, treatment of augment health discovery, treatment of augment health discovery, treatment of disease, and so on. So, using the disease, and so on. So, using the disease, and so on. So, using the AlphaGo example from before, and people AlphaGo example from before, and people AlphaGo example from before, and people surely still remember the cat example, surely still remember the cat example, surely still remember the cat example, those just follow certain rules. Alph Go those just follow certain rules. Alph Go those just follow certain rules. Alph Go is very complicated set of rules, but if is very complicated set of rules, but if is very complicated set of rules, but if you learn them, there's a constrained you learn them, there's a constrained you learn them, there's a constrained set of rules. set of rules. set of rules. >> With the cat, it seems unconstrained, >> With the cat, it seems unconstrained, >> With the cat, it seems unconstrained, like infinite possibilities, but it's like infinite possibilities, but it's like infinite possibilities, but it's constrained enough that machines and constrained enough that machines and constrained enough that machines and humans can learn it really well. humans can learn it really well. humans can learn it really well. >> When you start getting into medicine, >> When you start getting into medicine, >> When you start getting into medicine, >> there are rules of medicine. There are >> there are rules of medicine. There are >> there are rules of medicine. There are rules of science. You have a question, rules of science. You have a question, rules of science. You have a question, you pose a hypothesis, you test the you pose a hypothesis, you test the you pose a hypothesis, you test the hypothesis, you try and rule out your hypothesis, you try and rule out your hypothesis, you try and rule out your hypo and so on like the the scientific hypo and so on like the the scientific hypo and so on like the the scientific method. And in medicine, every field has method. And in medicine, every field has method. And in medicine, every field has its methods. We observe, we observe its methods. We observe, we observe its methods. We observe, we observe disease, we observe who recovers, we disease, we observe who recovers, we disease, we observe who recovers, we have a case report, we do a randomized have a case report, we do a randomized have a case report, we do a randomized control trial. So there are rules and control trial. So there are rules and control trial. So there are rules and the internet knows these rules. So LLMs the internet knows these rules. So LLMs the internet knows these rules. So LLMs can be used to mine health information can be used to mine health information can be used to mine health information very well because there are constrained very well because there are constrained very well because there are constrained rules. But I think you and I both know rules. But I think you and I both know rules. But I think you and I both know because I also consider you a biologist because I also consider you a biologist because I also consider you a biologist that the rules of biology are still that the rules of biology are still that the rules of biology are still revealing themselves to us. Which is not revealing themselves to us. Which is not revealing themselves to us. Which is not to say that the dermatologists, to say that the dermatologists, to say that the dermatologists, neurosurgeons, and oncologists don't neurosurgeons, and oncologists don't neurosurgeons, and oncologists don't know what they're doing, but they're know what they're doing, but they're know what they're doing, but they're doing what they're doing within a doing what they're doing within a doing what they're doing within a constrained set of rules that they constrained set of rules that they constrained set of rules that they learned. And even if they continue to learned. And even if they continue to learned. And even if they continue to learn and update them, learn and update them, learn and update them, >> it's every month it seems now that a >> it's every month it seems now that a >> it's every month it seems now that a discovery comes out that violates the discovery comes out that violates the discovery comes out that violates the rule. Like I learned that action rule. Like I learned that action rule. Like I learned that action potentials are unitary. They always look potentials are unitary. They always look potentials are unitary. They always look the same. You either fire or not. the same. You either fire or not. the same. You either fire or not. >> But there was a paper not but 12 years >> But there was a paper not but 12 years >> But there was a paper not but 12 years ago that showed that the shape of an ago that showed that the shape of an ago that showed that the shape of an action potential can vary quite a lot. action potential can vary quite a lot. action potential can vary quite a lot. It was published in Nature. Mhm. It was published in Nature. Mhm. It was published in Nature. Mhm. >> Everyone saw it and then no one wanted >> Everyone saw it and then no one wanted >> Everyone saw it and then no one wanted to deal with it. It's just too much. It to deal with it. It's just too much. It to deal with it. It's just too much. It changes the rule. changes the rule. changes the rule. >> Neurons are supposed to be either graded >> Neurons are supposed to be either graded >> Neurons are supposed to be either graded or all are one. And the all I mean it's or all are one. And the all I mean it's or all are one. And the all I mean it's in every single textbook. So now if I in every single textbook. So now if I in every single textbook. So now if I take a bunch of neural activity and I take a bunch of neural activity and I take a bunch of neural activity and I give it the rule, oh well you know give it the rule, oh well you know give it the rule, oh well you know action potentials can be big, they can action potentials can be big, they can action potentials can be big, they can be small in the same neuron. It be small in the same neuron. It be small in the same neuron. It completely confuses everything we completely confuses everything we completely confuses everything we understand about neuroscience understand about neuroscience understand about neuroscience >> and it just our understanding of the >> and it just our understanding of the >> and it just our understanding of the brain just breaks down to zero. Yeah. brain just breaks down to zero. Yeah. brain just breaks down to zero. Yeah. But if you gave AI the rule that it But if you gave AI the rule that it But if you gave AI the rule that it could be, you know, a hundred different could be, you know, a hundred different could be, you know, a hundred different shapes of this signal, well, AI could shapes of this signal, well, AI could shapes of this signal, well, AI could probably do a lot more than even the probably do a lot more than even the probably do a lot more than even the very very best graduate student at dare very very best graduate student at dare very very best graduate student at dare I say Stanford or to be fair MIT or I say Stanford or to be fair MIT or I say Stanford or to be fair MIT or Caltech. I don't think it can do it and Caltech. I don't think it can do it and Caltech. I don't think it can do it and it can do it like in the duration of it can do it like in the duration of it can do it like in the duration of this question, which admittedly is a bit this question, which admittedly is a bit this question, which admittedly is a bit long. So, I'd like to get your thoughts long. So, I'd like to get your thoughts long. So, I'd like to get your thoughts on how is it that humans in health care, on how is it that humans in health care, on how is it that humans in health care, the general public and AI can the general public and AI can the general public and AI can collaborate to help solve disease and collaborate to help solve disease and collaborate to help solve disease and ideally come up with new rules for ideally come up with new rules for ideally come up with new rules for discovery so that we can finally discovery so that we can finally discovery so that we can finally understand our biology at a level that understand our biology at a level that understand our biology at a level that can really change the course of humanity can really change the course of humanity can really change the course of humanity for the better. for the better. for the better. >> Yeah. No, Andrew, this is probably >> Yeah. No, Andrew, this is probably >> Yeah. No, Andrew, this is probably perhaps you touch one of the most perhaps you touch one of the most perhaps you touch one of the most exciting usage of AI, which is exciting usage of AI, which is exciting usage of AI, which is scientific discovery. And in the case of scientific discovery. And in the case of scientific discovery. And in the case of biio medicine, you know, scientific biio medicine, you know, scientific biio medicine, you know, scientific discovery directly connects to human discovery directly connects to human discovery directly connects to human health and diseases, I think we're we're health and diseases, I think we're we're health and diseases, I think we're we're ready for complete re rewriting of how ready for complete re rewriting of how ready for complete re rewriting of how scientific discovery can be done because scientific discovery can be done because scientific discovery can be done because for ages, I don't even know how long, it for ages, I don't even know how long, it for ages, I don't even know how long, it relies on smart humans retaining what relies on smart humans retaining what relies on smart humans retaining what they have learned from other smart they have learned from other smart they have learned from other smart humans and and and doing things at the humans and and and doing things at the humans and and and doing things at the speed of our own muscles, I guess, you speed of our own muscles, I guess, you speed of our own muscles, I guess, you know. Most likely of course there's like know. Most likely of course there's like know. Most likely of course there's like super colliders and and all that but by super colliders and and all that but by super colliders and and all that but by and large the the ways of doing and large the the ways of doing and large the the ways of doing scientific discovery scientific discovery scientific discovery human brain or scientists brain are the human brain or scientists brain are the human brain or scientists brain are the only central character in this process. only central character in this process. only central character in this process. Now we have a new tool whose brain that Now we have a new tool whose brain that Now we have a new tool whose brain that can retain humongous amount of can retain humongous amount of can retain humongous amount of information can help us synthesize information can help us synthesize information can help us synthesize knowledge can go across disciplines in knowledge can go across disciplines in knowledge can go across disciplines in ways that you and I cannot go. So for ways that you and I cannot go. So for ways that you and I cannot go. So for example we happen to be both in the example we happen to be both in the example we happen to be both in the vision neuroscience AI domain. vision neuroscience AI domain. vision neuroscience AI domain. I know nothing about you know oactory I know nothing about you know oactory I know nothing about you know oactory zero like I don't even know how to spell zero like I don't even know how to spell zero like I don't even know how to spell most of probably the these words in that most of probably the these words in that most of probably the these words in that our colleagues know right so it's so our colleagues know right so it's so our colleagues know right so it's so hard for our brain but now we have a hard for our brain but now we have a hard for our brain but now we have a tool that can break open so so I think tool that can break open so so I think tool that can break open so so I think that that that >> we need to change we need to use this >> we need to change we need to use this >> we need to change we need to use this tool we absolutely I I was just thinking tool we absolutely I I was just thinking tool we absolutely I I was just thinking 150 or I don't know exactly when years 150 or I don't know exactly when years 150 or I don't know exactly when years ago we electricity ago we electricity ago we electricity changed everything in in in our life, changed everything in in in our life, changed everything in in in our life, right? I'm sure that's a moment we were right? I'm sure that's a moment we were right? I'm sure that's a moment we were thinking about how thinking about how thinking about how the changes, the opportunities, the the changes, the opportunities, the the changes, the opportunities, the scary moment. I think we have to come to scary moment. I think we have to come to scary moment. I think we have to come to reckon that scientific discovery is one reckon that scientific discovery is one reckon that scientific discovery is one of the most exciting opportunity for AI of the most exciting opportunity for AI of the most exciting opportunity for AI and for health, right? How information and for health, right? How information and for health, right? How information can be synthesized, how information can can be synthesized, how information can can be synthesized, how information can be presented not only to clinicians but be presented not only to clinicians but be presented not only to clinicians but also to patients and how patients can also to patients and how patients can also to patients and how patients can participate in that process from participate in that process from participate in that process from diagnosis to treatment is also there is diagnosis to treatment is also there is diagnosis to treatment is also there is just so much we can do now. [snorts] just so much we can do now. [snorts] just so much we can do now. [snorts] >> Yeah. I mean AI I won't say AI is better >> Yeah. I mean AI I won't say AI is better >> Yeah. I mean AI I won't say AI is better than all doctors but AI was able to than all doctors but AI was able to than all doctors but AI was able to disambiguate vertigo from low blood disambiguate vertigo from low blood disambiguate vertigo from low blood pressure for me a few months back and pressure for me a few months back and pressure for me a few months back and one of the people who got it wrong is a one of the people who got it wrong is a one of the people who got it wrong is a ENT who works on the vestibular system ENT who works on the vestibular system ENT who works on the vestibular system >> what information did you provide just >> what information did you provide just >> what information did you provide just your subjective your subjective your subjective >> my subjective experience over a day or >> my subjective experience over a day or >> my subjective experience over a day or two two two >> okay good >> okay good >> okay good >> um turns out it was a medication that a >> um turns out it was a medication that a >> um turns out it was a medication that a doctor had prescribed me that I had a doctor had prescribed me that I had a doctor had prescribed me that I had a like a mild but adverse event and it's a like a mild but adverse event and it's a like a mild but adverse event and it's a weird thing to step and feel like the weird thing to step and feel like the weird thing to step and feel like the whole world's dropping down and then whole world's dropping down and then whole world's dropping down and then kind of spinning and I thought my kind of spinning and I thought my kind of spinning and I thought my goodness like feels like vertigo but I goodness like feels like vertigo but I goodness like feels like vertigo but I remember dizzy and lightheaded or remember dizzy and lightheaded or remember dizzy and lightheaded or different. So I started like looking different. So I started like looking different. So I started like looking into that and then and um sure enough it into that and then and um sure enough it into that and then and um sure enough it was a it was a blood pressure issue. It was a it was a blood pressure issue. It was a it was a blood pressure issue. It brought brought my blood pressure excuse brought brought my blood pressure excuse brought brought my blood pressure excuse me down too low me down too low me down too low >> and but I consult we know some smart >> and but I consult we know some smart >> and but I consult we know some smart doctors um none of these were at doctors um none of these were at doctors um none of these were at Stanford. I will say that this is the Stanford. I will say that this is the Stanford. I will say that this is the truth. But it was truth. But it was truth. But it was >> we should just be intellectually honest. >> we should just be intellectually honest. >> we should just be intellectually honest. >> But it's just remarkable. And when I ran >> But it's just remarkable. And when I ran >> But it's just remarkable. And when I ran it back to them, they were like, "That's it back to them, they were like, "That's it back to them, they were like, "That's really incredible." You know, had you really incredible." You know, had you really incredible." You know, had you not been on the phone with me and in my not been on the phone with me and in my not been on the phone with me and in my clinic, I would have been able to do clinic, I would have been able to do clinic, I would have been able to do some additional testing to be fair. But some additional testing to be fair. But some additional testing to be fair. But this was zero cost. It took a morning to this was zero cost. It took a morning to this was zero cost. It took a morning to know if I drank some uh electrolytes at know if I drank some uh electrolytes at know if I drank some uh electrolytes at what I would have thought would be what I would have thought would be what I would have thought would be excessive level that by two hours later, excessive level that by two hours later, excessive level that by two hours later, I would be fine. Now, of course, there's I would be fine. Now, of course, there's I would be fine. Now, of course, there's the possibility of a placebo effect the possibility of a placebo effect the possibility of a placebo effect here, but two hours later, I was fine. here, but two hours later, I was fine. here, but two hours later, I was fine. >> And so, it's also very consoling to the >> And so, it's also very consoling to the >> And so, it's also very consoling to the patient patient patient >> to have this. And so, it's not to say >> to have this. And so, it's not to say >> to have this. And so, it's not to say don't go to a doctor, but it it's don't go to a doctor, but it it's don't go to a doctor, but it it's incredible. I mean, this exists now. incredible. I mean, this exists now. incredible. I mean, this exists now. >> Doctor can use this tooling. By the way, >> Doctor can use this tooling. By the way, >> Doctor can use this tooling. By the way, I have a very interesting example. You I have a very interesting example. You I have a very interesting example. You know that we have to reschedule this uh know that we have to reschedule this uh know that we have to reschedule this uh our conversation because my father was our conversation because my father was our conversation because my father was going through a surgery right at going through a surgery right at going through a surgery right at Stanford uh with an incredible surgeon. Stanford uh with an incredible surgeon. Stanford uh with an incredible surgeon. But the surgery was done by a robot, the But the surgery was done by a robot, the But the surgery was done by a robot, the Davinci robot system because it was a Davinci robot system because it was a Davinci robot system because it was a liver surgery and the surgeon, liver surgery and the surgeon, liver surgery and the surgeon, incredible surgeon was driving the incredible surgeon was driving the incredible surgeon was driving the robot. So it was a deep human machine robot. So it was a deep human machine robot. So it was a deep human machine collaboration. After the surgery, I collaboration. After the surgery, I collaboration. After the surgery, I asked the surgeon, I said, "Do you asked the surgeon, I said, "Do you asked the surgeon, I said, "Do you imagine if say you've done a million, imagine if say you've done a million, imagine if say you've done a million, which is impossible for a surgeon, but which is impossible for a surgeon, but which is impossible for a surgeon, but human surgeon, but let's collect all of human surgeon, but let's collect all of human surgeon, but let's collect all of human surgeons uh for for this liver, human surgeons uh for for this liver, human surgeons uh for for this liver, this type of liver surgery data. Can we this type of liver surgery data. Can we this type of liver surgery data. Can we possibly train a automatic AI to do possibly train a automatic AI to do possibly train a automatic AI to do this?" The answer was not clear. So we this?" The answer was not clear. So we this?" The answer was not clear. So we went a little bit down the rabbit hole went a little bit down the rabbit hole went a little bit down the rabbit hole because liver is a very complicated because liver is a very complicated because liver is a very complicated organ. It's extremely vascular. It has a organ. It's extremely vascular. It has a organ. It's extremely vascular. It has a lot of vessels and everybody's liver is lot of vessels and everybody's liver is lot of vessels and everybody's liver is very different. So given the reality of very different. So given the reality of very different. So given the reality of how many patients undergo liver surgery how many patients undergo liver surgery how many patients undergo liver surgery per year, even if you aggregate um the per year, even if you aggregate um the per year, even if you aggregate um the world's liver patient um surgeries, you world's liver patient um surgeries, you world's liver patient um surgeries, you might not have enough data to train might not have enough data to train might not have enough data to train these algorithm. So this speaks of a these algorithm. So this speaks of a these algorithm. So this speaks of a very important fact that um AI learns very important fact that um AI learns very important fact that um AI learns from patterns. When the patterns are not from patterns. When the patterns are not from patterns. When the patterns are not abundant, abundant, abundant, then we have to be careful. We have to then we have to be careful. We have to then we have to be careful. We have to know how to use AI or how not to use AI. know how to use AI or how not to use AI. know how to use AI or how not to use AI. You know in this case that having a You know in this case that having a You know in this case that having a human collaborating with the robot is human collaborating with the robot is human collaborating with the robot is way better than a underlearned robot way better than a underlearned robot way better than a underlearned robot doing the surgery by itself. But the doing the surgery by itself. But the doing the surgery by itself. But the same issue might be true for surgeons same issue might be true for surgeons same issue might be true for surgeons because how many surgeries a surgeon can because how many surgeries a surgeon can because how many surgeries a surgeon can get trained on. So these are get trained on. So these are get trained on. So these are opportunities that humans and AI can opportunities that humans and AI can opportunities that humans and AI can totally collaborate with and might totally collaborate with and might totally collaborate with and might reveal the best result. Right now the reveal the best result. Right now the reveal the best result. Right now the future remains to be seen. Can we create future remains to be seen. Can we create future remains to be seen. Can we create a artificial simulation of a liver that a artificial simulation of a liver that a artificial simulation of a liver that we can now train infinite possibility? we can now train infinite possibility? we can now train infinite possibility? These are all incredibly open scientific These are all incredibly open scientific These are all incredibly open scientific possibilities that is waiting ahead of possibilities that is waiting ahead of possibilities that is waiting ahead of us. But then there are us. But then there are us. But then there are uh situations like your situation where uh situations like your situation where uh situations like your situation where the vertigo versus low blood pressure the vertigo versus low blood pressure the vertigo versus low blood pressure probably have been reported so many probably have been reported so many probably have been reported so many times that in the database there's times that in the database there's times that in the database there's enough of that that AI has learned that. enough of that that AI has learned that. enough of that that AI has learned that. So we can then now take advantage of So we can then now take advantage of So we can then now take advantage of that for people who don't have immediate that for people who don't have immediate that for people who don't have immediate access to doctors. access to doctors. access to doctors. >> Amazing. Is your father's surgery went >> Amazing. Is your father's surgery went >> Amazing. Is your father's surgery went okay? okay? okay? >> It did. It actually >> It did. It actually >> It did. It actually lost lost lost >> 10x less blood than a typical surgery >> 10x less blood than a typical surgery >> 10x less blood than a typical surgery >> uh thanks to the laparoscopic capability >> uh thanks to the laparoscopic capability >> uh thanks to the laparoscopic capability of a robot surgery. of a robot surgery. of a robot surgery. >> I'd like to talk a little bit about some >> I'd like to talk a little bit about some >> I'd like to talk a little bit about some features that we think are uniquely features that we think are uniquely features that we think are uniquely human that may or may not be. You'll human that may or may not be. You'll human that may or may not be. You'll tell me. These are genuine questions, tell me. These are genuine questions, tell me. These are genuine questions, not loaded questions. And then I'd also not loaded questions. And then I'd also not loaded questions. And then I'd also like to get educated on how AI is like to get educated on how AI is like to get educated on how AI is structured to allow these things to structured to allow these things to structured to allow these things to happen. For instance, intuition. We all happen. For instance, intuition. We all happen. For instance, intuition. We all like to think of intuition as this like like to think of intuition as this like like to think of intuition as this like mystical very like it certainly is mystical very like it certainly is mystical very like it certainly is powerful, but this thing that like we powerful, but this thing that like we powerful, but this thing that like we own that no one can take from us that own that no one can take from us that own that no one can take from us that can't be mimicked kind of thing. But I can't be mimicked kind of thing. But I can't be mimicked kind of thing. But I could also break intuition down to be could also break intuition down to be could also break intuition down to be well, it's my experience over time. It's well, it's my experience over time. It's well, it's my experience over time. It's a data set a data set a data set coupled to some bodily and brain coupled to some bodily and brain coupled to some bodily and brain sensations and some prediction cues like sensations and some prediction cues like sensations and some prediction cues like the last time I felt this this happened. the last time I felt this this happened. the last time I felt this this happened. The last two times I felt that things The last two times I felt that things The last two times I felt that things didn't work out that way so I'm going to didn't work out that way so I'm going to didn't work out that way so I'm going to go this ways. I mean that you could go this ways. I mean that you could go this ways. I mean that you could assign these rules to a computer. But assign these rules to a computer. But assign these rules to a computer. But there are other aspects of our deeper there are other aspects of our deeper there are other aspects of our deeper self if I can refer to them that way. self if I can refer to them that way. self if I can refer to them that way. Like we don't know where intuition is Like we don't know where intuition is Like we don't know where intuition is mapped in the body could do an imaging mapped in the body could do an imaging mapped in the body could do an imaging experiment but you're not going to experiment but you're not going to experiment but you're not going to collect all the neurons and hormones and collect all the neurons and hormones and collect all the neurons and hormones and everything simultaneously. who don't everything simultaneously. who don't everything simultaneously. who don't really have like a location or even a really have like a location or even a really have like a location or even a network to to point to like things like network to to point to like things like network to to point to like things like creativity, intuition, premonition, the creativity, intuition, premonition, the creativity, intuition, premonition, the idea that you know you really sense idea that you know you really sense idea that you know you really sense something is coming on but it hasn't something is coming on but it hasn't something is coming on but it hasn't happened yet. happened yet. happened yet. What sorts of rules can AI get that What sorts of rules can AI get that What sorts of rules can AI get that could give it these sorts of could give it these sorts of could give it these sorts of capabilities? And here I'm want to talk capabilities? And here I'm want to talk capabilities? And here I'm want to talk about it in the context if you will of about it in the context if you will of about it in the context if you will of energy. So whatever this thing is, it's energy. So whatever this thing is, it's energy. So whatever this thing is, it's like mitochondria driving cells more like mitochondria driving cells more like mitochondria driving cells more around one thing versus another, the around one thing versus another, the around one thing versus another, the same way fear or happiness would, right? same way fear or happiness would, right? same way fear or happiness would, right? We were just talking about energy. But We were just talking about energy. But We were just talking about energy. But within AI systems, and I'm not a within AI systems, and I'm not a within AI systems, and I'm not a computer scientist, within AI systems computer scientist, within AI systems computer scientist, within AI systems and GPUs, can we actually allocate more and GPUs, can we actually allocate more and GPUs, can we actually allocate more energetic flow through particular energetic flow through particular energetic flow through particular learning rules? So we could tell maybe learning rules? So we could tell maybe learning rules? So we could tell maybe someday you know based on everything you someday you know based on everything you someday you know based on everything you know about know about know about my sister who I love you know what is my sister who I love you know what is my sister who I love you know what is your intuition about how our uh brother your intuition about how our uh brother your intuition about how our uh brother sister relationship will evolve over sister relationship will evolve over sister relationship will evolve over time and what is your sense about what time and what is your sense about what time and what is your sense about what would be great for us to do perhaps for would be great for us to do perhaps for would be great for us to do perhaps for our birthdays this year that's different our birthdays this year that's different our birthdays this year that's different than before giving and it only has than before giving and it only has than before giving and it only has access to the internet can it actually access to the internet can it actually access to the internet can it actually become sort of mindlike or mindbody like become sort of mindlike or mindbody like become sort of mindlike or mindbody like and come up with a sort of sense of what and come up with a sort of sense of what and come up with a sort of sense of what might actually be worthwhile or does it might actually be worthwhile or does it might actually be worthwhile or does it just need more and more prompts like just need more and more prompts like just need more and more prompts like it's just going to keep asking me it's just going to keep asking me it's just going to keep asking me questions so I'm actually doing the questions so I'm actually doing the questions so I'm actually doing the work. work. work. >> Such a interesting question Andrew. So >> Such a interesting question Andrew. So >> Such a interesting question Andrew. So um I do want to separate intuition from um I do want to separate intuition from um I do want to separate intuition from creativity for the sake of argument here creativity for the sake of argument here creativity for the sake of argument here and maybe we'll come back to merging. So and maybe we'll come back to merging. So and maybe we'll come back to merging. So let's talk about this intuition of given let's talk about this intuition of given let's talk about this intuition of given my sibling love my sibling love my sibling love what's going to happen right is it what's going to happen right is it what's going to happen right is it really intuition so today when you go to really intuition so today when you go to really intuition so today when you go to a AI chatbot you're going to prompt you a AI chatbot you're going to prompt you a AI chatbot you're going to prompt you know I'm a Stanford professor and a um a know I'm a Stanford professor and a um a know I'm a Stanford professor and a um a um neuroscientist um neuroscientist um neuroscientist um give me this information that is um give me this information that is um give me this information that is already called context I don't know if already called context I don't know if already called context I don't know if you call it intuition but because you you call it intuition but because you you call it intuition but because you gave that piece of information. The AI's gave that piece of information. The AI's gave that piece of information. The AI's answer for you is already going to be answer for you is already going to be answer for you is already going to be different if I type that I'm a 14 year different if I type that I'm a 14 year different if I type that I'm a 14 year old teenager, you know, loving race old teenager, you know, loving race old teenager, you know, loving race cars. Even if we ask the same question, cars. Even if we ask the same question, cars. Even if we ask the same question, it'll have customized answer. That is a it'll have customized answer. That is a it'll have customized answer. That is a mathematical mathematical mathematical I wouldn't call it energy. I want to be I wouldn't call it energy. I want to be I wouldn't call it energy. I want to be that is just a mathematical that is just a mathematical that is just a mathematical uh fact of how these um these algorithms uh fact of how these um these algorithms uh fact of how these um these algorithms takes these context and tailor the the takes these context and tailor the the takes these context and tailor the the the outputs and it's called context. the outputs and it's called context. the outputs and it's called context. It's not that deep in the in computer It's not that deep in the in computer It's not that deep in the in computer science. That's one type of intuition science. That's one type of intuition science. That's one type of intuition that is fairly shallow because you that is fairly shallow because you that is fairly shallow because you already are able to use language to already are able to use language to already are able to use language to describe it or you can say I'll upload describe it or you can say I'll upload describe it or you can say I'll upload an image that that also is is already an image that that also is is already an image that that also is is already expressable and then AI gets it. The expressable and then AI gets it. The expressable and then AI gets it. The deeper intuition you just said is like deeper intuition you just said is like deeper intuition you just said is like you don't even know where they come you don't even know where they come you don't even know where they come from, right? Like is it because I smell from, right? Like is it because I smell from, right? Like is it because I smell something? Is it hormones? Is it you something? Is it hormones? Is it you something? Is it hormones? Is it you know the the mixture of mood? Is it my know the the mixture of mood? Is it my know the the mixture of mood? Is it my breakfast? breakfast? breakfast? That intuition, That intuition, That intuition, what would AI do with it? That is what I what would AI do with it? That is what I what would AI do with it? That is what I would say is inaccessible. There's no would say is inaccessible. There's no would say is inaccessible. There's no sensory apparatus yet that can glean sensory apparatus yet that can glean sensory apparatus yet that can glean that data and feed it to that data and feed it to that data and feed it to not only AI cannot even feed it to, you not only AI cannot even feed it to, you not only AI cannot even feed it to, you know, for example, sometimes as a couple know, for example, sometimes as a couple know, for example, sometimes as a couple you might have moment that you're just you might have moment that you're just you might have moment that you're just rubbing each other in the wrong way. rubbing each other in the wrong way. rubbing each other in the wrong way. >> Never. No, I'm just kidding. Yeah, of >> Never. No, I'm just kidding. Yeah, of >> Never. No, I'm just kidding. Yeah, of course. course. course. >> If you're really familiar with each >> If you're really familiar with each >> If you're really familiar with each other, you kind kind of can sense it, other, you kind kind of can sense it, other, you kind kind of can sense it, but you can't quite tell. Maybe you just but you can't quite tell. Maybe you just but you can't quite tell. Maybe you just leave quietly, leave that person alone. leave quietly, leave that person alone. leave quietly, leave that person alone. So that means whatever that intuition So that means whatever that intuition So that means whatever that intuition that person has, they could not even that person has, they could not even that person has, they could not even express it in words or or a gesture to express it in words or or a gesture to express it in words or or a gesture to give it to another person to use as a give it to another person to use as a give it to another person to use as a piece of information. So when you cannot piece of information. So when you cannot piece of information. So when you cannot even access that even access that even access that neither a human a different human nor a neither a human a different human nor a neither a human a different human nor a machine machine machine can can do anything about it because can can do anything about it because can can do anything about it because there's no access to that highly there's no access to that highly there's no access to that highly individualized intuition. individualized intuition. individualized intuition. There's no technology that can do that There's no technology that can do that There's no technology that can do that till you say we put brainwave collectors till you say we put brainwave collectors till you say we put brainwave collectors or you know skin conductance or you know skin conductance or you know skin conductance sensors. I mean by the time we do those sensors. I mean by the time we do those sensors. I mean by the time we do those maybe they become accessible. So we have maybe they become accessible. So we have maybe they become accessible. So we have to recognize. So so what I'm trying to to recognize. So so what I'm trying to to recognize. So so what I'm trying to say here is it's not what's not very say here is it's not what's not very say here is it's not what's not very deep is is the data accessible deep is is the data accessible deep is is the data accessible you know either through language or you know either through language or you know either through language or through picture or through imaging or through picture or through imaging or through picture or through imaging or through brain waves whatever it is it through brain waves whatever it is it through brain waves whatever it is it needs to be an accessible piece of needs to be an accessible piece of needs to be an accessible piece of information. If it's accessible information. If it's accessible information. If it's accessible then then then if we have collected enough of that you if we have collected enough of that you if we have collected enough of that you can train machines with or if a machine can train machines with or if a machine can train machines with or if a machine is well trained it can like you said in is well trained it can like you said in is well trained it can like you said in a private way forget about privacy uh uh a private way forget about privacy uh uh a private way forget about privacy uh uh uh breach but in a private way the uh breach but in a private way the uh breach but in a private way the machine can probably you use it. What machine can probably you use it. What machine can probably you use it. What I'm trying to do, Andrew, here is not to I'm trying to do, Andrew, here is not to I'm trying to do, Andrew, here is not to make it sound mystical, make it sound mystical, make it sound mystical, >> but try to give it a scientific >> but try to give it a scientific >> but try to give it a scientific process to describe if it were to process to describe if it were to process to describe if it were to happen, how would that happen? happen, how would that happen? happen, how would that happen? >> Yeah. Because um pattern recognition >> Yeah. Because um pattern recognition >> Yeah. Because um pattern recognition based on big data sets and rules get us based on big data sets and rules get us based on big data sets and rules get us a long way is what I'm hearing. And we a long way is what I'm hearing. And we a long way is what I'm hearing. And we earlier we were talking about where earlier we were talking about where earlier we were talking about where doctors fail and robots and machines doctors fail and robots and machines doctors fail and robots and machines perhaps do better or they collaborate to perhaps do better or they collaborate to perhaps do better or they collaborate to do better than either one alone. You do better than either one alone. You do better than either one alone. You know I as a neuroscientist you spend a know I as a neuroscientist you spend a know I as a neuroscientist you spend a lot of time looking at cells at some lot of time looking at cells at some lot of time looking at cells at some point in your career. And it's amazing point in your career. And it's amazing point in your career. And it's amazing how like the electrophysiologists for how like the electrophysiologists for how like the electrophysiologists for decades if not longer you develop an decades if not longer you develop an decades if not longer you develop an intuition. I'm not really a intuition. I'm not really a intuition. I'm not really a physiologist, but I learned to recognize physiologist, but I learned to recognize physiologist, but I learned to recognize cells based on like kind of these things cells based on like kind of these things cells based on like kind of these things that were not written up in any papers. that were not written up in any papers. that were not written up in any papers. But like if there was kind of a like a But like if there was kind of a like a But like if there was kind of a like a like a straighter edge along this thing like a straighter edge along this thing like a straighter edge along this thing and it had a certain shape and and it had a certain shape and and it had a certain shape and roundness, like I tell you right now, roundness, like I tell you right now, roundness, like I tell you right now, that's a transient offpha cell in the that's a transient offpha cell in the that's a transient offpha cell in the retina. Eventually, we we developed retina. Eventually, we we developed retina. Eventually, we we developed genetic labels to reveal that that was genetic labels to reveal that that was genetic labels to reveal that that was true in every case. But then you also true in every case. But then you also true in every case. But then you also saw some that didn't fit the rule. saw some that didn't fit the rule. saw some that didn't fit the rule. Machines can learn that, computers can Machines can learn that, computers can Machines can learn that, computers can learn that. And with all that learn that. And with all that learn that. And with all that information from all those papers, now information from all those papers, now information from all those papers, now we have a pretty good parts list of the we have a pretty good parts list of the we have a pretty good parts list of the retina. retina. retina. >> Cool. That works. And then you can apply >> Cool. That works. And then you can apply >> Cool. That works. And then you can apply rules like they fire this way, they fire rules like they fire this way, they fire rules like they fire this way, they fire that way. Okay, I'm good with all of that way. Okay, I'm good with all of that way. Okay, I'm good with all of that. What I think I was trying to get that. What I think I was trying to get that. What I think I was trying to get to with intuition, and I probably didn't to with intuition, and I probably didn't to with intuition, and I probably didn't give the best example, is like what are give the best example, is like what are give the best example, is like what are some internal states of humans that are some internal states of humans that are some internal states of humans that are really hard to imagine machines could really hard to imagine machines could really hard to imagine machines could recapitulate, but perhaps they can like recapitulate, but perhaps they can like recapitulate, but perhaps they can like motivation. Do machines, do robots get motivation. Do machines, do robots get motivation. Do machines, do robots get motivated? We have rules of motivation. motivated? We have rules of motivation. motivated? We have rules of motivation. Like when I'm really motivated to do Like when I'm really motivated to do Like when I'm really motivated to do something, we call that urgency, a state something, we call that urgency, a state something, we call that urgency, a state of urgency. And I might move faster to of urgency. And I might move faster to of urgency. And I might move faster to do it. Less activation energy. You say, do it. Less activation energy. You say, do it. Less activation energy. You say, "Let's go." I stand up a little bit "Let's go." I stand up a little bit "Let's go." I stand up a little bit faster. Machines could like go quicker faster. Machines could like go quicker faster. Machines could like go quicker in a certain direction. But can you say, in a certain direction. But can you say, in a certain direction. But can you say, "Hey, I want you to seek this out, but "Hey, I want you to seek this out, but "Hey, I want you to seek this out, but with a heightened level of urgency, or with a heightened level of urgency, or with a heightened level of urgency, or are they just constrained by the are they just constrained by the are they just constrained by the mathematical rules they can work with?" mathematical rules they can work with?" mathematical rules they can work with?" >> So you could build this in the >> So you could build this in the >> So you could build this in the mathematics. So certain things whether mathematics. So certain things whether mathematics. So certain things whether you call it motivation or in machine you call it motivation or in machine you call it motivation or in machine learning world we call them objective learning world we call them objective learning world we call them objective functions you can build certain things functions you can build certain things functions you can build certain things into math for example now you go to say into math for example now you go to say into math for example now you go to say JBT it has different mode like think JBT it has different mode like think JBT it has different mode like think deeper mode or or like give me a quick deeper mode or or like give me a quick deeper mode or or like give me a quick answer mode if you don't know how this answer mode if you don't know how this answer mode if you don't know how this works you're like oh this is interesting works you're like oh this is interesting works you're like oh this is interesting one has more urgency that gives me a one has more urgency that gives me a one has more urgency that gives me a quicker answer the other one has to go quicker answer the other one has to go quicker answer the other one has to go deeper into the search, right? And and deeper into the search, right? And and deeper into the search, right? And and take longer to give me the answer. So, take longer to give me the answer. So, take longer to give me the answer. So, as a human, if you anthropomorph as a human, if you anthropomorph as a human, if you anthropomorph anthrop morph morphalize it too much, anthrop morph morphalize it too much, anthrop morph morphalize it too much, you might call it urg urgency or you might call it urg urgency or you might call it urg urgency or motivation. But the truth is this is motivation. But the truth is this is motivation. But the truth is this is just a different kind of um objective just a different kind of um objective just a different kind of um objective for the uh algorithm. You can say, well, for the uh algorithm. You can say, well, for the uh algorithm. You can say, well, the the one that think quicker has a the the one that think quicker has a the the one that think quicker has a time limit or token limit. the one that time limit or token limit. the one that time limit or token limit. the one that thinks slower can activate a different thinks slower can activate a different thinks slower can activate a different part of the model that would take part of the model that would take part of the model that would take longer. So it become actually longer. So it become actually longer. So it become actually mathematically very dry and not that mathematically very dry and not that mathematically very dry and not that deep. But for a human you can call that deep. But for a human you can call that deep. But for a human you can call that motivation or urgency. But let's go motivation or urgency. But let's go motivation or urgency. But let's go deeper because you're asking something deeper because you're asking something deeper because you're asking something deeper than than that, right? Is that deeper than than that, right? Is that deeper than than that, right? Is that there are cognitive states that humans there are cognitive states that humans there are cognitive states that humans you truly just whether it's motivation you truly just whether it's motivation you truly just whether it's motivation or urgency or fear or love that is very or urgency or fear or love that is very or urgency or fear or love that is very hard to access and express. And do hard to access and express. And do hard to access and express. And do machines have it today? No. Let's make machines have it today? No. Let's make machines have it today? No. Let's make it very clear. we tend to imagine that it very clear. we tend to imagine that it very clear. we tend to imagine that the machines feel or or they're not they the machines feel or or they're not they the machines feel or or they're not they don't have that data they don't have don't have that data they don't have don't have that data they don't have that mathematical objective function so that mathematical objective function so that mathematical objective function so they can say when the machine says I'm they can say when the machine says I'm they can say when the machine says I'm sorry you're so sick today it's very sorry you're so sick today it's very sorry you're so sick today it's very different from how your friend says it different from how your friend says it different from how your friend says it to you because the machine said that to you because the machine said that to you because the machine said that because it has learned through pattern because it has learned through pattern because it has learned through pattern when someone tells it I'm sick you when someone tells it I'm sick you when someone tells it I'm sick you should say I'm sorry you're sick instead should say I'm sorry you're sick instead should say I'm sorry you're sick instead of I'm so glad you're sick because that of I'm so glad you're sick because that of I'm so glad you're sick because that data exists. Whereas your friend who data exists. Whereas your friend who data exists. Whereas your friend who hears that, they genuinely want your hears that, they genuinely want your hears that, they genuinely want your well-being. They love you. They want well-being. They love you. They want well-being. They love you. They want they don't want to see you suffer. They they don't want to see you suffer. They they don't want to see you suffer. They have that empathetic have that empathetic have that empathetic feel of, "Oh, wow. If you're in pain, feel of, "Oh, wow. If you're in pain, feel of, "Oh, wow. If you're in pain, I've experienced pain." So that's it's I've experienced pain." So that's it's I've experienced pain." So that's it's not mirror neuron, but it's at least a not mirror neuron, but it's at least a not mirror neuron, but it's at least a memory of what pain means. The machine memory of what pain means. The machine memory of what pain means. The machine doesn't have any of that. So we do need doesn't have any of that. So we do need doesn't have any of that. So we do need to make sure we differentiate to make sure we differentiate to make sure we differentiate uh that. So a lot of what drives human, uh that. So a lot of what drives human, uh that. So a lot of what drives human, what ticks human, what triggers human is what ticks human, what triggers human is what ticks human, what triggers human is doesn't exist in today's machine. We doesn't exist in today's machine. We doesn't exist in today's machine. We operate fundamentally different from operate fundamentally different from operate fundamentally different from today's AI and we have to recognize that today's AI and we have to recognize that today's AI and we have to recognize that respect that and this is where public respect that and this is where public respect that and this is where public communication is so important. We cannot communication is so important. We cannot communication is so important. We cannot confuse the public about this. confuse the public about this. confuse the public about this. I'd like to take a quick break to I'd like to take a quick break to I'd like to take a quick break to acknowledge one of our sponsors, David. acknowledge one of our sponsors, David. acknowledge one of our sponsors, David. David makes protein bars unlike any David makes protein bars unlike any David makes protein bars unlike any other. Their newest bar, the Bronze Bar, other. Their newest bar, the Bronze Bar, other. Their newest bar, the Bronze Bar, has 20 gram of protein, only 150 has 20 gram of protein, only 150 has 20 gram of protein, only 150 calories, and zero gram of sugar. I have calories, and zero gram of sugar. I have calories, and zero gram of sugar. I have to say, these are the best tasting to say, these are the best tasting to say, these are the best tasting protein bars I've ever had, and I've protein bars I've ever had, and I've protein bars I've ever had, and I've tried a lot of protein bars over the tried a lot of protein bars over the tried a lot of protein bars over the years. These New David bars have a years. These New David bars have a years. These New David bars have a marshmallow base, and they're covered in marshmallow base, and they're covered in marshmallow base, and they're covered in chocolate coating, and they're chocolate coating, and they're chocolate coating, and they're absolutely incredible. I of course eat absolutely incredible. I of course eat absolutely incredible. I of course eat regular whole foods. I eat meat, regular whole foods. I eat meat, regular whole foods. I eat meat, chicken, fish, eggs, fruits, vegetables, chicken, fish, eggs, fruits, vegetables, chicken, fish, eggs, fruits, vegetables, etc. But I also make it a point to eat etc. But I also make it a point to eat etc. But I also make it a point to eat one or two David bars per day as a one or two David bars per day as a one or two David bars per day as a snack, which makes it easy to hit my snack, which makes it easy to hit my snack, which makes it easy to hit my protein goal of one gram of protein per protein goal of one gram of protein per protein goal of one gram of protein per pound of body weight. And that allows me pound of body weight. And that allows me pound of body weight. And that allows me to take in the protein I need without to take in the protein I need without to take in the protein I need without consuming excess calories. I love all consuming excess calories. I love all consuming excess calories. I love all the David Bronze Bar flavors, including the David Bronze Bar flavors, including the David Bronze Bar flavors, including cookie dough, caramel chocolate, double cookie dough, caramel chocolate, double cookie dough, caramel chocolate, double chocolate, peanut butter chocolate. They chocolate, peanut butter chocolate. They chocolate, peanut butter chocolate. They all actually taste like candy bars. all actually taste like candy bars. all actually taste like candy bars. Again, they're amazing. But again, they Again, they're amazing. But again, they Again, they're amazing. But again, they have no sugar and they have 20 grams of have no sugar and they have 20 grams of have no sugar and they have 20 grams of protein with just 150 calories. If you'd protein with just 150 calories. If you'd protein with just 150 calories. If you'd like to try David, you can go to like to try David, you can go to like to try David, you can go to davidprotein.com/huberman. davidprotein.com/huberman. davidprotein.com/huberman. Right now, David is offering a deal Right now, David is offering a deal Right now, David is offering a deal where if you buy four cartons, you get where if you buy four cartons, you get where if you buy four cartons, you get the fifth carton for free. You can also the fifth carton for free. You can also the fifth carton for free. You can also find David on Amazon or in stores such find David on Amazon or in stores such find David on Amazon or in stores such as Target, Walmart, and Kroger. Again, as Target, Walmart, and Kroger. Again, as Target, Walmart, and Kroger. Again, to get the fifth carton for free, go to to get the fifth carton for free, go to to get the fifth carton for free, go to davidproin.com/huberman. I feel like people assume there's an I feel like people assume there's an emotion, a person or whatever inside of emotion, a person or whatever inside of emotion, a person or whatever inside of the AI chatbot because we're so language the AI chatbot because we're so language the AI chatbot because we're so language oriented. It's talking to us. It's oriented. It's talking to us. It's oriented. It's talking to us. It's writing things to me. And we do that writing things to me. And we do that writing things to me. And we do that more now than we did 30 years ago. Yeah. more now than we did 30 years ago. Yeah. more now than we did 30 years ago. Yeah. >> Certainly, we've gotten very accustomed >> Certainly, we've gotten very accustomed >> Certainly, we've gotten very accustomed to receiving communications in fairly to receiving communications in fairly to receiving communications in fairly deprived language. Texts are not like deprived language. Texts are not like deprived language. Texts are not like extensive pros. Language has changed. extensive pros. Language has changed. extensive pros. Language has changed. Modes of communication have changed. Modes of communication have changed. Modes of communication have changed. more deprived as opposed to more more deprived as opposed to more more deprived as opposed to more enriched. enriched. enriched. >> Yeah. >> Yeah. >> Yeah. >> But at some point soon, I'm guessing >> But at some point soon, I'm guessing >> But at some point soon, I'm guessing faces are going to start to enter the faces are going to start to enter the faces are going to start to enter the picture. picture. picture. >> Uh no pun intended. Um like how far off >> Uh no pun intended. Um like how far off >> Uh no pun intended. Um like how far off are we from? Like if you or I were to are we from? Like if you or I were to are we from? Like if you or I were to text the other person, oh uh see you on text the other person, oh uh see you on text the other person, oh uh see you on campus for coffee next week at this campus for coffee next week at this campus for coffee next week at this time. time. time. >> How soon is it that that text is going >> How soon is it that that text is going >> How soon is it that that text is going to be actually a photo or video like to be actually a photo or video like to be actually a photo or video like image of you just talking to me telling image of you just talking to me telling image of you just talking to me telling me that? I mean, this would be trivial me that? I mean, this would be trivial me that? I mean, this would be trivial to do nowadays. to do nowadays. to do nowadays. >> The technology is there. Mhm. >> The technology is there. Mhm. >> The technology is there. Mhm. >> But we have to now look zoom out a >> But we have to now look zoom out a >> But we have to now look zoom out a little bit and look think about the little bit and look think about the little bit and look think about the social parameters, the legal social parameters, the legal social parameters, the legal implications. I mean, humans are capable implications. I mean, humans are capable implications. I mean, humans are capable of doing a lot of things with our tools, of doing a lot of things with our tools, of doing a lot of things with our tools, but we don't do all of them. For but we don't do all of them. For but we don't do all of them. For example, today any car manufacturer can example, today any car manufacturer can example, today any car manufacturer can say every Friday the brake doesn't work. say every Friday the brake doesn't work. say every Friday the brake doesn't work. This is a trivial technology. There's a This is a trivial technology. There's a This is a trivial technology. There's a clock in the car's computer and it just clock in the car's computer and it just clock in the car's computer and it just turns off the brake every Friday. But we turns off the brake every Friday. But we turns off the brake every Friday. But we don't do that because it has deeply bad don't do that because it has deeply bad don't do that because it has deeply bad implications to our human society. implications to our human society. implications to our human society. That's where rules comes in, laws come That's where rules comes in, laws come That's where rules comes in, laws come in, social norm comes in, morality comes in, social norm comes in, morality comes in, social norm comes in, morality comes in and I think this is where we exit the in and I think this is where we exit the in and I think this is where we exit the pure technical discussion of AI and need pure technical discussion of AI and need pure technical discussion of AI and need to enter the social discussion of AI. to enter the social discussion of AI. to enter the social discussion of AI. Mhm. Well, let's do that because one Mhm. Well, let's do that because one Mhm. Well, let's do that because one thing that I know about biologists or thing that I know about biologists or thing that I know about biologists or technologists is they like to go fast technologists is they like to go fast technologists is they like to go fast cuz it it's exciting. It's the next cuz it it's exciting. It's the next cuz it it's exciting. It's the next edge, right? I remember long ago I had a edge, right? I remember long ago I had a edge, right? I remember long ago I had a friend he was studying viruses and ways friend he was studying viruses and ways friend he was studying viruses and ways of putting uh these weren't infectious of putting uh these weren't infectious of putting uh these weren't infectious disease viruses. These were viral disease viruses. These were viral disease viruses. These were viral vectors for getting genes expressed as vectors for getting genes expressed as vectors for getting genes expressed as experimental tools in animals. But there experimental tools in animals. But there experimental tools in animals. But there came the opportunity to actually put the came the opportunity to actually put the came the opportunity to actually put the rabies virus, a modified rabies virus rabies virus, a modified rabies virus rabies virus, a modified rabies virus into Drosophila, into fruit flies. into Drosophila, into fruit flies. into Drosophila, into fruit flies. >> Oh my god. >> Oh my god. >> Oh my god. >> Now, that's fine and good in my opinion >> Now, that's fine and good in my opinion >> Now, that's fine and good in my opinion if you are absolutely certain, 100% if you are absolutely certain, 100% if you are absolutely certain, 100% certainty that that is a nonfunctional certainty that that is a nonfunctional certainty that that is a nonfunctional version of the rabies virus because you version of the rabies virus because you version of the rabies virus because you can put other cargo in there and do all can put other cargo in there and do all can put other cargo in there and do all sorts of important experiments on, sorts of important experiments on, sorts of important experiments on, believe it or not, disease and things believe it or not, disease and things believe it or not, disease and things like that. like that. like that. >> But if there's just one fruitly that >> But if there's just one fruitly that >> But if there's just one fruitly that somehow an escaper and you get the somehow an escaper and you get the somehow an escaper and you get the actual rabies virus. actual rabies virus. actual rabies virus. >> There's the potential it mates with >> There's the potential it mates with >> There's the potential it mates with another and then they eventually find another and then they eventually find another and then they eventually find the others. I don't know if this would the others. I don't know if this would the others. I don't know if this would be a dominant or recessive situation, be a dominant or recessive situation, be a dominant or recessive situation, but but but >> now you have fruit flies with rabies and >> now you have fruit flies with rabies and >> now you have fruit flies with rabies and those things move really fast. So, those things move really fast. So, those things move really fast. So, there's a reason why you don't do that there's a reason why you don't do that there's a reason why you don't do that experiment. experiment. experiment. >> But it was exciting for them to think >> But it was exciting for them to think >> But it was exciting for them to think about and then they got denied, right? about and then they got denied, right? about and then they got denied, right? For good reason. I was grateful, right? For good reason. I was grateful, right? For good reason. I was grateful, right? Go to any biology department, you're Go to any biology department, you're Go to any biology department, you're going to see some fruit flies flying going to see some fruit flies flying going to see some fruit flies flying around. They love vinegar, by the way. around. They love vinegar, by the way. around. They love vinegar, by the way. you know, so they're coming to your you know, so they're coming to your you know, so they're coming to your salad. But the point here is that salad. But the point here is that salad. But the point here is that technologists love to go fast. They love technologists love to go fast. They love technologists love to go fast. They love sensing that next edge of things. So how sensing that next edge of things. So how sensing that next edge of things. So how is it that is it that is it that >> between government, the general public, >> between government, the general public, >> between government, the general public, technologists, and now I'm just leaving technologists, and now I'm just leaving technologists, and now I'm just leaving out biology here and medicine. How is it out biology here and medicine. How is it out biology here and medicine. How is it that that conversation can occur in a that that conversation can occur in a that that conversation can occur in a way that's going to satisfy each of way that's going to satisfy each of way that's going to satisfy each of those groups enough, not hold us back? those groups enough, not hold us back? those groups enough, not hold us back? Because we're also supposedly in an AI Because we're also supposedly in an AI Because we're also supposedly in an AI race right now. So that that warrants race right now. So that that warrants race right now. So that that warrants going faster, not slower. How do you going faster, not slower. How do you going faster, not slower. How do you think about this? think about this? think about this? >> I mean, Andrew, this is why I returned >> I mean, Andrew, this is why I returned >> I mean, Andrew, this is why I returned from Google um eight years ago back to from Google um eight years ago back to from Google um eight years ago back to Stanford and started the human center AI Stanford and started the human center AI Stanford and started the human center AI institute. These are profound societal institute. These are profound societal institute. These are profound societal questions we had to face. And back in questions we had to face. And back in questions we had to face. And back in 2018, there was no Chad GPT. But as a AI 2018, there was no Chad GPT. But as a AI 2018, there was no Chad GPT. But as a AI scientist, I knew that this is only scientist, I knew that this is only scientist, I knew that this is only going to accelerate. This is why I went going to accelerate. This is why I went going to accelerate. This is why I went to my colleagues and university to my colleagues and university to my colleagues and university leadership and say let's put a framework leadership and say let's put a framework leadership and say let's put a framework but it's not just my framework or but it's not just my framework or but it's not just my framework or Stanford's framework. The entire society Stanford's framework. The entire society Stanford's framework. The entire society in every way need to wake up to the in every way need to wake up to the in every way need to wake up to the social implication as we have done this social implication as we have done this social implication as we have done this in h human history whether it was cars in h human history whether it was cars in h human history whether it was cars or airplanes or or or biotech is that or airplanes or or or biotech is that or airplanes or or or biotech is that it's multi-dimensional with it's multi-dimensional with it's multi-dimensional with multistakeholders right there is the multistakeholders right there is the multistakeholders right there is the professional norm for example professional norm for example professional norm for example you guys as biologists don't sneak into you guys as biologists don't sneak into you guys as biologists don't sneak into the lab and and try to put rabies into the lab and and try to put rabies into the lab and and try to put rabies into drosophilas or fruit flies because drosophilas or fruit flies because drosophilas or fruit flies because that's a professional norm and your that's a professional norm and your that's a professional norm and your ethical training. There is industry uh ethical training. There is industry uh ethical training. There is industry uh rules for example IRBs every uh human rules for example IRBs every uh human rules for example IRBs every uh human subject experiment today on university subject experiment today on university subject experiment today on university campuses are subject to the IRB campuses are subject to the IRB campuses are subject to the IRB regulatory framework so that we can look regulatory framework so that we can look regulatory framework so that we can look at this and then there are uh laws and at this and then there are uh laws and at this and then there are uh laws and regulatory laws depending on if it's regulatory laws depending on if it's regulatory laws depending on if it's applied to humans versus applied to humans versus applied to humans versus uh crops or or you know so AI has to go uh crops or or you know so AI has to go uh crops or or you know so AI has to go through the same right we need to have through the same right we need to have through the same right we need to have our professional norms we need to have our professional norms we need to have our professional norms we need to have education computer scientists are not education computer scientists are not education computer scientists are not educated in ethics and societal studies educated in ethics and societal studies educated in ethics and societal studies you know they're starting to I mean this you know they're starting to I mean this you know they're starting to I mean this is why a number of universities is why a number of universities is why a number of universities including Stanford are feverishly including Stanford are feverishly including Stanford are feverishly putting that part of curriculum into our putting that part of curriculum into our putting that part of curriculum into our education now that those are the norms education now that those are the norms education now that those are the norms and education but we also should work and education but we also should work and education but we also should work with the government and different kind with the government and different kind with the government and different kind of Governments and society have of Governments and society have of Governments and society have different kind of norms and traditions different kind of norms and traditions different kind of norms and traditions and heritage and look at where the and heritage and look at where the and heritage and look at where the regulatory measure should apply AI for regulatory measure should apply AI for regulatory measure should apply AI for example crossing biology FDA I think example crossing biology FDA I think example crossing biology FDA I think that's a very important area to look at that's a very important area to look at that's a very important area to look at how AI uh should be used to help but how AI uh should be used to help but how AI uh should be used to help but also guard rail to harm uh so that we also guard rail to harm uh so that we also guard rail to harm uh so that we can avoid harm. What I would not like to can avoid harm. What I would not like to can avoid harm. What I would not like to see is one person or or a few people see is one person or or a few people see is one person or or a few people coming from industry and telling coming from industry and telling coming from industry and telling everybody what to do. I think that would everybody what to do. I think that would everybody what to do. I think that would be dangerous because market forces are be dangerous because market forces are be dangerous because market forces are different from uh societal norms and different from uh societal norms and different from uh societal norms and culture and heritage are different from culture and heritage are different from culture and heritage are different from uh education and ethics and and these uh education and ethics and and these uh education and ethics and and these are multistakeholder are multistakeholder are multistakeholder problems to solve together. problems to solve together. problems to solve together. >> I love that answer and it's something >> I love that answer and it's something >> I love that answer and it's something that's very very timely right now. Um, that's very very timely right now. Um, that's very very timely right now. Um, this aspect of our conversation is this aspect of our conversation is this aspect of our conversation is surely going to expand over time, but surely going to expand over time, but surely going to expand over time, but you bullseyed it. I'd like to get your you bullseyed it. I'd like to get your you bullseyed it. I'd like to get your thoughts on how the human brain is being thoughts on how the human brain is being thoughts on how the human brain is being shaped on machines and how machines are shaped on machines and how machines are shaped on machines and how machines are being shaped by our understanding of the being shaped by our understanding of the being shaped by our understanding of the human brain. So, first question first. human brain. So, first question first. human brain. So, first question first. Many people, parents and kids are Many people, parents and kids are Many people, parents and kids are thinking, oh, like my kid is never going thinking, oh, like my kid is never going thinking, oh, like my kid is never going to learn anything now. They're just to learn anything now. They're just to learn anything now. They're just going to look everything up on a going to look everything up on a going to look everything up on a chatbot. But if you look back in the chatbot. But if you look back in the chatbot. But if you look back in the history of learning, similar arguments history of learning, similar arguments history of learning, similar arguments were made about calculators um and were made about calculators um and were made about calculators um and computers and the typewriter and on and computers and the typewriter and on and computers and the typewriter and on and on. However, it is an interesting on. However, it is an interesting on. However, it is an interesting question that this hardware that we have question that this hardware that we have question that this hardware that we have in our heads evolved to process physical in our heads evolved to process physical in our heads evolved to process physical things in the world, light, sound, it things in the world, light, sound, it things in the world, light, sound, it smells, etc. And then it got this really smells, etc. And then it got this really smells, etc. And then it got this really cool piece up front, the prefrontal cool piece up front, the prefrontal cool piece up front, the prefrontal cortex that can learn learning rules and cortex that can learn learning rules and cortex that can learn learning rules and can update those learning rules. So like can update those learning rules. So like can update those learning rules. So like if anything we were gifted with a a if anything we were gifted with a a if anything we were gifted with a a learning tolearn machine and updating learning tolearn machine and updating learning tolearn machine and updating learning. So that's how kids can adjust learning. So that's how kids can adjust learning. So that's how kids can adjust and use LLMs. So I as a generation that and use LLMs. So I as a generation that and use LLMs. So I as a generation that grew up with the personal computer grew up with the personal computer grew up with the personal computer showed up. Granted I grew up in Palo showed up. Granted I grew up in Palo showed up. Granted I grew up in Palo Alto. It was like here's Pong and Alto. It was like here's Pong and Alto. It was like here's Pong and there's the Apple 2e and like we had and there's the Apple 2e and like we had and there's the Apple 2e and like we had and I think oh cool like the brain can I think oh cool like the brain can I think oh cool like the brain can mature around technology collaborate mature around technology collaborate mature around technology collaborate with technology in a way that I think my with technology in a way that I think my with technology in a way that I think my life has been greatly enriched by it. life has been greatly enriched by it. life has been greatly enriched by it. But I think the smartphone and perhaps But I think the smartphone and perhaps But I think the smartphone and perhaps the camera smartphone combination as the camera smartphone combination as the camera smartphone combination as people like Jonathan hate have pointed people like Jonathan hate have pointed people like Jonathan hate have pointed out out out have created a situation where most have created a situation where most have created a situation where most people like they love these technologies people like they love these technologies people like they love these technologies for the ease and convenience. for the ease and convenience. for the ease and convenience. >> But we're all a little bit more aware >> But we're all a little bit more aware >> But we're all a little bit more aware now or a lot more aware that we're now or a lot more aware that we're now or a lot more aware that we're giving up something too. Yeah. giving up something too. Yeah. giving up something too. Yeah. >> And that they're traps that people in >> And that they're traps that people in >> And that they're traps that people in particular young people can fall down. particular young people can fall down. particular young people can fall down. >> Yeah. So what is the very optimistic meh >> Yeah. So what is the very optimistic meh >> Yeah. So what is the very optimistic meh and very pessimistic view in your in and very pessimistic view in your in and very pessimistic view in your in your mind if three if three flavors your mind if three if three flavors your mind if three if three flavors actually exist there of how young brains actually exist there of how young brains actually exist there of how young brains can be enriched are unaffected or can be can be enriched are unaffected or can be can be enriched are unaffected or can be uh harmed by AI as it exists now. Let's uh harmed by AI as it exists now. Let's uh harmed by AI as it exists now. Let's just kind of stay with what we've got. just kind of stay with what we've got. just kind of stay with what we've got. Great question, Andrew. And the answer Great question, Andrew. And the answer Great question, Andrew. And the answer almost fall out of our previous almost fall out of our previous almost fall out of our previous conversations because you use the word conversations because you use the word conversations because you use the word motivation and I was using the word motivation and I was using the word motivation and I was using the word agency. The absolute agency. The absolute agency. The absolute bad outcome is that our young bad outcome is that our young bad outcome is that our young generation, their agency their agency and human level motivation of learning and human level motivation of learning and human level motivation of learning and living is taken away by tools. and living is taken away by tools. and living is taken away by tools. So doom scrolling, So doom scrolling, So doom scrolling, passive watching of shorts, all this are passive watching of shorts, all this are passive watching of shorts, all this are not helping not helping not helping agency, human agency. Learning agency, human agency. Learning agency, human agency. Learning fundamentally respecting the hardware fundamentally respecting the hardware fundamentally respecting the hardware you're talking about takes time, takes you're talking about takes time, takes you're talking about takes time, takes effort, sometimes takes some pain. That effort, sometimes takes some pain. That effort, sometimes takes some pain. That is just how our brain is. It doesn't is just how our brain is. It doesn't is just how our brain is. It doesn't matter how transistors move, our neurons matter how transistors move, our neurons matter how transistors move, our neurons move in certain ways, our chemistry, our move in certain ways, our chemistry, our move in certain ways, our chemistry, our hormones move in certain way. So for hormones move in certain way. So for hormones move in certain way. So for young generation, no matter how the young generation, no matter how the young generation, no matter how the society will be different, jobs will be society will be different, jobs will be society will be different, jobs will be different, our human body needs to go different, our human body needs to go different, our human body needs to go through a deeply developmental phase through a deeply developmental phase through a deeply developmental phase where learning needs to happen. And that where learning needs to happen. And that where learning needs to happen. And that agency of learning that motivation of agency of learning that motivation of agency of learning that motivation of learning cannot be taken away by anybody learning cannot be taken away by anybody learning cannot be taken away by anybody should not be taken away by humans nor should not be taken away by humans nor should not be taken away by humans nor should it be taken away by machines. should it be taken away by machines. should it be taken away by machines. That would be my concern which is that That would be my concern which is that That would be my concern which is that if AI is not used right the agency and if AI is not used right the agency and if AI is not used right the agency and motivation is taken away then we are motivation is taken away then we are motivation is taken away then we are left with generations or generations to left with generations or generations to left with generations or generations to come who have not properly developed the come who have not properly developed the come who have not properly developed the brick. The other kind of danger is in brick. The other kind of danger is in brick. The other kind of danger is in the name of agency and and uh and the name of agency and and uh and the name of agency and and uh and motivation the tools are denied to our motivation the tools are denied to our motivation the tools are denied to our students because we're worried you cheat students because we're worried you cheat students because we're worried you cheat or worry you only got your answer from or worry you only got your answer from or worry you only got your answer from Chad GBT. That is very bad as well Chad GBT. That is very bad as well Chad GBT. That is very bad as well because with the proper agency, proper because with the proper agency, proper because with the proper agency, proper motivation, proper ways of using this motivation, proper ways of using this motivation, proper ways of using this tool, we can go a lot deeper with AI tool, we can go a lot deeper with AI tool, we can go a lot deeper with AI than we have ever learned. I I was just than we have ever learned. I I was just than we have ever learned. I I was just thinking about I was a premed student thinking about I was a premed student thinking about I was a premed student for for a while. Man, organic chemistry for for a while. Man, organic chemistry for for a while. Man, organic chemistry was hard, you know. I remembered trying was hard, you know. I remembered trying was hard, you know. I remembered trying to learn the the molecules, their to learn the the molecules, their to learn the the molecules, their orientations, orientations, orientations, but the TA hours are too short or it but the TA hours are too short or it but the TA hours are too short or it overlaps with my other class and my overlaps with my other class and my overlaps with my other class and my professors only have certain number of professors only have certain number of professors only have certain number of office hours. It was just a struggle to office hours. It was just a struggle to office hours. It was just a struggle to learn that. Right? If today I were to learn that. Right? If today I were to learn that. Right? If today I were to have a AI companion, I would ask so many have a AI companion, I would ask so many have a AI companion, I would ask so many questions about organic chemistry questions about organic chemistry questions about organic chemistry because I know what where I'm stuck, because I know what where I'm stuck, because I know what where I'm stuck, right? I have the motivation to learn. I right? I have the motivation to learn. I right? I have the motivation to learn. I just need to uh guidance. That would be just need to uh guidance. That would be just need to uh guidance. That would be such a powerful tool for me to learn. So such a powerful tool for me to learn. So such a powerful tool for me to learn. So that we should not deny students from. that we should not deny students from. that we should not deny students from. So both things worry me is either So both things worry me is either So both things worry me is either denying the tool or taking away agency denying the tool or taking away agency denying the tool or taking away agency and motivation. Of course, the flip side and motivation. Of course, the flip side and motivation. Of course, the flip side is is great is let's find a way to keep is is great is let's find a way to keep is is great is let's find a way to keep our children and students motivation and our children and students motivation and our children and students motivation and agency. Let's find a way to give them agency. Let's find a way to give them agency. Let's find a way to give them the access and the right way of using the access and the right way of using the access and the right way of using these tools. Then this generation, this these tools. Then this generation, this these tools. Then this generation, this coming generation and many generations coming generation and many generations coming generation and many generations to come will be way smarter than us to come will be way smarter than us to come will be way smarter than us because they are superpowered. because they are superpowered. because they are superpowered. >> I love that answer. Um I have great >> I love that answer. Um I have great >> I love that answer. Um I have great faith in neuroplasticity and the younger faith in neuroplasticity and the younger faith in neuroplasticity and the younger generations too. Yeah, even our own I generations too. Yeah, even our own I generations too. Yeah, even our own I know we're old [laughter] but know we're old [laughter] but know we're old [laughter] but >> not so let's give ourselves some credit >> not so let's give ourselves some credit >> not so let's give ourselves some credit plasticity does exist throughout the plasticity does exist throughout the plasticity does exist throughout the lifan lifan lifan >> even our own neurop plasticity right >> even our own neurop plasticity right >> even our own neurop plasticity right like I I find AI a great tool for my like I I find AI a great tool for my like I I find AI a great tool for my learning learning learning >> I mean for me it's been a remarkable >> I mean for me it's been a remarkable >> I mean for me it's been a remarkable discovery of what it can do discovery of what it can do discovery of what it can do >> but I I tend to approach it from the >> but I I tend to approach it from the >> but I I tend to approach it from the position of consumer if I know nothing position of consumer if I know nothing position of consumer if I know nothing about something and from the position of about something and from the position of about something and from the position of creator if I have some creator if I have some creator if I have some >> uh knowledge set >> uh knowledge set >> uh knowledge set >> inside of whatever it is I'm asking. >> inside of whatever it is I'm asking. >> inside of whatever it is I'm asking. >> Well, I actually have another thing >> Well, I actually have another thing >> Well, I actually have another thing because Stefer undergrad taught me because Stefer undergrad taught me because Stefer undergrad taught me something last year and I realized something last year and I realized something last year and I realized before Chad GPT sometimes I got lazy. I before Chad GPT sometimes I got lazy. I before Chad GPT sometimes I got lazy. I if I have a question I ask the person I if I have a question I ask the person I if I have a question I ask the person I think is smart next to me. Now I realize think is smart next to me. Now I realize think is smart next to me. Now I realize I should not ask lazy questions because I should not ask lazy questions because I should not ask lazy questions because it's so much easier to get information it's so much easier to get information it's so much easier to get information before you spend somebody else's time to before you spend somebody else's time to before you spend somebody else's time to ask something that's that's that's too ask something that's that's that's too ask something that's that's that's too lazy. And AI is forcing me not to be too lazy. And AI is forcing me not to be too lazy. And AI is forcing me not to be too lazy. lazy. lazy. >> How essential is the specificity of the >> How essential is the specificity of the >> How essential is the specificity of the prompt to getting the best information prompt to getting the best information prompt to getting the best information out of AI? out of AI? out of AI? >> Prompting is very important. >> Prompting is very important. >> Prompting is very important. >> And that's a skill, right? >> And that's a skill, right? >> And that's a skill, right? >> That is a skill. This is why public >> That is a skill. This is why public >> That is a skill. This is why public education is so important. This is why education is so important. This is why education is so important. This is why education is so important. I would love education is so important. I would love education is so important. I would love to see our schools K12 to see our schools K12 to see our schools K12 teaching prompting. I here's a quiz. Who teaching prompting. I here's a quiz. Who teaching prompting. I here's a quiz. Who is humanity's best prompter? is humanity's best prompter? is humanity's best prompter? >> I'm going to flunk this quiz. >> I'm going to flunk this quiz. >> I'm going to flunk this quiz. >> Socrates if he were alive >> Socrates if he were alive >> Socrates if he were alive >> because that is the method of prompting. >> because that is the method of prompting. >> because that is the method of prompting. Right? Think about it. What is Socrates Right? Think about it. What is Socrates Right? Think about it. What is Socrates method is prompting and seeking truth by method is prompting and seeking truth by method is prompting and seeking truth by asking questions. asking questions. asking questions. And we should go back and teaching kids And we should go back and teaching kids And we should go back and teaching kids that that that >> and taking a walk while you have those >> and taking a walk while you have those >> and taking a walk while you have those discussions. discussions. discussions. >> Yes. >> Yes. >> Yes. >> Which actually is a a good transition >> Which actually is a a good transition >> Which actually is a a good transition perhaps to this notion of embodied AI. perhaps to this notion of embodied AI. perhaps to this notion of embodied AI. You know, it's a world apart to attach a You know, it's a world apart to attach a You know, it's a world apart to attach a face speaking to hearing words. Uh my face speaking to hearing words. Uh my face speaking to hearing words. Uh my good childhood friend um who I hope good childhood friend um who I hope good childhood friend um who I hope you'll meet soon because you both would you'll meet soon because you both would you'll meet soon because you both would benefit from the conversation so much benefit from the conversation so much benefit from the conversation so much and I just want to be a fly on the wall. and I just want to be a fly on the wall. and I just want to be a fly on the wall. Um Dr. Dr. Eddie Changeng, chair of Um Dr. Dr. Eddie Changeng, chair of Um Dr. Dr. Eddie Changeng, chair of neurosurgery, bioengineer, and he neurosurgery, bioengineer, and he neurosurgery, bioengineer, and he studies speech and language. He and studies speech and language. He and studies speech and language. He and others have figured out the others have figured out the others have figured out the transformation of neural activity to transformation of neural activity to transformation of neural activity to control of the larynx and ferings. And control of the larynx and ferings. And control of the larynx and ferings. And he's brought people essentially out of he's brought people essentially out of he's brought people essentially out of lockedin syndrome so they can speak. lockedin syndrome so they can speak. lockedin syndrome so they can speak. >> Wow. >> Wow. >> Wow. >> For the first time in 10 years, he has >> For the first time in 10 years, he has >> For the first time in 10 years, he has this patient who was sadly paralyzed and this patient who was sadly paralyzed and this patient who was sadly paralyzed and he could speak through a computer. He he could speak through a computer. He he could speak through a computer. He has others, many examples of these in has others, many examples of these in has others, many examples of these in fact. But the incredible thing is when fact. But the incredible thing is when fact. But the incredible thing is when he started putting an iPad next to this he started putting an iPad next to this he started putting an iPad next to this person who is uh one woman in particular person who is uh one woman in particular person who is uh one woman in particular who's wheelchair bound, they had a video who's wheelchair bound, they had a video who's wheelchair bound, they had a video of her at her wedding. So they knew her of her at her wedding. So they knew her of her at her wedding. So they knew her voice. They knew her emotive patterns. voice. They knew her emotive patterns. voice. They knew her emotive patterns. They knew a bit about how she moved her They knew a bit about how she moved her They knew a bit about how she moved her body as well. And she now speaks through body as well. And she now speaks through body as well. And she now speaks through an iPad next to her frozen real face. M an iPad next to her frozen real face. M an iPad next to her frozen real face. M >> but she can interact with the world and >> but she can interact with the world and >> but she can interact with the world and it can interact with her in a completely it can interact with her in a completely it can interact with her in a completely different level of depth different level of depth different level of depth >> than if it were just a microphone. The >> than if it were just a microphone. The >> than if it were just a microphone. The sort of Stephen Hawking thing sort of Stephen Hawking thing sort of Stephen Hawking thing >> and it's constantly being updated >> and it's constantly being updated >> and it's constantly being updated through machine learning. through machine learning. through machine learning. >> What and now also paying attention to >> What and now also paying attention to >> What and now also paying attention to the people she's speaking to and their the people she's speaking to and their the people she's speaking to and their responses. I mean this is responses. I mean this is responses. I mean this is >> this is embodiment. Yes, >> this is embodiment. Yes, >> this is embodiment. Yes, >> it's on a 2D flat 2D screen >> it's on a 2D flat 2D screen >> it's on a 2D flat 2D screen >> admittedly, but this is like a >> admittedly, but this is like a >> admittedly, but this is like a exponential leap over just robot sound exponential leap over just robot sound exponential leap over just robot sound or even accurate sound alone. or even accurate sound alone. or even accurate sound alone. >> It's not just embodiment of people, it's >> It's not just embodiment of people, it's >> It's not just embodiment of people, it's embodiment also embodied AI goes into embodiment also embodied AI goes into embodiment also embodied AI goes into robotics. Right? The next frontier of AI robotics. Right? The next frontier of AI robotics. Right? The next frontier of AI as I have been saying is beyond language as I have been saying is beyond language as I have been saying is beyond language because again humans develop first because again humans develop first because again humans develop first preverbally. preverbally. preverbally. evolution took, evolution took, evolution took, you know, 500 million years without you know, 500 million years without you know, 500 million years without verbal communication verbal communication verbal communication and uh and also the world would in the and uh and also the world would in the and uh and also the world would in the right version would be a lot better right version would be a lot better right version would be a lot better place with robots helping humans. place with robots helping humans. place with robots helping humans. >> Could you give me some examples? I love >> Could you give me some examples? I love >> Could you give me some examples? I love this idea, but again, I'm I realize I'm this idea, but again, I'm I realize I'm this idea, but again, I'm I realize I'm probably a little too deep into the probably a little too deep into the probably a little too deep into the technology rabbit hole and it's probably technology rabbit hole and it's probably technology rabbit hole and it's probably scaring some people. So, robots, we've scaring some people. So, robots, we've scaring some people. So, robots, we've got self-driving cars. Actually, the got self-driving cars. Actually, the got self-driving cars. Actually, the Whimo always stops for me and my puppy. Whimo always stops for me and my puppy. Whimo always stops for me and my puppy. My beautiful little six-month old puppy. My beautiful little six-month old puppy. My beautiful little six-month old puppy. How could you not stop when he wants to How could you not stop when he wants to How could you not stop when he wants to cross the street? A lot of people won't cross the street? A lot of people won't cross the street? A lot of people won't stop. They'll almost run us over in the stop. They'll almost run us over in the stop. They'll almost run us over in the morning. The Whimo is very respectful. morning. The Whimo is very respectful. morning. The Whimo is very respectful. >> The Whimo has to learn the rules, right? >> The Whimo has to learn the rules, right? >> The Whimo has to learn the rules, right? >> Exactly. Exactly. So, there's >> Exactly. Exactly. So, there's >> Exactly. Exactly. So, there's benevolence there that doesn't always benevolence there that doesn't always benevolence there that doesn't always exist in humans, but um where do you exist in humans, but um where do you exist in humans, but um where do you think this is going to show up first? think this is going to show up first? think this is going to show up first? And what's it going to look like if we And what's it going to look like if we And what's it going to look like if we zoom out 12 months from now? 12 months zoom out 12 months from now? 12 months zoom out 12 months from now? 12 months is a little bit too fast for robotics. is a little bit too fast for robotics. is a little bit too fast for robotics. >> Two years. Three years. >> Two years. Three years. >> Two years. Three years. >> I would say if we zoom out 30 years. >> I would say if we zoom out 30 years. >> I would say if we zoom out 30 years. >> 30 years. >> 30 years. >> 30 years. >> Okay. I'm not saying that's the first >> Okay. I'm not saying that's the first >> Okay. I'm not saying that's the first time it robots hit the street. We time it robots hit the street. We time it robots hit the street. We already have robotic uh cars. I'm just already have robotic uh cars. I'm just already have robotic uh cars. I'm just saying it takes longer for especially a saying it takes longer for especially a saying it takes longer for especially a hardware also involved technology to hardware also involved technology to hardware also involved technology to manifest. But I would say hopefully in manifest. But I would say hopefully in manifest. But I would say hopefully in you and my lifetime, I would love to see you and my lifetime, I would love to see you and my lifetime, I would love to see robots being part of robots being part of robots being part of our society, helping us. For example, our society, helping us. For example, our society, helping us. For example, I'm a single grown-up child taking care I'm a single grown-up child taking care I'm a single grown-up child taking care of two very advanced aged and very sick of two very advanced aged and very sick of two very advanced aged and very sick parents and they happen not to speak parents and they happen not to speak parents and they happen not to speak English either. The amount of work I do English either. The amount of work I do English either. The amount of work I do is incredible, right? So I would love to is incredible, right? So I would love to is incredible, right? So I would love to have have help. It doesn't take away have have help. It doesn't take away have have help. It doesn't take away family's responsibility. It doesn't take family's responsibility. It doesn't take family's responsibility. It doesn't take away love. It doesn't take away the away love. It doesn't take away the away love. It doesn't take away the necessary communication. necessary communication. necessary communication. But the physical labor would really But the physical labor would really But the physical labor would really certain part I would love to get help. certain part I would love to get help. certain part I would love to get help. We live in the state of California. What We live in the state of California. What We live in the state of California. What is the one thing we all experience? is the one thing we all experience? is the one thing we all experience? >> Traffic. [laughter] >> Traffic. [laughter] >> Traffic. [laughter] >> High taxes. Certain part of certain part >> High taxes. Certain part of certain part >> High taxes. Certain part of certain part of California doesn't have traffic but of California doesn't have traffic but of California doesn't have traffic but wildfires. wildfires. wildfires. >> Oh wow. Yes. >> Oh wow. Yes. >> Oh wow. Yes. >> Right. Who is fighting these wildfires? >> Right. Who is fighting these wildfires? >> Right. Who is fighting these wildfires? Putting humans in danger of rescue Putting humans in danger of rescue Putting humans in danger of rescue natural disaster is not a great idea. natural disaster is not a great idea. natural disaster is not a great idea. Right? So my family and my parents Right? So my family and my parents Right? So my family and my parents happen to have have enough means. But I happen to have have enough means. But I happen to have have enough means. But I was just thinking a elderly living was just thinking a elderly living was just thinking a elderly living alone. alone. alone. How do they go get grocery? How do they How do they go get grocery? How do they How do they go get grocery? How do they go get medicine? Now, there might be go get medicine? Now, there might be go get medicine? Now, there might be some shipping we are starting to see, some shipping we are starting to see, some shipping we are starting to see, but what if they want to go, you know, but what if they want to go, you know, but what if they want to go, you know, um um for a walk or want to go to a um um for a walk or want to go to a um um for a walk or want to go to a park? So, there are just so many things park? So, there are just so many things park? So, there are just so many things that uh Oh, by the way, you're in the that uh Oh, by the way, you're in the that uh Oh, by the way, you're in the school of medicine. school of medicine. school of medicine. We don't have an excess of caretakers. We don't have an excess of caretakers. We don't have an excess of caretakers. We have a shortage of caretakers. Our We have a shortage of caretakers. Our We have a shortage of caretakers. Our nurses are deeply fatigued and nurses are deeply fatigued and nurses are deeply fatigued and overworked. I was literally in the overworked. I was literally in the overworked. I was literally in the hospital with my dad for the past month hospital with my dad for the past month hospital with my dad for the past month and just watching the amount of work and just watching the amount of work and just watching the amount of work nurses do. We know that on a given shift nurses do. We know that on a given shift nurses do. We know that on a given shift nurses walk miles to fetch things, get nurses walk miles to fetch things, get nurses walk miles to fetch things, get medicine. There's just so many. Can you medicine. There's just so many. Can you medicine. There's just so many. Can you imagine robots helping, right? Like so imagine robots helping, right? Like so imagine robots helping, right? Like so there are just so many ways that our there are just so many ways that our there are just so many ways that our society can be structured and can society can be structured and can society can be structured and can benefit from uh help. benefit from uh help. benefit from uh help. >> Oh I I love these examples that you know >> Oh I I love these examples that you know >> Oh I I love these examples that you know so many spring to mind based on what you so many spring to mind based on what you so many spring to mind based on what you described. You know crossing guards. described. You know crossing guards. described. You know crossing guards. Yeah. You imagine with video that Yeah. You imagine with video that Yeah. You imagine with video that somebody who's you homebound because of somebody who's you homebound because of somebody who's you homebound because of age or illness could navigate to the age or illness could navigate to the age or illness could navigate to the store and pick things off the shelf. Um store and pick things off the shelf. Um store and pick things off the shelf. Um it doesn't have to be so disconnected it doesn't have to be so disconnected it doesn't have to be so disconnected that they just program and it comes that they just program and it comes that they just program and it comes back. that could be an option, too. back. that could be an option, too. back. that could be an option, too. >> Yeah, I think that we have to revise our >> Yeah, I think that we have to revise our >> Yeah, I think that we have to revise our notions of what this picture looks like notions of what this picture looks like notions of what this picture looks like because I think there are a couple because I think there are a couple because I think there are a couple things about robots and computers that things about robots and computers that things about robots and computers that scare people. Um, one is that is their scare people. Um, one is that is their scare people. Um, one is that is their physical hardness, physical hardness, physical hardness, >> right? And so the way we share space >> right? And so the way we share space >> right? And so the way we share space with them is very different than the way with them is very different than the way with them is very different than the way we share space with other things. we share space with other things. we share space with other things. >> Of course, I'm not thinking, oh, like >> Of course, I'm not thinking, oh, like >> Of course, I'm not thinking, oh, like you you cuddle with a with a robot, you you cuddle with a with a robot, you you cuddle with a with a robot, although some people might think that. although some people might think that. although some people might think that. That's not my mindset. But I am thinking That's not my mindset. But I am thinking That's not my mindset. But I am thinking like, okay, if I had a robot that could like, okay, if I had a robot that could like, okay, if I had a robot that could fold clothes, vacuum, water the plants, fold clothes, vacuum, water the plants, fold clothes, vacuum, water the plants, and feed my fish. Although I like to and feed my fish. Although I like to and feed my fish. Although I like to feed my fish myself. I really enjoy it. feed my fish myself. I really enjoy it. feed my fish myself. I really enjoy it. I love seeing them eat. I love being I love seeing them eat. I love being I love seeing them eat. I love being tactile, you know, in in literally in tactile, you know, in in literally in tactile, you know, in in literally in touch with them. They'll eat from my touch with them. They'll eat from my touch with them. They'll eat from my hand. hand. hand. >> Does your puppy like your fish? >> Does your puppy like your fish? >> Does your puppy like your fish? >> Uh, he does. He has his own fish tank. I >> Uh, he does. He has his own fish tank. I >> Uh, he does. He has his own fish tank. I just got him some tropical fish. Yeah. just got him some tropical fish. Yeah. just got him some tropical fish. Yeah. Right in front of his little Right in front of his little Right in front of his little >> He's taking care of them. >> He's taking care of them. >> He's taking care of them. >> Well, he looks at them. He's not >> Well, he looks at them. He's not >> Well, he looks at them. He's not equipped to take care of them yet, I equipped to take care of them yet, I equipped to take care of them yet, I don't think. [laughter] Unfortunately, don't think. [laughter] Unfortunately, don't think. [laughter] Unfortunately, there's not enough prefrontal cortex in there's not enough prefrontal cortex in there's not enough prefrontal cortex in him. So, and he's a he's a kind, but him. So, and he's a he's a kind, but him. So, and he's a he's a kind, but he's a bulldog mut. They're not the he's a bulldog mut. They're not the he's a bulldog mut. They're not the smartest breed. smartest breed. smartest breed. >> They only have a few learning rules, but >> They only have a few learning rules, but >> They only have a few learning rules, but they're very kind. they're very kind. they're very kind. >> Um, but if you want a dog that can take >> Um, but if you want a dog that can take >> Um, but if you want a dog that can take care of a fish tank, you probably need care of a fish tank, you probably need care of a fish tank, you probably need like a West Highland Terrier or like a West Highland Terrier or like a West Highland Terrier or something like that. Different, more something like that. Different, more something like that. Different, more prefrontal cortex. prefrontal cortex. prefrontal cortex. >> Take him. >> Take him. >> Take him. >> But the idea here is >> But the idea here is >> But the idea here is >> if one robot is doing one thing and >> if one robot is doing one thing and >> if one robot is doing one thing and another robot is doing another, it feels another robot is doing another, it feels another robot is doing another, it feels like a lot of hardware in my life. And I like a lot of hardware in my life. And I like a lot of hardware in my life. And I think that's kind of how people feel. think that's kind of how people feel. think that's kind of how people feel. >> But you could imagine a multimorphic >> But you could imagine a multimorphic >> But you could imagine a multimorphic robot. Do you know Baymax? robot. Do you know Baymax? robot. Do you know Baymax? >> I don't. >> I don't. >> I don't. >> Disney's robot probably 10 years ago, 15 >> Disney's robot probably 10 years ago, 15 >> Disney's robot probably 10 years ago, 15 years ago. It's a Google the image. The years ago. It's a Google the image. The years ago. It's a Google the image. The This is the white medicine robot. A This is the white medicine robot. A This is the white medicine robot. A healthcare robot that is very not healthcare robot that is very not healthcare robot that is very not fluffy. It's very spongy like it's it's fluffy. It's very spongy like it's it's fluffy. It's very spongy like it's it's feels like a big balloon. feels like a big balloon. feels like a big balloon. >> Mhm. Yeah. So, you might like that. >> Mhm. Yeah. So, you might like that. >> Mhm. Yeah. So, you might like that. Yeah. More more contours. Yeah. More more contours. Yeah. More more contours. >> Yeah. Yeah. >> Yeah. Yeah. >> Yeah. Yeah. >> And um more multitasking from the same >> And um more multitasking from the same >> And um more multitasking from the same robot. feels like a a world that I could robot. feels like a a world that I could robot. feels like a a world that I could adjust to more quickly than the idea of adjust to more quickly than the idea of adjust to more quickly than the idea of my world filled with robots. my world filled with robots. my world filled with robots. >> Yes. Again, Andrew, I think as we >> Yes. Again, Andrew, I think as we >> Yes. Again, Andrew, I think as we imagine the future and we talk about how imagine the future and we talk about how imagine the future and we talk about how we imagine the future, I keep coming we imagine the future, I keep coming we imagine the future, I keep coming back to the word agency. Humanity should back to the word agency. Humanity should back to the word agency. Humanity should have the agency to decide how we imagine have the agency to decide how we imagine have the agency to decide how we imagine this. It cannot just be a company or or this. It cannot just be a company or or this. It cannot just be a company or or I don't know an investor decide that the I don't know an investor decide that the I don't know an investor decide that the the world should be filled with metal the world should be filled with metal the world should be filled with metal like robots, right? like our society like robots, right? like our society like robots, right? like our society should be collectively should be collectively should be collectively proactively proactively proactively imagining and and one thing I worry in imagining and and one thing I worry in imagining and and one thing I worry in this AI rhetoric is that the public is this AI rhetoric is that the public is this AI rhetoric is that the public is put in a position of being reactive put in a position of being reactive put in a position of being reactive >> when it feels some people are just >> when it feels some people are just >> when it feels some people are just deciding deciding deciding >> and the multistakeholders are not >> and the multistakeholders are not >> and the multistakeholders are not participating in this participating in this participating in this designing the future together designing the future together designing the future together >> like with your example of your father's >> like with your example of your father's >> like with your example of your father's surgery surgery surgery to cross the the the robot with the to cross the the the robot with the to cross the the the robot with the physician, right? physician, right? physician, right? >> If we cross a problem where there's a >> If we cross a problem where there's a >> If we cross a problem where there's a vulnerability with a robot that clearly vulnerability with a robot that clearly vulnerability with a robot that clearly makes things better, makes things better, makes things better, >> the picture changes Yeah. >> the picture changes Yeah. >> the picture changes Yeah. >> in the right direction. So, I'm thinking >> in the right direction. So, I'm thinking >> in the right direction. So, I'm thinking of a few examples off the top of my head of a few examples off the top of my head of a few examples off the top of my head like um I think most people would agree like um I think most people would agree like um I think most people would agree that if their kids could walk themselves that if their kids could walk themselves that if their kids could walk themselves to school and home, it would be great, to school and home, it would be great, to school and home, it would be great, but you worry about safety. But if a but you worry about safety. But if a but you worry about safety. But if a robot was really a good guardian of your robot was really a good guardian of your robot was really a good guardian of your kid to the point where they could alert kid to the point where they could alert kid to the point where they could alert the authorities or maybe even protect the authorities or maybe even protect the authorities or maybe even protect physically protect your child, physically protect your child, physically protect your child, >> that would be awesome. Give them more >> that would be awesome. Give them more >> that would be awesome. Give them more agency in the world. agency in the world. agency in the world. >> You think about um some of the darker >> You think about um some of the darker >> You think about um some of the darker but nonetheless unfortunately real but nonetheless unfortunately real but nonetheless unfortunately real predatory behavior online. predatory behavior online. predatory behavior online. >> Parents can only oversee their kids >> Parents can only oversee their kids >> Parents can only oversee their kids behavior so much. Kids are only aware of behavior so much. Kids are only aware of behavior so much. Kids are only aware of so much that's happening. But you could so much that's happening. But you could so much that's happening. But you could imagine uh kind of an avatar in there imagine uh kind of an avatar in there imagine uh kind of an avatar in there with you that's really advocating for with you that's really advocating for with you that's really advocating for you that can spot things and keep you that can spot things and keep you that can spot things and keep predators at bay. predators at bay. predators at bay. >> Here you go. That's a great startup >> Here you go. That's a great startup >> Here you go. That's a great startup idea. idea. idea. >> Like that would be cool. But here's >> Like that would be cool. But here's >> Like that would be cool. But here's what's missing I think from the picture what's missing I think from the picture what's missing I think from the picture >> for me. I remember seeing this >> for me. I remember seeing this >> for me. I remember seeing this incredible guy. I know people some say incredible guy. I know people some say incredible guy. I know people some say he was kind of prickly but this he was kind of prickly but this he was kind of prickly but this incredible guy walking around downtown incredible guy walking around downtown incredible guy walking around downtown PaloAlto when I was a posttock and when PaloAlto when I was a posttock and when PaloAlto when I was a posttock and when I was a kid growing up working at the I was a kid growing up working at the I was a kid growing up working at the Paltoy and Sport World and that was Paltoy and Sport World and that was Paltoy and Sport World and that was Steve Jobs. no shoes, kind of look like Steve Jobs. no shoes, kind of look like Steve Jobs. no shoes, kind of look like a hippie. a hippie. a hippie. Yes, he shouted at people at work, and Yes, he shouted at people at work, and Yes, he shouted at people at work, and you know, probably HR wouldn't look too you know, probably HR wouldn't look too you know, probably HR wouldn't look too kindly upon him nowadays, but he kindly upon him nowadays, but he kindly upon him nowadays, but he understood that these things we call understood that these things we call understood that these things we call computers needed to have rounded edges. computers needed to have rounded edges. computers needed to have rounded edges. >> Yes, >> Yes, >> Yes, >> they needed to fit kind of seamlessly in >> they needed to fit kind of seamlessly in >> they needed to fit kind of seamlessly in our pocket. They needed to have Bob our pocket. They needed to have Bob our pocket. They needed to have Bob Dylan on the landing page or whatever so Dylan on the landing page or whatever so Dylan on the landing page or whatever so that it softened the relationship to that it softened the relationship to that it softened the relationship to technology. Some people would say, well, technology. Some people would say, well, technology. Some people would say, well, it went too far. It was a Trojan horse. it went too far. It was a Trojan horse. it went too far. It was a Trojan horse. But I don't think so. somebody who But I don't think so. somebody who But I don't think so. somebody who really understands human nature to allow really understands human nature to allow really understands human nature to allow these like what are clearly going to be these like what are clearly going to be these like what are clearly going to be benevolent collaborations between robots benevolent collaborations between robots benevolent collaborations between robots and humans to happen because as you've and humans to happen because as you've and humans to happen because as you've pointed out and with total respect to pointed out and with total respect to pointed out and with total respect to the technologists that have built AI and the technologists that have built AI and the technologists that have built AI and the scientists that do amazing science, the scientists that do amazing science, the scientists that do amazing science, there's a hardness to either the way there's a hardness to either the way there's a hardness to either the way they're being presented or what they're they're being presented or what they're they're being presented or what they're capable of sharing that is a real capable of sharing that is a real capable of sharing that is a real separator. separator. separator. >> Yes. And I'm not a therapist, but if I >> Yes. And I'm not a therapist, but if I >> Yes. And I'm not a therapist, but if I could like wrap my arms around him, I'd could like wrap my arms around him, I'd could like wrap my arms around him, I'd be like, "Listen, guys, you're the be like, "Listen, guys, you're the be like, "Listen, guys, you're the smartest people in the room, guys and smartest people in the room, guys and smartest people in the room, guys and gals. To be fair, you're the smartest gals. To be fair, you're the smartest gals. To be fair, you're the smartest people in the room." But people don't people in the room." But people don't people in the room." But people don't like you because they don't understand like you because they don't understand like you because they don't understand you and they're maybe you need a you and they're maybe you need a you and they're maybe you need a collaborator to help you share your collaborator to help you share your collaborator to help you share your vision in a way that isn't going to vision in a way that isn't going to vision in a way that isn't going to allow the press because the media is allow the press because the media is allow the press because the media is guilty of of building this chasm because guilty of of building this chasm because guilty of of building this chasm because it's like these technologists, they're it's like these technologists, they're it's like these technologists, they're coming for us. I I think that's I think coming for us. I I think that's I think coming for us. I I think that's I think that's a total trick of media, too. that's a total trick of media, too. that's a total trick of media, too. that's just to put money in their that's just to put money in their that's just to put money in their pocket. Like there's a lot going on pocket. Like there's a lot going on pocket. Like there's a lot going on right now. So who's the Steve Jobs or right now. So who's the Steve Jobs or right now. So who's the Steve Jobs or the Stacy Stacy whoever it's I mean the Stacy Stacy whoever it's I mean the Stacy Stacy whoever it's I mean could be a man could be a woman. Someone could be a man could be a woman. Someone could be a man could be a woman. Someone who really understands human beings. who really understands human beings. who really understands human beings. >> Many of them there are many of us you >> Many of them there are many of us you >> Many of them there are many of us you know I mean Stanford started human know I mean Stanford started human know I mean Stanford started human center AI institute. Well, there's you. center AI institute. Well, there's you. center AI institute. Well, there's you. There's you. There's you. There's you. >> Okay. But there are many. >> Okay. But there are many. >> Okay. But there are many. >> Yeah. >> Yeah. >> Yeah. >> There are plenty of entrepreneurs who >> There are plenty of entrepreneurs who >> There are plenty of entrepreneurs who are doing incredible uh startups on AI are doing incredible uh startups on AI are doing incredible uh startups on AI for drug discovery, AI for health care, for drug discovery, AI for health care, for drug discovery, AI for health care, AI for aging, AI for mental health. AI for aging, AI for mental health. AI for aging, AI for mental health. These people care about AI, right? There These people care about AI, right? There These people care about AI, right? There are many designers and and product are many designers and and product are many designers and and product managers who are trying to I I do think managers who are trying to I I do think managers who are trying to I I do think the megaphone is too much focused on the megaphone is too much focused on the megaphone is too much focused on people pumping their chest and talking people pumping their chest and talking people pumping their chest and talking about tech in a certain particular way. about tech in a certain particular way. about tech in a certain particular way. So, you know, even this podcast is is So, you know, even this podcast is is So, you know, even this podcast is is making a positive difference, I hope, is making a positive difference, I hope, is making a positive difference, I hope, is to to put that human angle, the the the to to put that human angle, the the the to to put that human angle, the the the rounded human angle, human perspective, rounded human angle, human perspective, rounded human angle, human perspective, human human future into these human human future into these human human future into these conversations. I don't feel despair, conversations. I don't feel despair, conversations. I don't feel despair, Andrew. I'm an educator. I'm a builder. Andrew. I'm an educator. I'm a builder. Andrew. I'm an educator. I'm a builder. I'm a technologist. I see many people I'm a technologist. I see many people I'm a technologist. I see many people around me, including my entire startup. around me, including my entire startup. around me, including my entire startup. They they these brilliant young They they these brilliant young They they these brilliant young technologists could join any startup or technologists could join any startup or technologists could join any startup or company they want but they come to world company they want but they come to world company they want but they come to world labs because they want to empower people labs because they want to empower people labs because they want to empower people right so I see many people but I don't right so I see many people but I don't right so I see many people but I don't think there's enough you're right I think there's enough you're right I think there's enough you're right I don't think the com the public discourse don't think the com the public discourse don't think the com the public discourse is um is balanced right now and there is is um is balanced right now and there is is um is balanced right now and there is too much extreme rhetoric either in too much extreme rhetoric either in too much extreme rhetoric either in terms of extreme doomerism and terms of extreme doomerism and terms of extreme doomerism and lack of safety like it's just freaking lack of safety like it's just freaking lack of safety like it's just freaking people out or extreme utopian as if people out or extreme utopian as if people out or extreme utopian as if technology can do no run and then that's technology can do no run and then that's technology can do no run and then that's disingenuous people would say well okay disingenuous people would say well okay disingenuous people would say well okay you're the halves of course you say that you're the halves of course you say that you're the halves of course you say that so I think we should come to the middle so I think we should come to the middle so I think we should come to the middle and talk about what this technology is and talk about what this technology is and talk about what this technology is how to use it how we can collectively how to use it how we can collectively how to use it how we can collectively have that agency to guide the future have that agency to guide the future have that agency to guide the future Yeah. One thing that was pointed out to Yeah. One thing that was pointed out to Yeah. One thing that was pointed out to me by one of my podcast colleagues that me by one of my podcast colleagues that me by one of my podcast colleagues that that was should have been obvious but that was should have been obvious but that was should have been obvious but wasn't and and clearly this is something wasn't and and clearly this is something wasn't and and clearly this is something that you you uh for lack of a better that you you uh for lack of a better that you you uh for lack of a better word you embody among many other things word you embody among many other things word you embody among many other things is people don't really want to hear is people don't really want to hear is people don't really want to hear stories about machines. But people love stories about machines. But people love stories about machines. But people love hearing that some person cured their hearing that some person cured their hearing that some person cured their dog's cancer or their child that was dog's cancer or their child that was dog's cancer or their child that was experiencing experiencing experiencing >> crazy symptoms. They had no clue. The >> crazy symptoms. They had no clue. The >> crazy symptoms. They had no clue. The doctors had no clue and their fingertips doctors had no clue and their fingertips doctors had no clue and their fingertips AI AI AI >> solved the problem. >> solved the problem. >> solved the problem. >> These are the stories that really need >> These are the stories that really need >> These are the stories that really need amplification because I think that they amplification because I think that they amplification because I think that they we can relate to them and and they're we can relate to them and and they're we can relate to them and and they're beautiful stories. They're incredible beautiful stories. They're incredible beautiful stories. They're incredible stories, but they're not getting nearly stories, but they're not getting nearly stories, but they're not getting nearly as much attention as the other stuff. as much attention as the other stuff. as much attention as the other stuff. Yeah. Yeah. Yeah. >> And that's a it's a challenge. >> And that's a it's a challenge. >> And that's a it's a challenge. >> Yeah. >> Yeah. >> Yeah. >> You know, traditional media doesn't >> You know, traditional media doesn't >> You know, traditional media doesn't really care about the long arc of really care about the long arc of really care about the long arc of things. they are on a like a 12 to 24 things. they are on a like a 12 to 24 things. they are on a like a 12 to 24 hour cycle but other names perhaps of hour cycle but other names perhaps of hour cycle but other names perhaps of like people who are really trying to like people who are really trying to like people who are really trying to like talk about the benevolent use of AI like talk about the benevolent use of AI like talk about the benevolent use of AI these collaborations that you know we these collaborations that you know we these collaborations that you know we should be aware of should be aware of should be aware of >> Stephan for HI's newsletter our website >> Stephan for HI's newsletter our website >> Stephan for HI's newsletter our website our seminars we uh promote a lot of our seminars we uh promote a lot of our seminars we uh promote a lot of those work those work those work >> I would love to learn more about your >> I would love to learn more about your >> I would love to learn more about your startup um because you don't pick startup um because you don't pick startup um because you don't pick projects haphazardly so what is the what projects haphazardly so what is the what projects haphazardly so what is the what is the project what's the goal is the project what's the goal is the project what's the goal >> so my startup uh co-founded with um a >> so my startup uh co-founded with um a >> so my startup uh co-founded with um a couple of other co-founders is called uh couple of other co-founders is called uh couple of other co-founders is called uh World Labs. World Labs. World Labs. >> We co-ounded it at the beginning of uh >> We co-ounded it at the beginning of uh >> We co-ounded it at the beginning of uh 2024. 2024. 2024. It really is for for me a kind of my It really is for for me a kind of my It really is for for me a kind of my life's work. You know, we both come from life's work. You know, we both come from life's work. You know, we both come from vision and the recognition of uh there's vision and the recognition of uh there's vision and the recognition of uh there's more beyond language intelligence is more beyond language intelligence is more beyond language intelligence is what really motivated me to to think what really motivated me to to think what really motivated me to to think hard about what's the next chapter of AI hard about what's the next chapter of AI hard about what's the next chapter of AI frontier and uh we recognize that frontier and uh we recognize that frontier and uh we recognize that unlocking spatial and physical unlocking spatial and physical unlocking spatial and physical intelligence is really the next chapter intelligence is really the next chapter intelligence is really the next chapter that it's not excluding languages of that it's not excluding languages of that it's not excluding languages of course the language technology is course the language technology is course the language technology is incredible is where we can um uh devote incredible is where we can um uh devote incredible is where we can um uh devote more time to build um models or build more time to build um models or build more time to build um models or build eventually products that can help eventually products that can help eventually products that can help unlocking capabilities in spatial unlocking capabilities in spatial unlocking capabilities in spatial intelligence like generating 3D 4D intelligence like generating 3D 4D intelligence like generating 3D 4D worlds that are um deeply useful for worlds that are um deeply useful for worlds that are um deeply useful for creators for robot training for uh creators for robot training for uh creators for robot training for uh architecture design design or to en architecture design design or to en architecture design design or to en enable those interactive environments. enable those interactive environments. enable those interactive environments. Whether you're talking about healthcare Whether you're talking about healthcare Whether you're talking about healthcare usage or education usage or robotics usage or education usage or robotics usage or education usage or robotics usage or industry usage, these usage or industry usage, these usage or industry usage, these capabilities goes beyond language capabilities goes beyond language capabilities goes beyond language >> per se. And uh so World Labs was founded >> per se. And uh so World Labs was founded >> per se. And uh so World Labs was founded based on that premise. We are still a based on that premise. We are still a based on that premise. We are still a young company. We're very much a um a young company. We're very much a um a young company. We're very much a um a model focused company where we're model focused company where we're model focused company where we're building these this foundation model and building these this foundation model and building these this foundation model and we're started by a lot of PhDs we're started by a lot of PhDs we're started by a lot of PhDs but now we we we we're starting to build but now we we we we're starting to build but now we we we we're starting to build products and uh so it's a it's still the products and uh so it's a it's still the products and uh so it's a it's still the beginning. It's very exciting and as a beginning. It's very exciting and as a beginning. It's very exciting and as a technologist I feel deep in my heart I'm technologist I feel deep in my heart I'm technologist I feel deep in my heart I'm a builder a builder a builder >> you know it's maybe it's because also >> you know it's maybe it's because also >> you know it's maybe it's because also I'm an immigrant so that that I'm an immigrant so that that I'm an immigrant so that that rolling your sleeves up and just get in rolling your sleeves up and just get in rolling your sleeves up and just get in with the young generation that's so with the young generation that's so with the young generation that's so incredibly smart and just build incredibly smart and just build incredibly smart and just build something from scratch is just so something from scratch is just so something from scratch is just so exciting. exciting. exciting. >> I recall a time not but what 15 20 years >> I recall a time not but what 15 20 years >> I recall a time not but what 15 20 years ago when there were cars driving around ago when there were cars driving around ago when there were cars driving around >> Oh yeah. taking images >> Oh yeah. taking images >> Oh yeah. taking images >> still still driving around >> still still driving around >> still still driving around >> still driving around taking images but I >> still driving around taking images but I >> still driving around taking images but I imagine that there and there are imagine that there and there are imagine that there and there are certainly aerial views as well but you certainly aerial views as well but you certainly aerial views as well but you could imagine little tiny drones like could imagine little tiny drones like could imagine little tiny drones like the type that could fly through a neuron the type that could fly through a neuron the type that could fly through a neuron and just kind of look at everything or and just kind of look at everything or and just kind of look at everything or um so to speak or drones picking up um so to speak or drones picking up um so to speak or drones picking up information about every nook and cranny information about every nook and cranny information about every nook and cranny of the fjords in Norway has that been of the fjords in Norway has that been of the fjords in Norway has that been done to sort of map the the done to sort of map the the done to sort of map the the three-dimensional world three-dimensional world three-dimensional world >> first of all let's not make it sound >> first of all let's not make it sound >> first of all let's not make it sound scary that drones are [clears throat] scary that drones are [clears throat] scary that drones are [clears throat] getting into people's homes and getting into people's homes and getting into people's homes and properties. I think that the ability to properties. I think that the ability to properties. I think that the ability to capture imageries of the world is really capture imageries of the world is really capture imageries of the world is really rapidly advanced, right? Like our cell rapidly advanced, right? Like our cell rapidly advanced, right? Like our cell phones are incredible sensors. They're phones are incredible sensors. They're phones are incredible sensors. They're not drones, but people take a lot of not drones, but people take a lot of not drones, but people take a lot of photos. And of course, our camera photos. And of course, our camera photos. And of course, our camera technology has improved. What World Labs technology has improved. What World Labs technology has improved. What World Labs is doing is not just taking real world is doing is not just taking real world is doing is not just taking real world images. It's we allow people to imagine images. It's we allow people to imagine images. It's we allow people to imagine what's in their mind's eye. As long as what's in their mind's eye. As long as what's in their mind's eye. As long as you could type a sentence or show a you could type a sentence or show a you could type a sentence or show a picture or a sketch of what you imagine, picture or a sketch of what you imagine, picture or a sketch of what you imagine, we try to turn that into worlds we try to turn that into worlds we try to turn that into worlds >> and environments. Um why is it useful? >> and environments. Um why is it useful? >> and environments. Um why is it useful? Because uh entertainment industry would Because uh entertainment industry would Because uh entertainment industry would use it, design industry would use it, use it, design industry would use it, use it, design industry would use it, robotics industry uh very much would use robotics industry uh very much would use robotics industry uh very much would use it for training environments and and and it for training environments and and and it for training environments and and and all that. So the combination of all that. So the combination of all that. So the combination of capturing what's in the real world as capturing what's in the real world as capturing what's in the real world as well as capturing what's in your well as capturing what's in your well as capturing what's in your imagined world is the new frontier. imagined world is the new frontier. imagined world is the new frontier. >> If you don't mind, I'd like to just take >> If you don't mind, I'd like to just take >> If you don't mind, I'd like to just take a couple of more minutes and talk about a couple of more minutes and talk about a couple of more minutes and talk about this uh moving from imagination to this uh moving from imagination to this uh moving from imagination to >> something. Because this is Los Angeles, >> something. Because this is Los Angeles, >> something. Because this is Los Angeles, it occurred to me that a lot of people it occurred to me that a lot of people it occurred to me that a lot of people write scripts write scripts write scripts >> and then they try and get them their >> and then they try and get them their >> and then they try and get them their movie made. Mhm. movie made. Mhm. movie made. Mhm. >> But with AI, in theory, you could take a >> But with AI, in theory, you could take a >> But with AI, in theory, you could take a script and give it to AI and it could script and give it to AI and it could script and give it to AI and it could make the movie in theory, right? Going make the movie in theory, right? Going make the movie in theory, right? Going from words to pictures to video. Um, and from words to pictures to video. Um, and from words to pictures to video. Um, and you could maybe edit it a little bit you could maybe edit it a little bit you could maybe edit it a little bit here and there where it needed help, of here and there where it needed help, of here and there where it needed help, of course. Has that been done? Has a a course. Has that been done? Has a a course. Has that been done? Has a a successful movie been made start to successful movie been made start to successful movie been made start to finish using AI? finish using AI? finish using AI? >> So, this is a very nuance topic. This is >> So, this is a very nuance topic. This is >> So, this is a very nuance topic. This is where we also get into people's where we also get into people's where we also get into people's weariness of AI and creativity when if weariness of AI and creativity when if weariness of AI and creativity when if not careful it might sound like we're not careful it might sound like we're not careful it might sound like we're taking away from storytellers and taking away from storytellers and taking away from storytellers and creators job. Right? So, so let's creators job. Right? So, so let's creators job. Right? So, so let's separate this job conversation from the separate this job conversation from the separate this job conversation from the technology conversation a little bit technology conversation a little bit technology conversation a little bit even though they're entangled. even though they're entangled. even though they're entangled. technology has advanced enough that technology has advanced enough that technology has advanced enough that taking scripts and generating shots, taking scripts and generating shots, taking scripts and generating shots, video shots is is getting really good. video shots is is getting really good. video shots is is getting really good. We have seen short movies even almost We have seen short movies even almost We have seen short movies even almost feature length films being assembled by feature length films being assembled by feature length films being assembled by AI AI tools. We have and and there are AI AI tools. We have and and there are AI AI tools. We have and and there are many companies US companies, Asian many companies US companies, Asian many companies US companies, Asian companies creating technology. companies creating technology. companies creating technology. But what remains deeply human and that But what remains deeply human and that But what remains deeply human and that is important is is important is is important is every part of storytelling and story every part of storytelling and story every part of storytelling and story creation. creation. creation. There are humans behind it with their There are humans behind it with their There are humans behind it with their unique, unique, unique, emotion, story, emotion, story, emotion, story, technique, technique, technique, how they see the world, how they move how they see the world, how they move how they see the world, how they move the cameras, how they characterize pe the cameras, how they characterize pe the cameras, how they characterize pe characters. A lot of that is what characters. A lot of that is what characters. A lot of that is what Hollywood and and novel writers is Hollywood and and novel writers is Hollywood and and novel writers is about. So how do we meet the human need about. So how do we meet the human need about. So how do we meet the human need and human and human and human desire of storytelling with modern tools desire of storytelling with modern tools desire of storytelling with modern tools is actually a a challenge because there is actually a a challenge because there is actually a a challenge because there is a fear very much coming from is a fear very much coming from is a fear very much coming from Hollywood that AI is taking over and Hollywood that AI is taking over and Hollywood that AI is taking over and storytellers and actors and storytellers and actors and storytellers and actors and screenwriters the jobs are being screenwriters the jobs are being screenwriters the jobs are being impacted and I think it is but how is it impacted and I think it is but how is it impacted and I think it is but how is it being impacted, what are we doing about being impacted, what are we doing about being impacted, what are we doing about it? Who is working in a in a it? Who is working in a in a it? Who is working in a in a constructive way? You know, this is not constructive way? You know, this is not constructive way? You know, this is not my industry per se, but I would love to my industry per se, but I would love to my industry per se, but I would love to see much more nuanced work see much more nuanced work see much more nuanced work >> in in this and also nuanced public >> in in this and also nuanced public >> in in this and also nuanced public discussion about that. But I do think discussion about that. But I do think discussion about that. But I do think just like healthcare, we were talking just like healthcare, we were talking just like healthcare, we were talking about how AI can rapidly change and about how AI can rapidly change and about how AI can rapidly change and disrupt the old ways of doing disrupt the old ways of doing disrupt the old ways of doing healthcare. I think AI is absolutely healthcare. I think AI is absolutely healthcare. I think AI is absolutely changing the way we're doing um changing the way we're doing um changing the way we're doing um storytelling. So one story speaking of storytelling. So one story speaking of storytelling. So one story speaking of which I have a co-founder whose name is which I have a co-founder whose name is which I have a co-founder whose name is Ben and Ben and I met with Ben Affleck. Ben and Ben and I met with Ben Affleck. Ben and Ben and I met with Ben Affleck. So I was joking Ben meeting Ben who is So I was joking Ben meeting Ben who is So I was joking Ben meeting Ben who is also thinking very avanguard about using also thinking very avanguard about using also thinking very avanguard about using AI tools about film making right. So AI tools about film making right. So AI tools about film making right. So having conversations between having conversations between having conversations between technologists technologists technologists and storytellers or movie makers at this and storytellers or movie makers at this and storytellers or movie makers at this moment is critical. moment is critical. moment is critical. >> Yeah. I feel like in every example of >> Yeah. I feel like in every example of >> Yeah. I feel like in every example of technology, there's some crossover point technology, there's some crossover point technology, there's some crossover point that when somebody who's truly an that when somebody who's truly an that when somebody who's truly an insider embraces a technology insider embraces a technology insider embraces a technology >> and then >> and then >> and then >> it just kind of takes off like >> it just kind of takes off like >> it just kind of takes off like >> you know uh Steven Spielberg or >> you know uh Steven Spielberg or >> you know uh Steven Spielberg or something like that or these probably something like that or these probably something like that or these probably aren't the best examples but like the aren't the best examples but like the aren't the best examples but like the the Steve Jobs Wnjak crossover kind of a the Steve Jobs Wnjak crossover kind of a the Steve Jobs Wnjak crossover kind of a designer designer designer technology curious guy and and a real technology curious guy and and a real technology curious guy and and a real forgive me to the Jobs film but a real forgive me to the Jobs film but a real forgive me to the Jobs film but a real computer scientist. right? That merge computer scientist. right? That merge computer scientist. right? That merge these collaborations are really key like these collaborations are really key like these collaborations are really key like so you need an insider and an outsider so you need an insider and an outsider so you need an insider and an outsider to do it right because you have to to do it right because you have to to do it right because you have to understand both cultures and how to understand both cultures and how to understand both cultures and how to include include include >> the industry the the people >> the industry the the people >> the industry the the people >> so I really hope right cuz world labs >> so I really hope right cuz world labs >> so I really hope right cuz world labs works with VFX industry as well it's so works with VFX industry as well it's so works with VFX industry as well it's so important for me that our customers and important for me that our customers and important for me that our customers and users feel empowered users feel empowered users feel empowered >> it's not that technology should be >> it's not that technology should be >> it's not that technology should be taking their jobs away technology should taking their jobs away technology should taking their jobs away technology should be making their jobs better be making their jobs better be making their jobs better superpowering their creativity And superpowering their creativity And superpowering their creativity And that's how I I see this technology and that's how I I see this technology and that's how I I see this technology and that's how I would like to work with the that's how I would like to work with the that's how I would like to work with the users and customers. users and customers. users and customers. >> It's wild to think that, you know, when >> It's wild to think that, you know, when >> It's wild to think that, you know, when I was a kid on California Avenue in Palo I was a kid on California Avenue in Palo I was a kid on California Avenue in Palo Alto, there was this store, Keeblin Alto, there was this store, Keeblin Alto, there was this store, Keeblin Shucket, and it was just a photograph Shucket, and it was just a photograph Shucket, and it was just a photograph store and camera store. Yes. store and camera store. Yes. store and camera store. Yes. >> You go in there, you got your film >> You go in there, you got your film >> You go in there, you got your film developed, and there were all these guys developed, and there were all these guys developed, and there were all these guys behind the counter, and they tell you behind the counter, and they tell you behind the counter, and they tell you all you could rent a longdistance lens all you could rent a longdistance lens all you could rent a longdistance lens and this kind of thing. Um, none of that and this kind of thing. Um, none of that and this kind of thing. Um, none of that exists anymore. or everything went exists anymore. or everything went exists anymore. or everything went digital, you know, but there are still digital, you know, but there are still digital, you know, but there are still camera stores. So, industries can morph. camera stores. So, industries can morph. camera stores. So, industries can morph. They don't always get obliterated. They don't always get obliterated. They don't always get obliterated. >> Yeah, it morphs. People also get >> Yeah, it morphs. People also get >> Yeah, it morphs. People also get reskilled, upskilled. You know, we are reskilled, upskilled. You know, we are reskilled, upskilled. You know, we are working with a lot of creators who are working with a lot of creators who are working with a lot of creators who are using AI tools because they see where using AI tools because they see where using AI tools because they see where technology is going and they want to technology is going and they want to technology is going and they want to reskill and upskill themselves. So, I reskill and upskill themselves. So, I reskill and upskill themselves. So, I think moments of change is moment of think moments of change is moment of think moments of change is moment of both opportunity and loss. We both opportunity and loss. We both opportunity and loss. We [clears throat] need to really be [clears throat] need to really be [clears throat] need to really be thoughtful about that. thoughtful about that. thoughtful about that. >> My last question is about the young >> My last question is about the young >> My last question is about the young generation. How do they feel about AI? generation. How do they feel about AI? generation. How do they feel about AI? Because there is this Because there is this Because there is this >> How young are you talking about? >> How young are you talking about? >> How young are you talking about? >> I'm talking about kids between the age >> I'm talking about kids between the age >> I'm talking about kids between the age of uh seven and 20. of uh seven and 20. of uh seven and 20. >> Okay, that that's literally my kids. >> Okay, that that's literally my kids. >> Okay, that that's literally my kids. >> Yeah. So, I might have asked that >> Yeah. So, I might have asked that >> Yeah. So, I might have asked that question for a reason. You know, how do question for a reason. You know, how do question for a reason. You know, how do they feel about it? Are they excited by they feel about it? Are they excited by they feel about it? Are they excited by it? Because there is this phenomenon it? Because there is this phenomenon it? Because there is this phenomenon where like computers come along and you where like computers come along and you where like computers come along and you know your handwriting teacher is getting know your handwriting teacher is getting know your handwriting teacher is getting nervous that people aren't just typing. nervous that people aren't just typing. nervous that people aren't just typing. Now they're all writing with their Now they're all writing with their Now they're all writing with their fingertips and no one's going to know fingertips and no one's going to know fingertips and no one's going to know how to write and we wrote for there's how to write and we wrote for there's how to write and we wrote for there's these these stories have been around for these these stories have been around for these these stories have been around for a long time about how we're just going a long time about how we're just going a long time about how we're just going to dissolve into a puddle of our own to dissolve into a puddle of our own to dissolve into a puddle of our own neurons if we don't uh embrace the past neurons if we don't uh embrace the past neurons if we don't uh embrace the past as much as the future. And I like to as much as the future. And I like to as much as the future. And I like to think some of both is what's important. think some of both is what's important. think some of both is what's important. But how do the kids feel? What do they But how do the kids feel? What do they But how do the kids feel? What do they think? This is actually my pet project think? This is actually my pet project think? This is actually my pet project as a educator and technologist. as a educator and technologist. as a educator and technologist. Everywhere I go, I try to talk to to Everywhere I go, I try to talk to to Everywhere I go, I try to talk to to students, parents, and teachers because students, parents, and teachers because students, parents, and teachers because I think that is the most forgotten I think that is the most forgotten I think that is the most forgotten population, population, population, our policy makers and our technologist our policy makers and our technologist our policy makers and our technologist and our investors. They don't talk about and our investors. They don't talk about and our investors. They don't talk about teachers, parents, and students. They teachers, parents, and students. They teachers, parents, and students. They all have opinions and they all have all have opinions and they all have all have opinions and they all have kids, but they don't talk about it. I kids, but they don't talk about it. I kids, but they don't talk about it. I always have hope for kids. Maybe because always have hope for kids. Maybe because always have hope for kids. Maybe because I'm an educator because I think the I'm an educator because I think the I'm an educator because I think the biggest thing humanity never learns is biggest thing humanity never learns is biggest thing humanity never learns is the older generation lamenting about the the older generation lamenting about the the older generation lamenting about the future generation as if the future future generation as if the future future generation as if the future generation doesn't know anything. generation doesn't know anything. generation doesn't know anything. They're rude. They're they're they're They're rude. They're they're they're They're rude. They're they're they're forgetting the past. But if you look at forgetting the past. But if you look at forgetting the past. But if you look at arc of history of humanity, by and arc of history of humanity, by and arc of history of humanity, by and large, we advance for the better. Now, large, we advance for the better. Now, large, we advance for the better. Now, I'm not denying the atrocities. I'm not I'm not denying the atrocities. I'm not I'm not denying the atrocities. I'm not denying the setbacks. I'm not denying denying the setbacks. I'm not denying denying the setbacks. I'm not denying this but you know humanity there this but you know humanity there this but you know humanity there fundamentally I'm a optimist in humanity fundamentally I'm a optimist in humanity fundamentally I'm a optimist in humanity right so so that's where I come from so right so so that's where I come from so right so so that's where I come from so if you're a total pessimist maybe we're if you're a total pessimist maybe we're if you're a total pessimist maybe we're already on the wrong footing but I look already on the wrong footing but I look already on the wrong footing but I look at kids they're curious that's why at kids they're curious that's why at kids they're curious that's why they're kids they're curious they of they're kids they're curious they of they're kids they're curious they of course they get massively entertained by course they get massively entertained by course they get massively entertained by this technology but they also are this technology but they also are this technology but they also are starting to use it what I worry worry starting to use it what I worry worry starting to use it what I worry worry about our teachers and some parents about our teachers and some parents about our teachers and some parents because I think our society today and because I think our society today and because I think our society today and especially Silicon Valley are not doing especially Silicon Valley are not doing especially Silicon Valley are not doing them a service. We're forgetting about them a service. We're forgetting about them a service. We're forgetting about them. We are lecturing them. We are them. We are lecturing them. We are them. We are lecturing them. We are berating them. We are looking down at berating them. We are looking down at berating them. We are looking down at them. They are the most important people them. They are the most important people them. They are the most important people in our society. We should be talking to in our society. We should be talking to in our society. We should be talking to them. We should be uplifting them. We them. We should be uplifting them. We them. We should be uplifting them. We should be supporting them. We should be should be supporting them. We should be should be supporting them. We should be providing resources to them. K12 teacher providing resources to them. K12 teacher providing resources to them. K12 teacher or K16 teachers, they share the most or K16 teachers, they share the most or K16 teachers, they share the most important critical burden of our important critical burden of our important critical burden of our society. I'll tell you a real story. society. I'll tell you a real story. society. I'll tell you a real story. November November November 2022, Chad GPT came out. Obviously, I'm 2022, Chad GPT came out. Obviously, I'm 2022, Chad GPT came out. Obviously, I'm an insider in terms of technology, but an insider in terms of technology, but an insider in terms of technology, but the first thing I did was emailing the the first thing I did was emailing the the first thing I did was emailing the principal of the elementary school my principal of the elementary school my principal of the elementary school my kid was in and said, "I would like to kid was in and said, "I would like to kid was in and said, "I would like to come and guest lecture for your students come and guest lecture for your students come and guest lecture for your students and teachers." It's not because I'm so and teachers." It's not because I'm so and teachers." It's not because I'm so special. It's because I want them in special. It's because I want them in special. It's because I want them in real time to know what's happening. real time to know what's happening. real time to know what's happening. Because nobody, nobody in Silicon Because nobody, nobody in Silicon Because nobody, nobody in Silicon Valley, no investors, multi-billion Valley, no investors, multi-billion Valley, no investors, multi-billion dollar investment firms or multi-million dollar investment firms or multi-million dollar investment firms or multi-million dollar, multi-t trillion dollar dollar, multi-t trillion dollar dollar, multi-t trillion dollar companies. When chap GBT came out, the companies. When chap GBT came out, the companies. When chap GBT came out, the first thing is what about our teachers first thing is what about our teachers first thing is what about our teachers in the neighborhood? Nobody think like in the neighborhood? Nobody think like in the neighborhood? Nobody think like that. that. that. But we need to we need to be talking to But we need to we need to be talking to But we need to we need to be talking to teachers. We need to show teachers. Of teachers. We need to show teachers. Of teachers. We need to show teachers. Of course, they're going to ask the course, they're going to ask the course, they're going to ask the question about what if kids cheat. It's question about what if kids cheat. It's question about what if kids cheat. It's okay. They ask those questions. Let's okay. They ask those questions. Let's okay. They ask those questions. Let's just show them. Let's work with them and just show them. Let's work with them and just show them. Let's work with them and empower them to come up with ways to empower them to come up with ways to empower them to come up with ways to deal with that. They are smart, too. deal with that. They are smart, too. deal with that. They are smart, too. They are eager to change. They're just They are eager to change. They're just They are eager to change. They're just forgotten. forgotten. forgotten. So, I have hope for kids, but in order So, I have hope for kids, but in order So, I have hope for kids, but in order not to have a blind hope. I think we not to have a blind hope. I think we not to have a blind hope. I think we should all remember our teachers and should all remember our teachers and should all remember our teachers and help our teachers and parents so that we help our teachers and parents so that we help our teachers and parents so that we can help our kids. can help our kids. can help our kids. I absolutely love that answer and I know I absolutely love that answer and I know I absolutely love that answer and I know that sentiment is shared by many many that sentiment is shared by many many that sentiment is shared by many many people listening. Um, God bless the people listening. Um, God bless the people listening. Um, God bless the teachers and they need help, support and teachers and they need help, support and teachers and they need help, support and information because now they they turn information because now they they turn information because now they they turn on not yours but most podcasts they're on not yours but most podcasts they're on not yours but most podcasts they're just scared. just scared. just scared. They're so scared they hear these They're so scared they hear these They're so scared they hear these doomerism. doomerism. doomerism. They hear the dooms say or they say, They hear the dooms say or they say, They hear the dooms say or they say, "Oh, don't worry. It's utopian." Neither "Oh, don't worry. It's utopian." Neither "Oh, don't worry. It's utopian." Neither of these messages can help our teachers of these messages can help our teachers of these messages can help our teachers and if they're not helped, our kids are and if they're not helped, our kids are and if they're not helped, our kids are not helped. not helped. not helped. >> Couldn't agree more. >> Couldn't agree more. >> Couldn't agree more. >> Yeah, couldn't agree more. Fifi, thank >> Yeah, couldn't agree more. Fifi, thank >> Yeah, couldn't agree more. Fifi, thank you so much for taking the time out of you so much for taking the time out of you so much for taking the time out of your incredibly busy schedule. I'm so your incredibly busy schedule. I'm so your incredibly busy schedule. I'm so glad to hear your father's okay. And glad to hear your father's okay. And glad to hear your father's okay. And that is also part of your schedule, that is also part of your schedule, that is also part of your schedule, taking care of your parents, kids, and taking care of your parents, kids, and taking care of your parents, kids, and all the rest to come educate us on this all the rest to come educate us on this all the rest to come educate us on this thing that's not just important, it's a thing that's not just important, it's a thing that's not just important, it's a major wedge of where we're at and where major wedge of where we're at and where major wedge of where we're at and where we're headed. And I I share great we're headed. And I I share great we're headed. And I I share great optimism with caution even more so on optimism with caution even more so on optimism with caution even more so on the basis of what you shared today. And the basis of what you shared today. And the basis of what you shared today. And also thank you for teaching us more also thank you for teaching us more also thank you for teaching us more neuroscience uh as we went along uh neuroscience uh as we went along uh neuroscience uh as we went along uh because these machines are informed by because these machines are informed by because these machines are informed by the brain and the brain is informed by the brain and the brain is informed by the brain and the brain is informed by these machines and this is the world these machines and this is the world these machines and this is the world we're living in and uh I have great we're living in and uh I have great we're living in and uh I have great optimism in no small part thanks to the optimism in no small part thanks to the optimism in no small part thanks to the fact that you exist in this world and fact that you exist in this world and fact that you exist in this world and thank you for taking the time to come thank you for taking the time to come thank you for taking the time to come here to share. I I know many people are here to share. I I know many people are here to share. I I know many people are very grateful. So thank you. very grateful. So thank you. very grateful. So thank you. >> Thank you. Andrew and I really >> Thank you. Andrew and I really >> Thank you. Andrew and I really appreciated this conversation. It's a appreciated this conversation. It's a appreciated this conversation. It's a civilizational moment. civilizational moment. civilizational moment. >> Thank you for joining me for today's >> Thank you for joining me for today's >> Thank you for joining me for today's discussion with Dr. Feay Lee. To learn discussion with Dr. Feay Lee. To learn discussion with Dr. Feay Lee. To learn more about her work, please see the more about her work, please see the more about her work, please see the links in the show note caption. If links in the show note caption. If links in the show note caption. If you're learning from and or enjoying you're learning from and or enjoying you're learning from and or enjoying this podcast, please subscribe to our this podcast, please subscribe to our this podcast, please subscribe to our YouTube channel. That's a terrific YouTube channel. That's a terrific YouTube channel. That's a terrific zerocost way to support us. In addition, zerocost way to support us. In addition, zerocost way to support us. In addition, please follow the podcast by clicking please follow the podcast by clicking please follow the podcast by clicking the follow button on both Spotify and the follow button on both Spotify and the follow button on both Spotify and Apple. And on both Spotify and Apple, Apple. And on both Spotify and Apple, Apple. And on both Spotify and Apple, you can leave us up to a fivestar you can leave us up to a fivestar you can leave us up to a fivestar review. and you can now leave us review. and you can now leave us review. and you can now leave us comments at both Spotify and Apple. comments at both Spotify and Apple. comments at both Spotify and Apple. Please also check out the sponsors Please also check out the sponsors Please also check out the sponsors mentioned at the beginning and mentioned at the beginning and mentioned at the beginning and throughout today's episode. That's the throughout today's episode. That's the throughout today's episode. That's the best way to support this podcast. If you best way to support this podcast. If you best way to support this podcast. If you have questions for me or comments about have questions for me or comments about have questions for me or comments about the podcast or guests or topics that the podcast or guests or topics that the podcast or guests or topics that you'd like me to consider for the you'd like me to consider for the you'd like me to consider for the Huberman Lab podcast, please put those Huberman Lab podcast, please put those Huberman Lab podcast, please put those in the comment section on YouTube. I do in the comment section on YouTube. I do in the comment section on YouTube. I do read all the comments. For those of you read all the comments. For those of you read all the comments. For those of you that haven't heard, I have a new book that haven't heard, I have a new book that haven't heard, I have a new book coming out. It's my very first book. coming out. It's my very first book. coming out. It's my very first book. It's entitled Protocols: An Operating It's entitled Protocols: An Operating It's entitled Protocols: An Operating Manual for the Human Body. This is a Manual for the Human Body. This is a Manual for the Human Body. This is a book that I've been working on for more book that I've been working on for more book that I've been working on for more than five years and that's based on more than five years and that's based on more than five years and that's based on more than 30 years of research and than 30 years of research and than 30 years of research and experience. And it covers protocols for experience. And it covers protocols for experience. And it covers protocols for everything from sleep to exercise to everything from sleep to exercise to everything from sleep to exercise to stress control protocols related to stress control protocols related to stress control protocols related to focus and motivation. And of course, I focus and motivation. And of course, I focus and motivation. And of course, I provide the scientific substantiation provide the scientific substantiation provide the scientific substantiation for the protocols that are included. The for the protocols that are included. The for the protocols that are included. The book is now available by pre-sale at book is now available by pre-sale at book is now available by pre-sale at protocolsbook.com. protocolsbook.com. protocolsbook.com. There you can find links to various There you can find links to various There you can find links to various vendors. You can pick the one that you vendors. You can pick the one that you vendors. You can pick the one that you like best. Again, the book is called like best. Again, the book is called like best. Again, the book is called Protocols, an operating manual for the Protocols, an operating manual for the Protocols, an operating manual for the human body. And if you're not already human body. And if you're not already human body. And if you're not already following me on social media, I am following me on social media, I am following me on social media, I am Huberman Lab on all social media Huberman Lab on all social media Huberman Lab on all social media platforms. So that's Instagram, X, platforms. So that's Instagram, X, platforms. So that's Instagram, X, Threads, Facebook, and LinkedIn. And on Threads, Facebook, and LinkedIn. And on Threads, Facebook, and LinkedIn. And on all those platforms, I discuss science all those platforms, I discuss science all those platforms, I discuss science and science related tools, some of which and science related tools, some of which and science related tools, some of which overlaps with the content of the overlaps with the content of the overlaps with the content of the Hubberman Lab podcast, but much of which Hubberman Lab podcast, but much of which Hubberman Lab podcast, but much of which is distinct from the information on the is distinct from the information on the is distinct from the information on the Hubberman Lab podcast. Again, it's Hubberman Lab podcast. Again, it's Hubberman Lab podcast. Again, it's Huberman Lab on all social media Huberman Lab on all social media Huberman Lab on all social media platforms. And if you haven't already platforms. And if you haven't already platforms. And if you haven't already subscribed to our neural network subscribed to our neural network subscribed to our neural network newsletter, the neural network newsletter, the neural network newsletter, the neural network newsletter is a zerorost monthly newsletter is a zerorost monthly newsletter is a zerorost monthly newsletter that includes podcast newsletter that includes podcast newsletter that includes podcast summaries as well as what we call summaries as well as what we call summaries as well as what we call protocols in the form of one to protocols in the form of one to protocols in the form of one to three-page PDFs that cover everything three-page PDFs that cover everything three-page PDFs that cover everything from how to optimize your sleep, how to from how to optimize your sleep, how to from how to optimize your sleep, how to optimize dopamine, deliberate cold optimize dopamine, deliberate cold optimize dopamine, deliberate cold exposure. We have a foundational fitness exposure. We have a foundational fitness exposure. We have a foundational fitness protocol that covers cardiovascular protocol that covers cardiovascular protocol that covers cardiovascular training and resistance training. All of training and resistance training. All of training and resistance training. All of that is available completely zero cost. that is available completely zero cost. that is available completely zero cost. You simply go to hubermanlab.com, go to You simply go to hubermanlab.com, go to You simply go to hubermanlab.com, go to the menu tab in the top right corner, the menu tab in the top right corner, the menu tab in the top right corner, scroll down to newsletter, and enter scroll down to newsletter, and enter scroll down to newsletter, and enter your email. And I should emphasize that your email. And I should emphasize that your email. And I should emphasize that we do not share your email with anybody. we do not share your email with anybody. we do not share your email with anybody. Thank you once again for joining me for Thank you once again for joining me for Thank you once again for joining me for today's discussion with Dr. Fay Lee. And today's discussion with Dr. Fay Lee. And today's discussion with Dr. Fay Lee. And last, but certainly not least, [music] last, but certainly not least, [music] last, but certainly not least, [music] thank you for your interest in science.