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

Full transcript

I think the biggest thing humanity never learns is the older generation lamenting about the future generation. As if the future generation doesn't know anything, they're rude, they're forgetting the past. But if you look at arc of history of humanity, by and large, we advance for the better. I'm not denying the atrocities, I'm not denying the setbacks, I'm not denying this, But fundamentally, I'm an optimist in humanity. I look at kids, they're curious. Of course, they get massively entertained by this technology, but they also are starting to use it. What I worry about are teachers and some parents, because I think our society today, and especially Silicon Valley, are not doing them a service.

We're forgetting about them. Hey, everyone. To celebrate the launch of my new book entitled, Protocols, I'm pleased to share that I'll be hosting three live events very soon. The first live event is in New York City at Radio City Music Hall on September 17th. The second event is in Los Angeles at the Dolby Theater on October 8th. And the third live event is in San Francisco at the Masonic on October 28th. At each of these events, I'll be discussing topics from the book. And my favorite part, taking questions directly from you, the audience. To get tickets, you can go to HubermanLab.com slash events and use the code protocols to get early access.

Again, that's HubermanLab.com slash events and use the code protocols to get early access to tickets. Welcome to the Huberman Lab podcast, where we discuss science and science-based tools for everyday life. I'm Andrew Huberman, and I'm a professor of neurobiology and ophthalmology at Stanford School of Medicine. My guest today is Dr. Fei-Fei Li, a computer scientist and professor at Stanford and one of the pioneers and luminaries of artificial intelligence and computer vision. As you all know, millions of people use AI chatbots to look up information every single day. And of course, many people are concerned about AI, where it's going and how it might replace certain human jobs or degrade our experience of life in one way or another.

Today, we discuss from a neuroscience perspective what intelligence really is and the ways that AI can and is being used for good, meaning to truly enhance learning, health, and to enrich rather than diminish the human experience. We start off by talking about how human brains of all ages learn new information, what rules the brain follows in that process, and how AI, because it is based on the content of the internet, both resembles and falls short of what human brains can learn. And we discuss exciting uses of AI and robotics in medicine. To be clear, Fei-Fei acknowledges and addresses the many valid concerns about AI, but as the director of the Stanford Institute for Human Centered Artificial Intelligence, her goal is to make sure that humans and humanity at large are represented in where AI goes next.

As you'll soon hear, Dr. Fei-Fei Li is an extraordinary scientist and educator. She has been called the godmother of AI for her ushering in of AI technologies, but also for her insistence that the ethics and benevolent uses of AI stay central to AI and robotics. So whether you are young or old, Today's conversation will inform and empower you to understand and use AI in ways that truly benefit you and enrich your life. Before we begin, I'd like to emphasize that this podcast is separate from my teaching and research roles at Stanford. It is, however, part of my desire and effort to bring zero cost to consumer information about science and science-related tools to the general public.

In keeping with that theme, today's episode does include sponsors. And now for my discussion with Dr. Fei-Fei Li. Dr. Fei-Fei Li, welcome. Thank you, I'm excited to be here, Andrew. Yeah, this is a long time coming, and you are a luminary in this AI field, but I also consider you a neuroscientist and computer scientist, and we share a common path through vision science, and so I'd like to- And fellow colleagues. And fellow colleagues at Stanford. So I'd like to start in vision. What is so special about vision and seeing and light as it pertains to AI and where it's all going?

because I think for most people, those probably sound like very divorced themes, but actually that's where it all starts. Yeah, I see vision as a cornerstone of intelligence in almost two parallel way. One is what evolution has taught us. You know, what's the evolution of vision and animal intelligence and human intelligence? The other one is computer vision and AI, what that relationship is. So I'll go into each. Evolution, I always say that 540 million years ago, animals saw the first light. These are simple sea ocean animals, trilobites and the cousins. And before that, there was very little sensing. Around that same time, tactile and haptics was starting also to emerge in animal bodies, but there was no hearing, there's no smelling, there's no, but there's absolutely no nervous system.

But the first photoreceptive cells created a evolutionary force that propelled animals to evolve because sensing the external world changes your self perception, changes the way your relationship with the external world. To put it simply, if you seek, you can see food, It changes your life, right, from an evolution point of view. And you become someone else's food, and also you're actively seeking food. You're actively seeking mates and all that. So really, because of sensing and perception, evolution took an incredibly accelerated pace in terms of animal speciation. studies have told us that 10 million years after the first light for animals was what we call the big band of evolution or Cambrian explosion of animal speciation.

And fast forward, I think vision has always played a huge role in not only in the early evolution of animals, but as well as advanced intelligence and how that emerged. You and I are both vision students and scientists. It is estimated half of the cortical activities in human brain is involved in visual function. Children were first visual before they were verbal in development. So vision really to this day plays a central role in both the evolution of animal intelligence as well as in the daily life of human life. Now, in parallel, vision as a discipline or as an area of artificial intelligence really played a pivotal role in what we see as this modern AI moment in a couple of ways. First of all is the algorithms, the neural network algorithms. Neural network algorithms were first computer scientists start dabbling that in the early 1950s. And Andrew, you might remember what's What's happening on the neuroscience side in the early 1950s is that neuroscientists like Hubel and Wiesel were starting to record visual cells in mammalian brain and starting to realize there is a hierarchical structure of nervous cells that stack against each other and pass neuroinformation across these hierarchy.

And it goes from, you know, collecting light from retina all the way to recognizing there is a shape in front of you. And that very neural architecture that we see in mammalian brain is also part of the inspiration of neural network algorithm. Now today's neural network algorithm runs on hundreds of billions and even trillion of parameters. It has the complexity that departs from what we recorded in the mammalian brain or the visual pathway. But the origin is very close to each other about half a century ago, a little more than half a century ago. That's one aspect of vision's contribution to AI.

There is another aspect of vision's contribution to AI that is also pivotal, which is through big data is that that comes closer to my own work is that AI around the century was a field of machine learning. A lot of different labs, different research scientists were trying out different algorithms. And it's not just neural network. There are other methods, jargon words like Bayesian methods, support vector machine methods. It doesn't matter what these methods are, but it's a explorative phase that we're trying to get these algorithms to work so that we can empower the machine to read or to see. A group of us, computer vision scientists, were struggling with these algorithms. And I was a very young faculty, first-year faculty, 2006, at Princeton, and my students and I are looking at these algorithms and how little data were fed into these algorithms to learn.

So I turned to cognitive neuroscience literature, namely vision literature, and started to study how much humans learn, how much humans can see, and the numbers were incredible. were by age six can learn tens of thousands of different object categories, and the exposure to visual world is also massive, right? Babies can see most of the time the moment they're born. So they're inundated with this big data. So we conjectured that the lack of data was a huge part of the reason that's the lack of progress in AI. So we took a departure from everybody else who are really focusing only on algorithm and said that we need data.

We need data to drive these algorithms. So long story short, we let this image that project that collected the first ever internet scale large dataset for the field of artificial intelligence, but really through the field of vision because ImageNet is a collection of 15 million images and the goal of ImageNet was to drive machines to recognize everyday objects, you know, microphones, cups, chairs. And that work converged with the advances in neural network algorithm, as well as in GPU computing. And by 2012, that work, that convergence of the three elements of modern AI became the defining moment of what modern AI is.

I recall somewhere around 2012, it seems, there was this debate at this vision course at Cold Spring Harbor that was held every other summer. Like could a computer learn to recognize specific cases as well as humans? Now I think most people would say computers are actually much better at it than humans are, even though you have these super recognizer people who are exceptional at this. Could you tell us how is it that this technology went from a state basically where it would confuse you and maybe a cousin or even someone that looks somewhat like you. Or kooky. Or to the point where now it is exquisitely precise.

How do we get here? I wanna definitely double, triple click on the convergence of this technology. I think around the second decade of 21st century, So like you said, around 2012, the huge convergence was the capability of TPU computing, which basically accelerated or parallelized computing so that you can have more flops going through algorithms, right? You need that speed. Then you also have a, after many decades of research, neural network algorithm is getting more mature. Starting, as we said, 1950s, people start to create these very simple algorithm that behaves similarly to neurons, but much simpler. Neurons, as you know, are very complex.

But here the idea is that you have one unit of node that takes some input and outputs another input and within it is just a function, a very simple function. So you stack them together, that's what neural network is. But by the time it's in the, after, you know, around 2010-ish, the maturity of these algorithms have gotten to a level that it's becoming really good. But also, last but not the least, the recognition of big data. internet definitely feel that it made data more available. But the reckoning moment of, well, big data needs to be part of that equation, we need to use big data to drive these algorithms to learn these patterns. So these convergence of these three things really set off the revolution of AI. The specific moment is also worth mentioning because you mentioned face recognition.

Is this image that challenge my lab put forward, that starting 2010, after we collected this humongous data set, at that point, GPU was not yet mature. And we put out a public challenge for the research community for multiple years in the role and invited people to solve this major computer vision problem called object recognition. The task was very easy. We have a data set of 1,000 different categories of objects. And this data set is more than a million images large. It's what we call the testing data set. And the task for the algorithm is, I'll show you a picture. You have to name the main objects inside.

And if you guess right, you get a point. if you guess wrong, you don't get a point. So that image, that challenge, we later, a couple of years later, benchmarked human performance by a very smart graduate student at Stanford, and that was roughly 4%. So random chance will be one over a thousand, right? So 4% for humans is not that bad. The first few years, machines were not as good as humans. The turning point was 2012, the convergence of neural network, image data set, and GPU. Even that year, even though the error rate was cut to, by the way, the human performance error rate was 4%, sorry, I need to correct that.

The error rate was cut down to the teens. It wasn't where human performance was. So this is looking at images and assigning a- One out of a thousand labels. Got it. Yeah. But 2012 was so momentous that year because the error rate from previous algorithm dropped a lot by this neural network algorithm. And we know in the research community when something this drastic happens, it means an inflection point. But it still took another three years, I remember, by 2012, 2016 for the algorithm to beat humans in naming a thousand objects. Could I ask you where this 4% error is coming from in this very smart graduate student? Is it that they don't recognize the objects or it's a recognition against time pressure like they have to they're being fed images fast enough that occasionally they do an incorrect assignment. I don't think the time pressure was the main issue even though for a graduate student to do this I don't think they want to do this forever but I think you know that the human brain as you know has limited memory whether it's long term or short term. So retaining the patterns of a thousand object classes, even if some classes you're familiar, is not that easy. So I think there is the confusion. And also, for example, different species of dogs gets really close. And that's a challenge.

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Absolutely. I think that what you see is the flogging got open and every sub-area of AI, whether it's speech recognition, sound recognition, natural language processing, which is more than recognition, vision, all areas got really a boost in terms of the technology. We have colleagues at Stanford who are studying whale sounds using machine learning and AI now, and speech recognition is another area that did so well in the early days of this AI revolution. And of course, the technology continues to advance. By the time the Transformer paper was was published around 2016, 2017, it quickly showed that it is even more powerful than the early ImageNet, AlexNet algorithm.

There it was not the field of computer vision that made the next big progress. It's the field of natural language processing. So because the recipe hasn't changed, now we have an even more powerful neural network algorithm called Transformer. But we have even more data on the internet from at least more readily available data on the internet in the form of texts. And now we have more powerful GPUs. So companies like OpenAI and Google quickly rallied beyond this very important technology. And it That still took about five years from 2017 to 2022 to get to the chat GPT moment in natural language, but that's yet another step forward.

So I think for people who are not computer scientists nor neuroscientists, the natural human experience will perhaps resonate with them and maybe I can just frame my question through that lens. So when a child learns that there's something called a kitty cat, they go, oh, cat. Then they usually drop the kitty part. They may say kitty and then they learn cat. And if they have enough interactions with a cat, they'll realize what a cat is, even if they see it from the side, from the back. And eventually if they see a tail that looks a little bit like a cat and it's behind some books, you say, what is that?

They're very likely to say cat, even if they've also seen foxes and other animals with tails, just based on their experience. they're making a probability judgment. And that's essentially what AI can do. That's essentially what machine learning can do. But it seems to me that there's a key moment that had to happen in the progression of, from calculators to the AI we have now, to be able to see an image of a tail and make the reasonable assumption that it's most likely a cat if it's indoors or something like that, because foxes generally aren't indoors, this sort of thing. So at what point did machine learning and AI gain the ability to do kind of contextual learning and come up with the most likely assignment of what something is?

Because it's one thing to show apples and bananas and oranges, they're all fruit. Okay, you could distinguish them, you could distinguish those from cars and trucks, et cetera. But this object constancy piece that if something is moving, you're only getting a partial image. This isn't what most people will think of in terms of intelligence, but it's part of what makes our brains and the brains of other animals, but especially our brains, so remarkable and why we consider ourselves probably the smartest species on earth and if not the smartest then certainly the best at technology development. So when did AI achieve this and how was that scripted into these computers to allow them to do that?

So let's just take the problem very, you have described it so well, this problem of seeing a glimpse of a cat tail and being able to recognize cat, right? or a sign of high likelihood there is a cat. The interesting thing is Andrew, generations of machine learning computer scientists have tried this problem. So before today that machines can reliably do it, there were different algorithm. You can imagine a common sense way of thinking about this is, oh, maybe we should recognize all the furniture to know it's an indoor, so it's unlikely to be a fox. So though there are rules like that, that it was built into previous generations of algorithms.

There are also rules like, well, let's only, instead of guess it's a cat, let's only guess one out of the 10 potential animals, cat being one of them. That limits the search or guess space, and that would help. help. So many ideas were tried. So when was the moment it became much more reliable? Is this current era when the huge data that these algorithms have learned, let's take Gemini or GPT, have learned, really created the capability in the machine's learned space, so much knowledge, so much pattern, that when presented with this more or less maybe new-ish photo of a cat's tail sticking outside of a bookshelf, that pattern activated the learned, what we We call learned weights or learned parameters that put the machine's assessment or guess of this object closer to what it has seen, which is likely to be a cat tail or a jest tail because there's just so much data.

This is where, Andrew, as a neuroscientist, I think we depart from the human brain because Because that child who learns about what you say, kitty cat, will not have the chance to download the internet of images of cat. They likely have seen three cats, 10 cats at most, but yet they're able to identify that tail as a cat tail instead of a fox tail through a different kind of learning pathway. These are the mysteries we haven't fully solved, but I do want to point out that departure between today's AI algorithm that is learned with a humongous amount of data versus how humans have evolved.

If we continue to ascend the kind of hierarchy from simple object recognition to what you you and I would call higher order brain functions, like moving more towards what most people, they hear the word intelligence and they just think, oh, it must be some higher order thing. Creativity, imagination. Let's go to a middle step and then a much further step out. So staying with the cat example, if a computer or a child learns to recognize a cat through the tail, the whole thing, whatever, and they've seen a cat move, it's a very new world at that point for that brain, child or that computer because now they know that the cat generally moves in the direction of its head not its tail these are simple simple learning rules right it might go after mice but it might run from dogs maybe yes maybe no and on and on so it seems that the next layer up in terms of quote-unquote intelligence is to assign likelihoods of direction to move directions not to move other objects that that object is likely to interact with.

This all sounds very basic to people, but like this is how brains learn and this is how machines learn. So when was the next sort of big inflection in terms of like giving a computer, AI, a picture of a cat and saying, animate this cat for me, make it move like a cat without giving it any specific instructions about how to move its limbs, et cetera, but I would imagine that was a pretty quick, but a remarkably important transformation in this whole thing that we call AI, because that's what a brain does. Yeah, so it's really funny you asked this, you put it beautifully, I never thought it to put it in this way for a public audience, but that moment came when video become part of the training data.

So see, again, I'm going back to the training data. So around 2023, very shortly after chat GPT moment, moment, multiple research teams start to put video into the training data. Of course, I'm not going to get into the nuance of the algorithm. There's a little bit of changes and variations. So remember, January 2024, Sora was released. And that's where people see a video can be generated, literally what you just said. can then type and say, a cat running towards a mouse, and then a few second clip would be generated and there would be a cat moving its leg in a plausible way running towards a mouse.

At that time, there were still mistakes, still even today, it's not perfect, but things have gotten a lot better, but that opened the floodgate of video generation, as you described it. So what happened there? What happened there is actually not as revolutionary as you might think, because the bottom line is it's still data. As a scientist, I can tell you there are all kinds of algorithm tweaks and changes and improvements and all that, but overall, if you zoom out, it's still part of this great neural network era, right? what happened is that we're now able to process video data in a way against some clever engineering, tokenize it, whatever you call it. And now we can generate these short clips of videos, which is frames put together that look like plausible cat movement. Now you might ask, does the algorithm know the muscle structure of a cat's legs so that when the algorithm shows that the cat is moving in a plausible way with the paws, you know, in a sequence, I would say the algorithm doesn't.

But what it does have is so many videos, especially cat on the internet, so many videos of cat. So it learned what it should look like. So in a way, humans do that. Most of us without education will not know how muscles move in cats. I still don't know. You know, our colleagues in medical school might know, but we have just got so used to seeing cats moving this way. We have a plausible idea of how cats move. So that is similar. That's how similar AI is. It's the statistics. It's the large amount of data that showed you what is the plausible generation of cat movements.

Yeah. So when people have heard almost certainly that the brain is a prediction machine, it's a learning machine. This is exactly what you're referring to. Let's go to a really far out there aspect of brain function that we know exists in humans, which is thoughts and creativity. Now there are probably rules for thoughts and creativity. They're a little bit harder to tack down than examples from the visual system. Like if it's a tail and it's indoors, it's likely a cat, this kind of thing. But they're there. The rules are there. use Apple as an example, we could have gone from low level seeing an apple to mid-level seeing apple always drop, not fly off, at the highest level.

What is the equation that governs the apples movement? Right. So that's a sending to a higher order, more reductionist analysis. What do you think about the idea that while AI is indeed intelligent, it can do things that reins can do? even, well, certainly things that individual human brains can't do. We know this by virtue of beating humans at chess and this sort of thing. The idea right now, as I understand it, is that AI is trained on the internet. Images, discussions, videos, songs, but that's not all of human cognition, right? So are there aspects of AI that are, whether or not it's chat or it's Claude or even the most powerful, not yet released machine learning and AI tools that don't have access to features of human brain function yet because they've never been uploaded to the internet, at least not in a way that the AI can pull out.

For instance, you could put a symphony there and it follows certain rules of music and mathematics and sound, like that makes sense. But you have thoughts all day long and I have thoughts all day long that don't quite mesh with language in a way that I can just type them out on the internet. Stay with me here. I know this is a long question, but I feel like this is the one thing you are perfectly poised to answer. And I've been waiting to ask you this for a year and a half since I saw you in Utah. In the world of art, we have this thing called abstraction, right?

And occasionally somebody will come up with a painting or a drawing that it doesn't look like anything specific. This happens in music too, where you just feel something like there's like a fundamental rule or an emotion associated with it. Like they've tapped into some aspect of brain function but you can't say what it is. I feel like this is the sort of thing that is complicated for AI or for me to understand how AI could do because you can put that piece of art into AI and say, you know, what fundamental feature of human experience does this reveal? And it only has access to what's on the internet.

So how can you capture a complex constellation of feelings and experience with AI? That seems to be the gap for me. I'm sure we'll get there with AI, but I'm not seeing from neuroscience to AI in any kind of direct way, the same way we could ratchet through visual motion, sadness, happiness. You could pull out a lot of things, but it's hard to get to these higher order abstract representations that can't be spoken or written down or drawn. If I just say, give me your example of whatever, nostalgia for your childhood home. You could write about it, but those are just words.

It's not, I can't understand your experience at a first person level. Totally. Andrew, I know you put a lot of thoughts into this question and I think it's a very important question. And let's, let's peel this one step at a time. First of all, TLDR, short answer is I agree with you that we do have to be very careful recognizing what AI can do, is likely to do, not conjecturing over 100 years or whatever, and recognize what you just said are these extremely nuanced, personalized, hard to characterize or not even captured human cognitive behaviors. And because they were not captured, they then they were not uploaded on the internet. And we don't have today's AI doesn't have a way to do that. So when you call internet, which is the source of AI's data, let's be very clear. What is internet? Internet is not some random thing. Internet is the biggest collection of human behavior. In multimodal forms, let's break it down further. Internet has the world's population typing on it for many, many, at this point, multiple decades. That typing is a sensing mechanism that captured everything from teenager chitchat all the way to deep scientific articles who got digitized and get uploaded, right?

So that capturing human language is what internet is super good at. Then internet captures images. How? Because we now have digital cameras that's so prevalent in smartphones and digital cameras so that humans love taking photos from, you know, the cat in your house, to selfies, to beautiful, you know, BBC captured photos. Those also got uploaded in our digital sphere. On top of that, there's videos. Videos now has sound and has movements. That also got uploaded to our digital sphere. On top of that, there's music. We're not even getting into the legal discussion of copyrights, but let's just table that aside. I'm just talking about the forms of data, the speeches and singing and music and orchestra, that also got uploaded into the digital sphere.

So now we have created this humongous library of human knowledge in words, human behavior in videos, human expressions, or even nature's whatever in sound. And now AI gets trained on that. That is why it's so powerful. This is why especially in the words front, that AI can recognize patterns, can synthesize patterns because so much of this is already there. But the thing that you just talked about, that when, let's say Picasso had that incredibly profound thought about that particular way of expressing that portrait of the young woman, that thought has never been captured. In fact, as neuroscientists, if I ask you, which brain area did that all come from?

You don't know, right? Is it BRCA? Is it V1? Is it motor? Is it prefrontal? We don't know. Maybe it's diffused everywhere. Because that thought is so personalized, so special, you can call it creativity. You can call it emotion. You can call it whatever you want. You can call it Cat 231, whatever name you can give it. That thought is not captured. Therefore, it's not on the internet. Therefore, AI has not seen it. So that is where humans still remain so unique. But we also need to give credit to AI. Because AI has learned so many things, it can combine information in highly creative way.

Did you remember Move 37? This is AlphaGo, right? Right? Yeah. Move 37 has symbolized AI's creativity. I think it's both true, but can be taken out of context, because that was a game when AlphaGo was playing Lisa Dole. And I think it's a third game out of the five games that AlphaGo as a computer algorithm made a move that the human masters of Go never thought about. And that is an incredible move, right? because it really humans collectively, these are the masters never thought about it. But if you really go deep into what AI did there, it was because first of all, goal is a highly mathematical game.

It has very clear mathematical objective, very clear mathematical rules in terms of move. So when AI having a bigger compute and ways to retain how many moves it can remember, it was able to do things that human brains don't typically do. So is that co-creativity? I think it is. But we do have to recognize that's a special kind of creativity. I was talking to an incredible mathematician of our time, and I was asking him about the unsolved problem mathematics and how AI can contribute to that, and he was very positive. He said there are many problems in today's mathematics. As hard as they are, even as, say, a field medalist, I probably have forgotten there are known methods in math that can solve these problems, because I have a human break. I don't know all of math's solutions in the past hundreds of years, even if I were a field medalist. So AI can help us to solve these problems. But as a mathematician, he was also telling me, he said, I don't know if AI can solve all of math problems because some of these math problems require solutions that have not been invented, that will push creativity to a whole different level.

And this is where, you know, we should be curious, is it going to be a human creativity? Or AI would go through its iterations of improvement and get to a point of creativity that humans don't have? Or is it a combined creativity? My current conjecture is hybrid, is that humans working alongside AI would help us to solve these problems whose solutions have yet to be invented. And then what you said, especially you touched on emotion, is even more personalized. This is not necessarily logic. This is not necessarily deductive reasoning. This is maybe Andrew, you look at this cup and say it's a great cup.

What if it evoked an emotion in me, a childhood moment, that a great cop might mean something that only me and my best friend share? That is a completely inaccessible piece of information in my brain that is never uploaded on the internet. no matter how mighty AI is today, cannot access that. So that my reaction to this cup and potentially what I would do with it because of that piece of memory can be completely different. You can call it creativity, you can call it expression, you can call it storytelling, you can call it in many ways, but that's where AI cannot access.

I feel like at some point in the not too distant future, computers will have access to our brain activity in non-invasive ways. So what, you know, like I might even imagine in five, 10 years, I'm wearing something on my head right now, you can't see it. It's a very, very fine hair net. Hair net makes it sound like it was whatever, like some electrodes that are just there on the outside of my skull, not bothering me. Sensing my activity inside the brain, maybe also sensing my heart rate, autonomic activity, how alert I am. And comparing that yes to what I'm saying and what I'm doing, this is all totally within reach and it's going to happen.

You and I both know this and it's probably already starting to scare people but let's keep it benevolent, right? There's this world where a computer that I own and I'm not worried about data getting out or anything like that, we've managed that problem, is sensing all these aspects of me and is picking up on the fact that yes, what I say might be important, but there are aspects of my internal state brain activity that I'm not even aware of. And I can decide to collaborate with this aspect of me and say, let's come up with a really interesting picture that I've never seen before, but comes from some experience of mine that's important based on whatever. And it could reveal that to me, because it has access to unconscious features of my brain activity. I think this is very likely to to happen in the not too distant future.

And perhaps if people thought about it within the bubble of their own experience, like this isn't immediately going to the internet or it's not gonna be used against them, you're actually learning about yourself. Of course. And I feel most people have an inherent interest in what's going on for them, also with other people, thank goodness. But they're, I think, amazing. Like I would love to know why I trip up in certain ways and don't have the best day or why some days I have the best day or where ideas come from in me, what states I could, you know, kind of elaborate on, but I'm not gonna know how to do that except, okay, one cup of coffee, good, one and a half, a little better, two is too much.

If I sit, like right now, if you think about how primitively we go about this, it's kind of crazy, it's crazy. And everyone has a different method, and we all try and get this right, and then you've aged enough by the time you get it right, that then you have to update it. And like, we're probably not getting the most out of our biology and our brains at all right now. No, this is why I keep saying this is why it bothers me when people talk about AI. Some people make it sound like it's replacing humanity, but what we really, what you describe is about enhancing and augmenting humanity, right?

This is where it doesn't even have to go as sci-fi as a small hairnet accessing your brainwaves. AI learning your patterns of writing can already help you to be a better communicator, a more effective communicator, a more efficient communicator, and that is an empowering capability that we could unleash in today's AI. I think one of the most important thing, Andrew, that as a neuroscientists and also faculty, we know agency is so important for humanity. You know, that boils down to motivation, agency, and dignity at every individual level. And I think we need to recognize that we need to think about AI as a tool that helps us in our agency. It should not take away our agency and people who lead in today's AI should not try to talk like that. This work will take away agency from people. Yeah, I think people who are very familiar with the technology, whether it's computers or it's biology or any technology, cars for that matter, we, they become such nerds of that thing that we forget that it can be scary to people.

and that the languaging around it is essential. It is. And I remember a time in the early 90s, I'm sure you remember this too, when genetic testing was viewed as this thing like, would you want to have it? Would you want to do a blood test? Because oh my goodness, you might see something that could really scare you. And that discussion is happening now around, you know, self-elected MRIs and things like that. None of which people have to do. But I come from the stance, like more information is better, but I've come to understand that not everyone feels that way.

Some people don't want to know. They don't want to know. Yeah, but they should have the choice. In the meantime, we should have enough public education and communication to let people know the pros and cons, but not to deny them the choice and also not to take away, you know, and say, well, since you don't understand this, let me decide for you what's good. That is not good, you know, and the rhetoric around AI right now is getting really skewed because people who know what this is tend to talk down at the public. It tend to talk, whether the motivation is a positive one or negative one.

There is a rhetoric of, you guys don't know what this is, and I will tell you, and I will make you whether happy, safe, whatever it is, and I will decide for you, these are not healthy and not helpful. Yeah, I agree. And I think, you know, one of the reasons for starting this podcast was to showcase the scientists and physicians who really have a benevolence about them. And they have no interest in dumbing things down, but they do have an interest in people understanding things. And many people would feel that, you know, health information is among the more important things to understand. Absolutely.

Well, thankfully you're breaking the mold of the, you know, the phenotype you just described. And there are a few others, but you've been doing this at the highest levels, really encouraging people to think about the collaboration that is AI, the agency that exists and whether to use it or not to use it and so forth. One of the agency I do think is important for individual humans, whether you're a student, a teacher, doctor, a policymaker, is learn about this. Not necessarily learn about how to code. I don't think it's necessary, depending on your job, right? So for example, if you're an artist or if you're a teacher or doctor, you don't necessarily need to code.

But learn about what this technology is. Learn about how you can use it yourself to empower yourself, your learning or your work or your expression. By learning, one feels more in control. By learning, you're less scared of trying. And by learning, you retain that agency and that dignity, because at the end of the day, no matter how advanced technology is or medicine is, as humans, we want that benevolence that helps us to live better, keep our dignity, and make our community better. I'd like to take a quick break and acknowledge our sponsor, AG1. I'm excited to share that AG1 has just launched their newest formulation, AG1 Pro.

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Again, that's drinkelement.com slash Huberman to claim a free sample pack. The idea that technologies can be connectors as opposed to separators, I think has to sit at the center of the discussion. Yes. And we all know who they are. There are several of them, but the big names in this field, they are also in a developmental process where they're learning how to be public facing and it happens very fast. Like the microscope is on them and the cameras are on them. And so every subtle dysfunction is magnified. So I like to think that they will mature quickly enough to realize that, and I think they are, that some are, that the public needs to hear the correct, the true message, but in a way that makes them understand.

That's the kind of dirty secret of medicine and academia, that you break this mold. I like to think I break this mold, is that there's a power in not sharing how things work, but it doesn't serve anybody well. At the end of the day, like you pull back the veil and let people in and people feel safer. Yeah, there is a power in not sharing. There's also a power to say, just trust me, I will tell you. And neither as educators, that is we don't go to our lectures and say, just trust me, you know, two plus two equals four.

we actually say here's how you break it down and learn about it so next time you can do it yourself, right? I also think that especially your your podcast is so important as part of public communication and education of knowledge. I also think that we need to hear voices of different different background, right? So because there are plenty of scholars, technologists, builders, thinkers out there who have been dealing with AI, using AI, thinking hard about how to use AI to empower people. And these voices are so important. Well, certainly I'll take names of people to host in addition to you.

But since you're here, I'm going to go next to something that I think most everybody would agree would be a wonderful thing if it existed. and it's already starting to happen, which is the use of AI to augment health discovery, treatment of disease, and so on. So using the AlphaGo example from before, and people surely still remember the cat example, those just follow certain rules. AlphaGo is a very complicated set of rules, but if you learn them, there's a constrained set of rules. With the cat, it seems unconstrained, like infinite possibilities, but it's constrained enough that machines and humans and learn it really well.

When you start getting into medicine, there are rules of medicine, there are rules of science. You have a question, you pose a hypothesis, you test the hypothesis, you try and rule out your hypothesis and so on, like the scientific method. And in medicine, every field has its methods. We observe, we observe disease, we observe who recovers, we have a case report, we will randomize controlled trials. So there are rules and the internet knows these rules. So LLMs can be used to mine health information very well because there are constrained rules. But I think you and I both know, because I also consider you a biologist, that the rules of biology are still revealing themselves to us, which is not to say that the dermatologists, neurosurgeons, and oncologists don't know what they're doing, but they're doing what they're doing within a constrained set of rules that they learned.

And even if they continue to learn and update them, it's every month it seems now that a discovery comes out that violates the rule. Like I learned that action potentials are unitary. they always look the same, you either fire or not. But there was a paper not but 12 years ago that showed that the shape of an action potential can vary quite a lot, it was published in Nature, everyone saw it and then no one wanted to deal with it. It's just too much, it changes the rule. Neurons are supposed to be either graded or all are one. And it's in every single textbook.

So now if I take a bunch of neural activity and I give it the rule, oh well, action potentials can be big, they can be small, in the same neuron, it completely confuses everything we understand about neuroscience. And it just, our understanding of the brain just breaks down to zero. But if you gave AI the rule that it could be, you know, a hundred different shapes of the signal, well, AI could probably do a lot more than even the very, very best graduate student at, dare I say, Stanford or to be fair, MIT or Caltech. I don't think it can do it.

And it can do it like in the duration of this question, which admittedly is a bit long. So I'd like to get your thoughts on how is it that humans in healthcare, the general public and AI can collaborate to help solve disease and ideally come up with new rules for discovery so that we can finally understand our biology at a level that can really change the course of humanity for the better. Yeah, no, Andrew, this is probably, perhaps you touch one of the most exciting usage of AI, which is scientific discovery. And in the case of biomedicine, scientific discovery directly connects to human health and diseases.

I think we're, we're ready for a complete re rewriting of how scientific discovery can be done. Because for ages, I don't even know how long it relies on smart humans retaining what they have learned from other smart humans and, and, and doing things at the speed of our own muscles, I guess, you know, most likely. Of course, there's like super colliders and all that, but by and large, the ways of doing scientific discovery, human brain or scientist's brain are the only central character in this process. Now, we have a new tool whose brain that can retain humongous amount of information, can help us synthesize knowledge can go across disciplines in ways that you and I cannot go. So for example, we happen to be both in the vision neuroscience, AI domain, I know nothing about, you know, olfactory zero, like, I don't even know how to spell most of probably these words in that our colleagues know, right?

So it's so hard for our brain. But now we have a tool that can break open. So I think that we need to change. We need to use this tool. I was just thinking 150 or I don't know exactly when years ago, electricity changed everything in our life, right? I'm sure that's a moment where we're thinking about how the changes, the opportunities, the scary moment, I think we have to come to reckon that scientific discovery is one of the most exciting opportunities for AI and for health, right? How information can be synthesized, how information can be presented not only to clinicians but also to patients, and how patients can participate in that process from diagnosis to treatment is also, there is just so much we can do now.

Yeah, I mean, AI, I won't say AI is better than all doctors, but AI was able to disambiguate vertigo from low blood pressure for me a few months back. And one of the people who got it wrong is a ENT who works on the vestibular system. What information did you provide, just your subjective feeling? My subjective experience over a day or two. Turns out it was a medication that a doctor had prescribed me that I had like a mild but adverse event. And it's a weird thing to step and feel like the whole world's dropping down and then kind of spinning.

And I thought, my goodness, this feels like vertigo, but I remember dizzy and lightheaded are different. So I started looking into that. And sure enough, it was a blood pressure issue. Brought my blood pressure, excuse me, down too low. And, but I consulted, we know some smart doctors. None of these are at Stanford. I will say that, this is the truth. But it was- No, we should just be intellectually honest. It's just remarkable. And when I ran it back to them, they were like, that's really incredible. You know, had you not been on the phone with me and in my clinic, I would have been able to do some additional testing to be fair.

But this was zero cost. It took a morning to know if I drank some electrolytes at what I would have thought would be excessive level, that by two hours later, I would be fine. Now, of course, there's the possibility of a placebo effect here, but two hours later, I was fine. And so it's also very consoling to the patient to have this. And so it's not to say don't go to a doctor, but it's incredible. I mean, this exists now. Yeah, the doctor can use this tooling. By the way, I have a very interesting example, that we have to reschedule this, our conversation, because my father was going through a surgery, right, at Stanford with an incredible surgeon, but the surgery was done by a robot, the DaVinci robot system, because it was a liver surgery, and the surgeon, the incredible surgeon was driving the robot.

So it was a deep human-machine collaboration. After the surgery, I asked the surgeon, I said, do you imagine if, say you've done a million, which is impossible for a human surgeon, but let's collect all of human surgeons for this liver, this type of liver surgery data. Can we possibly train a automatic AI to do this? The answer was not clear. So we went a little bit down the rabbit hole because liver is a very complicated organ. It's extremely vascular, it has a lot of vessels and everybody's liver is very different. So given the reality of how many patients undergo liver surgery per year, even if you aggregate the world's liver patient surgeries, you might not have enough data to train these algorithm.

So this speaks of a very important fact that AI learns from patterns. When the patterns are not abundant, then we have to be careful. We have to know how to use AI or how not to use AI. You know, in this case, that having a human collaborating with the robot is way better than a under-learned robot doing the surgery by itself. But the same issue might be true for surgeons because how many surgeries a surgeon can get trained on. So these are opportunities that humans and AI can totally collaborate with I might reveal the best result, right? Now, the future remains to be seen.

Can we create an artificial simulation of a liver that we can now train infinite possibility? These are all incredibly open scientific possibilities that is waiting ahead of us. But then there are situations like your situation where the vertigo versus low blood pressure probably have been reported so many times that in the database, there's enough of that, that AI has learned that. So we can then now take advantage of that for people who don't have immediate access to doctors. Amazing, is your father's surgery went okay? It did, it actually- I'm happy to hear that. He lost 10X less blood than a typical surgery, surgery, thanks to the laparoscopic capability of a robot.

I'd like to talk a little bit about some features that we think are uniquely human that may or may not be. You'll tell me these are genuine questions, not loaded questions. And then I'd also like to get educated on how AI is structured to allow these things to happen. For instance, intuition. We all like to think of intuition as this like mystical, very like, it certainly is powerful, but this thing that like we own that no one can take from us that can't be mimicked kind of thing. But I could also break intuition down to be, well, it's my experience over time, it's a data set coupled to some bodily and brain sensations and some prediction cues.

Like the last time I felt this, this happened. The last two times I felt that, and yeah, things didn't work out that way, so I'm gonna go this way. I mean, that you could assign these rules to a computer, but there are other aspects of our deeper self, if I can refer to them that way. Like we don't know where intuition is mapped in the body, could do an imaging experiment, but you're not going to collect all the neurons and hormones and everything simultaneously. So we don't really have like a location or even a network to point to. Like things like creativity, intuition, premonition, the idea that, you know, you really sense something is coming on, but it hasn't happened yet.

What sorts of rules can AI get that could give it these sorts of capabilities? And here I want to talk about it in the context, if you will, of energy. So whatever this thing is, it's like mitochondria driving cells more around one thing versus another, the same way fear or happiness would, right? We were just talking about energy. But within AI systems, and I'm not a computer scientist, within AI systems and GPUs, Can we actually allocate more energetic flow through particular learning rules? So we could tell maybe someday, based on everything you know about my sister, who I love, what is your intuition about how our brother-sister relationship will evolve over time?

And what is your sense about what would be great for us to do perhaps for our birthdays this year? That's different than before. And it only has access to the internet. Can it actually become sort of mind-like or mind-body-like and come up with a sort of sense of what might actually be worthwhile? Or does it just need more and more prompts? Like, it's just gonna keep asking me questions. So I'm actually doing the work. Such a interesting question, Andrew. So I do want a separate intuition from creativity for the sake of argument here. And maybe we'll come back to merging.

So let's talk about this intuition of given my sibling love, what's going to happen, right? Is it really intuition? So today when you go to a AI chat bot, you're going to prompt, you know, I'm a Stanford professor and a neuroscientist, give me this information. That is already called context. I don't know if you call it intuition, but because you gave that piece of information, the AI's answer for you is already gonna be different if I type that I'm a 14-year-old teenager loving race cars. Even if we ask the same question, it'll have customized answer. That is a mathematical, I wouldn't call it energy, I wanna be, that is just a mathematical fact of how these algorithms takes these contexts and tailor the outputs.

And it's called context. It's not that deep in computer science. That's one type of intuition that is fairly shallow because you already are able to use language to describe it. Or you can say I'll upload an image that also is already expressible and then AI gets it. The deeper intuition you just said is like, you don't even know where they come from, right? Like, is it because I smell something? Is it hormones? Is it, you know, the mixture of mood is in my breakfast? That intuition, what would AI do with it? That is what I would say is inaccessible. There's no sensory apparatus yet that can glean that data.

and feed it to, not only AI, cannot even feed it to, you know, for example, sometimes as a couple, you might have moment that you're just rubbing each other in the wrong way. Never, no, I'm just kidding. Yeah, of course. So if you're really familiar with each other, you kind of can sense it, but you can't quite tell, maybe you just leave, quietly leave that person alone. So that means whatever that intuition that person has, they could not even express it in words or a gesture to give it to another person to use as a piece of information. So when you cannot even access that, neither a human, a different human nor a machine can do anything about it because there's no access to that.

Highly individualized intuition, There's no technology that can do that till you say we put brainwave collectors or skin conductance sensors. By the time we do those, maybe they become accessible. So we have to recognize. So what I'm trying to say here is what's not very deep is the data accessible, either through language or through picture or through imaging or through brain waves, whatever it is, it needs to be an accessible piece of information. If it's accessible, then if we have collected enough of that, you can train machines with or if a machine is well trained, it can, like you said, in a private way, forget about privacy breach, but in a private way, the machine can probably use it.

What I'm trying to do, Andrew, here is not to make it sound mystical, but try to give it a scientific process to describe if it were to happen, how would that happen? Yeah, because pattern recognition based on big data sets and rules get us a long way. That's what I'm hearing. And earlier we were talking about where doctors fail and robots and machines perhaps do better or they collaborate to do better than either one alone. You know, as a neuroscientist, you spend a lot of time looking at cells at some point in your career. And it's amazing how like the electrophysiologists for decades, if not longer, you develop an intuition.

I'm not really a physiologist, but I learned to recognize cells based on like kind of these things that were not written up in any papers, but like if there was kind of like a straighter edge along this thing and it had a certain shape and roundness, like I tell you right now, that's a transient off alpha cell in the retina. Eventually we developed genetic labels to reveal that that was true in every case, but then you also saw some that didn't fit the rule. Machines can learn that, computers can learn that, and with all that information from all those papers, now we have a pretty good parts list of the retina.

Cool, that works. And then you can apply rules, like they fire this way, they fire that way. Okay, I'm good with all of that. What I think I was trying to get to with intuition, and I probably didn't give the best example, is like, what are some internal states of humans that are really hard to imagine machines could recapitulate, but perhaps they can, like motivation. Do machines, do robots get motivated? We have rules of motivation, like when I'm really motivated to do something, we call that urgency, a state of urgency, and I might move faster to do it, less activation energy, you say, let's go, I stand up a little bit faster.

Machines could like go quicker in a certain direction. But can you say, hey, I want you to seek this out, but with a heightened level of urgency? Or are they just constrained by the mathematical rules they can work with? So you could build this in the mathematics. So certain things, whether you call it motivation or in machine learning world, we call them objective functions, you can build certain things into math. For example, now you go to, say, chat GPT, it has different mode, like think deeper mode, or like give me a quick answer mode. If you don't know how this works, you're like, oh, this is interesting.

One has more urgency that gives me a quicker answer. The other one has to go deeper into the search, right? And take longer to give me the answer. So as a human, if you anthropomorphize it too much, you might call it urgency or motivation, but the truth is this is just a different kind of objective for the algorithm. You can say, well, the one that think quicker has a time limit or token limit, the one that thinks slower can activate a different part of the model that would take longer. So it become actually mathematically very dry and not that deep.

But for a human, you can call that motivation or urgency. But let's go deeper, because you're asking something deeper than that, right? Is that there are cognitive states that humans, you truly just, whether it's motivation or urgency or fear or love, that is very hard to access and express and do machines have it today? No. Let's make it very clear. We tend to imagine that the machines feel or they're not. They don't have that data, they don't have that mathematical objective function, so they can say when the machine says, I'm sorry you're so sick today, it's very different from how your friend says it to you, because the machine said that because it has learned through pattern. When someone tells it, I'm sick, you should say, I'm sorry, you're sick instead of I'm so glad you're sick, because that data exists. Whereas your friend who hears that, they genuinely want your well being, they love you, they want they They don't want to see you suffer.

They have that empathetic feel of, oh, well, if you're in pain, I've experienced pain. So that's, it's not mirror neuron, but it's at least a memory of what pain means. The machine doesn't have any of that. So we do need to make sure we differentiate that. So a lot of what drives human, what takes human, what triggers human is doesn't exist in today's machine. We operate fundamentally different from today's AI and we have to recognize that, respect that. And this is where public communication is so important. We cannot confuse the public about this. I'd like to take a quick break to acknowledge one of our sponsors, David. David makes protein bars unlike any other. Their newest bar, the Bronze Bar, has 20 grams of protein only 150 calories and zero grams of sugar.

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I feel like people assume there's an emotion, a person or whatever inside of the AI chatbot because we're so language oriented. It's talking to us, it's writing things to me and we do that more now than we did 30 years ago. Certainly we've gotten very accustomed to receiving communications in fairly deprived language. Texts are not like extensive prose, languages changed, modes of communication have changed more deprived as opposed to more enriched. But at some point soon, I'm guessing faces are going to start to enter the picture, no pun intended. Like how far off are we from like, if you or I were to text the other person, oh, see you on campus for coffee next week at this time.

How soon is it that that text is going to be actually a photo or video-like image of you just talking to me, telling me that? I mean, this would be trivial to do nowadays. The technology is there, but we have to now look, zoom out a little bit and think about the social parameters, the legal implications. I mean, humans are capable of doing a lot of things with our tools, but we don't do all of them. For example, today, any car manufacturer can say, every Friday, the brake doesn't work. This is a trivial technology. There's a clock in the car's computer, and it just turns off the brake every Friday.

But we don't do that because it has deeply bad implications to our human society. That's where rules comes in, laws come in, social norm comes in, morality comes in, And I think this is where we exit the pure technical discussion of AI and need to enter the social discussion of AI. Well, let's do that because one thing that I know about biologists or technologists is they like to go fast because it's exciting, it's the next edge, right? I remember long ago I had a friend who was studying viruses and ways of putting, these weren't infectious disease viruses, These were viral vectors for getting genes expressed as experimental tools in animals.

But there came the opportunity to actually put the rabies virus, a modified rabies virus into Drosophila, into fruit flies. Oh my God. Now that's fine and good, in my opinion, if you are absolutely certain, 100% certainty, that that is a nonfunctional version of the rabies virus because you can put other cargo in there and do all sorts of important experiments on, believe it or not, disease and things like that. But if there's just one fruit fly that somehow is an escape or you get the actual rabies virus, there's the potential it makes with another and then they eventually find the others.

I don't know if this would be a dominant or recessive situation, but now you have fruit flies with rabies and those things move really fast. So there's a reason why you don't do that experiment. But it was exciting for them to think about and then they got denied, right? For good reason. I was grateful, right? to any biology department, you're going to see some fruit flies flying around. They love vinegar, by the way, you know, so they're coming to your salad. But the point here is that technologists love to go fast. They love sensing that next edge of things. So how is it that between government, the general public, technologists, and now I'm just leaving out biology here and medicine, how is it that that conversation can occur in a way that's going to satisfy each of those groups enough, not hold us back because we're also supposedly in an AI race right now, so that that warrants going faster, not slower.

How do you think about this? I mean, Andrew, this is why I returned from Google eight years ago back to Stanford and started the Human Center AI Institute. These are profound societal questions we had to face, and back in 2018, there was no chat APT, but as an AI scientist, I knew that this is only going to accelerate. This is why I went to my colleagues and university leadership and say, let's put a framework. But it's not just my framework or Stanford's framework. The entire society in every way need to wake up to the social implication. Because we have done this in human history, whether it was cars or airplanes or biotech, is that it's multi-dimensional with multi-stakeholders, right?

There is the professional norm. For example, you guys as biologists don't sneak into the lab and try to put rabies into drosophilus or fruit flies because that's a professional norm in your ethical training. There is industry rules, for example, IRBs, every human subject experiment today on university campuses are subject to the IRB regulatory framework so that we can look at this. And then there are laws, regulatory laws, depending on if it's applied to humans versus crops. So AI has to go through the same, right? We need to have our professional norms. We need to have education. Computer scientists are not educated in ethics and societal studies.

They're starting to. I mean, this is why a number of universities, including Stanford, are feverishly putting that part of curriculum into our education. Now, those are the norms in education, but we also should work with the government and different kind of governments and society have different kind of norms and traditions and heritage and look at where the regulatory measure should apply. AI, for example, crossing biology, FDA, I think that's a very important area to look at how AI should be used to help, but also guard rail to harm so that we can avoid harm. What I would not like to see is one person or a few people coming from industry and telling everybody what to do.

I think that would be dangerous because market forces are different from societal norms and culture and heritage are different from education and ethics and these are multi-stakeholder problems to solve together. I love that answer and it's something that's very, very timely right now. This aspect of our conversation is surely going to expand over time, but you bullseyed it. I'd like to get your thoughts on how the human brain is being shaped on machines and how machines are are being shaped by our understanding of the human brain. So first question first, many people, parents and kids are thinking, oh, like my kid is never gonna learn anything now.

They're just gonna look everything up on a chat bot. But if you look back in the history of learning, similar arguments were made about calculators and computers and the typewriter and on and on. However, it is an interesting question that this hardware that we have in our heads evolved to process physical things in the world, light sound, it smells, et cetera. And then it got this really cool piece up front, the prefrontal cortex that can learn learning rules and can update those learning rules. So like if anything, we were gifted with a learning to learn machine and updating learning. So that's how kids can adjust and use LLMs.

So I, as a generation that grew up with the personal computer showed up, granted I grew up in Palo Alto, it was like, here's Pong and there's the Apple IIe and like we had in the, and I think, Oh cool, like the brain can mature around technology, collaborate with technology in a way that I think my life has been greatly enriched by it. But I think the smartphone and perhaps the camera smartphone combination as people like Jonathan Haight have pointed out have created a situation where most people, like they love these technologies for the ease and convenience, but we're all a little bit more aware now or a lot more aware that we're giving up something too.

And that there are traps that people in particular young people can fall down. So what is the very optimistic meh and very pessimistic view in your mind, if three flavors actually exist there, of how young brains can be enriched or unaffected or can be harmed by AI as it exists now? Let's just kind of stay with what we've got. Great question, Andrew. and the answer almost fall out of our previous conversations because you use the word motivation and I was using the word agency. The absolute bad outcome is that our young generation, their agency and human level motivation of learning and living is taken away by tools.

So doom scrolling, passive watching of shorts, all this are not helping agency, human agency. Learning fundamentally, respecting the hardware you're talking about, takes time, takes effort, sometimes takes some pain. That is just how our brain is. It doesn't matter how transistors move, our neurons move in certain ways, our chemistry, our hormones moving certain way. So for young generation, no matter how the society will be different, jobs will be different, our human body needs to go through a deeply developmental phase where learning needs to happen. And that agency of learning, that motivation of learning cannot be taken away by anybody, should not be taken away by humans, nor should it be taken away by machines.

That would be my concern, which is that if AI is not used right, the agency and motivation is taken away. Though we are left with generations or generations to come who have not properly developed the brick. The other kind of danger is in the name of agency and motivation, the tools are denied to our students because we're worried you cheat. or worry you only got your answer from chat GPT, that is very bad as well. Because with the proper agency, proper motivation, proper ways of using this tool, we can go a lot deeper with AI than we have ever learned.

I was just thinking about, I was a pre-med student for a while. Man, organic chemistry was hard. I remembered trying to learn the molecules, their orientations. But the TA hours are too short, or it overlaps with my other class and my professors only have a certain number of office hours. It was just a struggle to learn that, right? If today I were to have an AI companion, I would ask so many questions about organic chemistry because I know where I'm stuck. I have the motivation to learn. I just needed to a guidance that would be such a powerful tool for me to learn.

So that we should not deny students from. So both things worry me is either denying the tool or taking away agency and motivation. Of course, the flip side is great is let's find a way to keep our children and students motivation and agency. find a way to give them the access and the right way of using these tools then this generation this coming generation and many generations to come will be way smarter than us because they are super powered. I love that answer. I have great faith in neuroplasticity and the younger generations too. Even our own, I know we're old. Not so, let's give ourselves some credit. Plasticity does exist throughout the lunch. Even our own neuroplasticity, right? Like I find AI a great tool for my learning. I mean for me it's been a remarkable discovery of what it can do, but I tend to approach it from the position of consumer if I know nothing about something and from the position of creator if I have some knowledge set inside of whatever it is I'm asking. Well I actually have another thing that Stafford undergrad taught me something last year and I realized before chat GPT sometimes I get lazy. If I have a question I ask the person I think is smart next to me. Now I realize I should not ask lazy questions because it's so much easier to get information before you spend somebody else's time to ask something that's too lazy and AI is forcing me not to be too lazy.

How essential is the specificity of the prompt to getting the best information out of AI? Prompting is very important. And that's a skill, right? That is a skill. This is why public education is so important. This is why education is so important. I would love to see our schools, K-12, teaching prompting. Here's a quiz. Who is humanity's best prompter? I'm going to flunk this quiz. Socrates, if he were alive, because that is the method of prompting, right? Think about it. What is Socrates' method? Is prompting and seeking truth by asking questions, and we should go back and teaching kids that.

And taking a walk while you have those discussions, which actually is a good transition, perhaps, to this notion of embodied AI. It's a world apart to attach a face speaking to hearing words. My good childhood friend, I hope you'll meet soon because you both would benefit from the conversation so much and I just want to be a fly on the wall. Dr. Eddie Cheng, chair of neurosurgery, bio engineer and he studies speech and language. He and others have figured out the transformation of neural activity to control of the larynx and pharynx and he's brought people essentially out of locked-in syndrome so they can speak.

For the first time in 10 years, he has this patient who was sadly paralyzed and he could speak through a computer. He has others, many examples of these in fact. But the incredible thing is when he started putting an iPad next to this person who is, one woman in particular who's wheelchair bound, they had a video of her at her wedding. So they knew her voice, they knew her emotive patterns, they knew a bit about how she moved her body as well. And she now speaks through an iPad next to her frozen real face, but she can interact with the world and it can interact with her in a completely different level of depth than if it were just a microphone, the sort of Stephen Hawking thing.

And it's constantly being updated through machine learning. What, now also paying attention to the people she's speaking to and their responses. I mean, this is embodiment. It's on a 2D flat, 2D screen admittedly, But this is like an exponential leap over just robot sound or even accurate sound alone. It's not just embodiment of people, it's embodiment also embodied AI goes into robotics, right? The next frontier of AI as I have been saying is beyond language because again, humans develop first pre-verbally. Evolution took 500 million years without verbal. communication and also the world would in the right version would be a lot better place with robots helping humans.

Could you give me some examples? I love this idea, but again, I realize I'm probably a little too deep into the technology rabbit hole and it's probably scaring some people. So robots, we've got self-driving cars. Actually the Waymo always stops for me and my puppy, my beautiful little six month old puppy. How could you not stop when he wants to cross the street? But a lot of people won't stop. They'll almost run us over in the morning. The Waymo is very respectful. Waymo has to learn the rules, right? Exactly, exactly. So there's benevolence there that doesn't always exist in humans.

But where do you think this is going to show up first? And what's it gonna look like? Like if we zoom out 12 months from now? 12 months is a little bit too fast for robotics. Two years, three years. I would say if we zoom out 30 years. 30 years. Okay. I'm not saying that's the first time robots hit the street. We already have robotic cars. I'm just saying it takes longer for especially a hardware also involved technology to manifest. But I would say hopefully in you and my lifetime, I would love to see robots being part of our society helping us.

For example, I'm a single grown-up child taking care of two very advanced-aged and very sick parents, and they happen not to speak English either. The amount of work I do is incredible, right? So I would love to have help. It doesn't take away family's responsibility, it doesn't take away love, it doesn't take away the necessary communication. But the physical labor would really, certain part, I would love to get help. We live in the state of California. What is the one thing we all experience? Traffic. High taxes. Certain part of California doesn't have traffic, but wildfires. Oh, yes. Right. Who is fighting these wildfires?

Maybe humans in danger of rescue, natural disaster is not a great idea, right? So my family and my parents happen to have enough means, but I was just thinking, elderly living alone, how do they go get grocery? How do they go get medicine? Now there might be some shipping we are starting to see, but what if they want to go? you know, for a walk or a while ago in a park. So there are just so many things that, oh, by the way, you're in the School of Medicine. We don't have an excess of caretakers. We have a shortage of caretakers.

Our nurses are deeply fatigued and overworked. I was literally in the hospital with my dad for the past month and just watching the amount of work nurses do. We know that on a given shift, nurses walk miles to fetch things, get medicine. There's just so many, can you imagine robots helping, right? So there are just so many ways that our society can be structured and can benefit from help. Oh, I love these examples. So many spring to mind based on what you described, crossing guards. Yeah, you imagine with video that somebody who's homebound because of age or illness could navigate to the store and pick things off the shelf.

It doesn't have to be so disconnected that they just program and it comes back or that could be an option too. Yeah, I think that we have to revise our notions of what this picture looks like. Because I think there are a couple of things about robots and computers that scare people. One is their physical hardness, right? And so the way we share space with them is very different than the way we share space with other things. Of course, I'm not thinking, oh, like you cuddle with a robot, although some people might think that that's not my mindset. But I am thinking like, okay, if I had a robot that could fold clothes, vacuum, water the plants, and feed my fish, although I like to feed my fish myself, I really enjoy it, love seeing them eat.

I love being tactile, literally in touch with them, they'll eat from my hands. Does your puppy like your fish? He does, he has his own fish tank. I just got him some tropical fish. Yeah, right in front of his little thing. He's taking care of them. Well, he looks at them. He's not equipped to take care of them yet. I don't think it. Unfortunately, there's not enough prefrontal cortex in him. So, and he's a kind, but he's a bulldog. They're not the smartest breed. They only have a few learning rules, but they're very kind. But if you want a dog that can take care of a fish tank, you probably need like a West Highland Terrier or something like that.

More prefrontal cortex. Point taken. But the idea here is if one robot is doing one thing And another robot is doing another, it feels like a lot of hardware in my life. And I think that's kind of how people feel. But you could imagine a multi-morphic robot. Do you know Baymax? I don't. Disney's robot, probably 10 years ago, 15 years ago, it's Google the image that this is the white medicine robot, a healthcare robot that is very, not fluffy, it's very spongy, like it feels like a big balloon. You might like that. More contours and more multitasking from the same robot feels like a world that I could adjust to more quickly than the idea of my world filled with robots.

Again, Andrew, I think as we imagine the future and we talk about how we imagine the future, I keep coming back to the word agency. Humanity should have the agency to decide how we imagine this. It cannot just be a company or, or I don't know, an investor decide that the world should be filled with metal like robots, right? Like our society should be collectively, proactively imagining. And one thing I worry in this AI rhetoric is that the public is putting a position of being reactive when it feels some people are just deciding and the multi-stakeholders are not participating in this designing the future together.

Like with your example of your father's surgery, to cross the robot with the physician, right? If we cross a problem where there's a vulnerability with a robot that clearly makes things better, the picture changes in the right direction. So I'm thinking of a few examples off the top of my head. Like, I think most people would agree that if their kids could walk themselves to school and home, it would be great, but you worry about safety. But if a robot was really a good guardian of your kid, to the point where they could alert the authorities or maybe even physically protect your child, that would be awesome.

Give them more agency in the world. You think about some of the darker, but nonetheless, unfortunately, real predatory behavior online. Parents can only oversee their kids' behavior so much, kids are only aware of so much that's happening, but you could imagine kind of an avatar in there with you that's really advocating for you that can spot things and keep predators at bay. Here you go, that's a great startup idea. Like, that would be cool. But here's what's missing, I think, from the picture. For me, I remember seeing this incredible guy, I know people, some say he was kind of prickly, but this incredible guy walking around downtown Palo Alto when I was a postdoc and when I was a kid growing up working at the Palo Alto Toy and Sport world, and that was Steve Jobs.

No shoes, kind of looked like a hippie. Yes, he shouted at people at work, and probably HR wouldn't look too kindly upon him nowadays, but he understood that these things we call computers needed to have rounded edges. They needed to fit kind of seamlessly in our pocket. They needed to have Bob Dylan on the landing page or whatever so that it softened the relationship to technology. Some people say, well, it went too far. It was a Trojan horse, but I don't think so. Somebody who really understands human nature to allow these, like what are clearly going to be benevolent collaborations between robots and humans to happen, because as you pointed out and with total respect to the technologists that have built AI and the scientists that do amazing science, there's a hardness to either the way they're being presented or what they're capable of sharing that is a real separator.

Yes. And I'm not a therapist, but if I could like wrap my arms around them, I'd be like, listen guys, you're the smartest people in the room, guys and gals, to be fair. You're the smartest people in the room, but people don't like you because they don't understand you. And they're, maybe you need a collaborator to help you share your vision in a way that isn't gonna allow the press. Cause the media is guilty of building this chasm cause it's like these technologists, they're coming for us. I think that's a total trick of media too. That's just to put money in their pocket.

Like there's a lot going on right now. So who's the Steve Jobs or the Stacey, Stacey, whoever it is. I mean, could be a man, could be a woman, someone who really understands human nature. There are many of them, there are many of us, you know, I mean, Stanford started Human Center AI Institute. Well, there's you. There's you. Okay. But there are many. Yeah. There are plenty of entrepreneurs who are doing incredible startups on AI for drug discovery, AI for healthcare, AI for aging, AI for mental health. These people care about AI, right? There are many designers and product managers who are trying to ... I do think the megaphone is too much focused on people pumping their chest and talking about tech in a certain particular way.

So, you know, even this podcast is making a positive difference, I hope, is to put that human angle, the rounded human angle, human perspective, human future into these conversations. I don't feel despair, Andrew. I'm an educator. I'm a builder, I'm a technologist, I see many people around me, including my entire startup. These brilliant young technologists could join any startup or company they want, but they can't do world labs because they want to empower people, right? So I see many people, but I don't think there's enough, you're right. I don't think the public discourse is balanced right now, and there's too much extreme rhetoric, either in terms of extreme doomerism and lack of safety. It's just freaking people out. Or extreme utopian, as if technology can do no run. And then that's disingenuous. People would say, well, okay, you're the halves, of course, you say that. So I think we should come to the middle and talk about what this technology is, how to use it, how we can collectively have that agency to guide the future. Yeah, one thing that was pointed out to me by one of my podcast colleagues, that was should have been obvious, but wasn't. And clearly, this is something that you, you, for lack of a better word, you embody, among many other things, is people don't really wanna hear stories about machines, but people love hearing that some person cured their dog's cancer, or their child that was experiencing crazy symptoms.

They had no clue, the doctors had no clue, and their fingertips, AI, solved the problem. These are the stories that really need amplification, because I think that we can relate to them and they're beautiful stories, they're incredible stories, but they're not getting nearly as much attention as the other stuff and it's a challenge. Traditional media doesn't really care about the long arc of things. They are on a 12 to 24 hour cycle, but other names perhaps of people who are really trying to talk about the benevolent use of AI, these collaborations that we should be aware of. Stanford HAI's newsletter, our website, our seminars.

We promote a lot of those work. I would love to learn more about your startup because you don't pick projects haphazardly. So what is the project? What's the goal? So my startup co-founded with a couple of other co-founders is called WorldLabs. We co-founded it at the beginning of 2024. It really is, for me, kind of my life's work. We both come from vision and the recognition of there's more beyond language intelligence is what really motivated me to think hard about what's the next chapter of AI Frontier. And we recognize that unlocking spatial and physical intelligence is really the next chapter that it's not excluding languages.

Of course, the language technology is incredible. It's where we can devote more time to build models or build eventually products that can help unlocking capabilities in spatial intelligence like generating 3D, 4D worlds that are deeply useful for creators, for robot training, for architecture design, or to enable those interactive environments, whether you're talking about healthcare usage or education usage or robotics usage or industry usage, these capabilities goes beyond language, per se. And so WorldLabs was founded based on that premise. We are still a young company. We're very much a model-focused company where we're building this foundation model and we're started by a lot of PhDs, but now we're starting to build products.

And so it's still the beginning. It's very exciting. And as a technologist, I feel deep in my heart, I'm a builder. Maybe it's because also I'm an immigrant. So that, that rolling your sleeves up and just get in with the young generation that is so incredibly smart and just build something from scratch is just so exciting. I recall a time not, but what, 15, 20 years ago when there were cars driving around, taking images, still driving out, still driving out, taking images. But I imagine that there, and there are certainly aerial views as well, but you imagine little tiny drones like the type that could fly through a neuron and just kind of look at everything or so to speak or drones picking up information about every nook and cranny of the fjords in Norway. Has that been done to sort of map the three-dimensional world?

First of all let's not make it sound scary that drones are getting to people's homes and properties. I think that the ability to capture imageries of the world is really rapidly advanced right? Like our cell phones are incredible sensors. They're not drones, but people take a lot of photos. And of course, our camera technology has improved. What WorldLabs is doing is not just taking real world images. It's we allow people to imagine what's in their minds. As long as you can type a sentence or show a picture or a sketch of what you imagine, We try to turn that into worlds and environments.

Why is it useful? Because entertainment industry will use it, design industry will use it, robotics industry very much would use it for training environments and all that. So the combination of capturing what's in the real world as well as capturing what's in your imagined world is the new frontier. If you don't mind, I'd like to just take a couple of more minutes and talk about this moving from imagination to something. Because this is Los Angeles, it occurred to me that a lot of people write scripts, and then they try and get their movie made. But with AI in theory, you could take a script and give it to AI, and it could make the movie in theory, right?

Going from words to pictures to video, and you could maybe edit it a little bit here and there where it needed help of course. Has that been done? Has a successful movie been made start to finish using AI? So this is a very nuanced topic. This is where we're also getting to people's wearing of AI and creativity when if not careful it might sound like we're taking away from storytellers and creator's job, right? So let's separate this job conversation from the technology conversation a little bit, even though they're entangled. Technology has advanced enough that taking scripts and generating shots, video shots, is getting really good.

We have seen short movies, even almost feature-length films being assembled by AI tools we have. And there are many companies, US companies, Asian companies, creating technology. But what remains deeply human and that is important is every part of storytelling and story creation, there are humans behind it with their unique emotion story, technique, how they see the world, how they move the cameras, how they characterize people, characters, a lot of that is what Hollywood and novel writers is about. So how do we meet the human need and human desire of storytelling with modern tools is actually a challenge because there is a fear, very much coming from Hollywood, that AI is taking over, and storytellers and actors and screenwriters, the jobs are being impacted. And I think it is. But how is it being impacted? What are we doing about it?

who is working in a constructive way. This is not my industry per se, but I would love to see much more nuanced work in this, and also nuanced public discussion about that. But I do think just like healthcare, we were talking about how AI can rapidly change and disrupt the old ways of doing healthcare. I think AI is absolutely changing the way we're doing storytelling. So one story, speaking of which, I have a co-founder whose name is Ben, and Ben and I met with Ben Affleck. So I was joking, Ben meeting Ben, who is also thinking very avant-garde about using AI tools about filmmaking, right?

So having conversations between technologists as storytellers or movie makers at this moment is critical. Yeah. I feel like in every example of technology, there's some crossover point that when somebody who's truly an insider embraces a technology and then it just kind of takes off like Steven Spielberg or something like that, or these are probably aren't the best examples. but like the Steve Jobs, Washington crossover, kind of a designer technology, curious guy, and a real, forgive me to the jobs family, but real computer scientists, right? That merges, these collaborations are really key. Like, so you need an insider and an outsider to do it right because you have to understand both cultures and how to include the industry, the people.

So I really hope, right? Because WorldLabs works with VFX industry as well. It's so important for me that our customers and users feel empowered. It's not that technology should be taking their jobs away, technology should be making their jobs better, super powering their creativity. And that's how I see this technology and that's how I would like to work with the users and customers. It's wild to think that, you know, when I was a kid on California Avenue in Palo Alto, there was this store, Keeblin Shucket, and it was just a photograph store and camera store. Yes, yes. in there, you got your film developed, and there are all these guys behind the counter, and they tell you, I'll say you can rent a long distance lens and this kind of thing.

None of that exists anymore. Everything went digital, you know? But there are still camera stores, so industries can morph, they don't always get obliterated. Yeah, it morphs, people also get reskilled, upskilled. You know, we are working with a lot of creators who are using AI tools because they see where technology is going, and they want to re-skill and up-skill themselves. So I think moments of change is moment of both opportunity and loss. We need to really be thoughtful about that. My last question is about the young generation. How do they feel about AI? Because there is this- How young are you talking about?

I'm talking about kids between the age of seven and 20. Okay, that's literally my guess. Yeah, so I might've asked that question for a reason. How do they feel about it? Are they excited by it? Because there is this phenomenon where like computers come along and your handwriting teacher is getting nervous that people aren't just typing. Now they're all writing with their fingertips and no one's gonna know how to write and we wrote. These stories have been around for a long time about how we're just gonna dissolve into a puddle of our own neurons if we don't embrace the past as much as the future.

And I like to think some of both is what's important. But how do the kids feel? What do they think? This is actually my pet project as an educator and technologist. Everywhere I go, I try to talk to students, parents, and teachers, because I think that is the most forgotten population. Our policymakers and our technologists and our investors, They don't talk about teachers, parents, and students. They all have opinions, and they all have kids, but they don't talk about it. I always have hope for kids, maybe because I'm an educator, because I think the biggest thing humanity never learns is the older generation lamenting about the future generation, as if the future generation doesn't know anything.

They're rude, they're forgetting the past, but if you look at arc of history of humanity, by in large, we advance for the better. I'm not denying the atrocities. I'm not denying the setbacks. I'm not denying this, but humanity, fundamentally, I'm an optimist in humanity, right? So that's where I come from. So if you're a total pessimist, maybe we're already on the wrong footing, but I look at kids, they're curious. That's why they're kids. They're curious. Of course they get massively entertained by this technology, but they also are starting to use it. What I worry about are teachers and some parents, because I think our society today, and especially Silicon Valley, are not doing them a service.

We're forgetting about them. We are lecturing them. We are berating them. We are looking down at them. They are the most important people in our society. We should be talking to them. We should be uplifting them. We should be supporting them. We should be providing resources to them. K-12 teacher or K-16 teachers, they share the most important, critical burden of our society. I'll tell you a real story. November, 2022, chat GPT came out. Obviously I'm an insider in terms of technology, but the first thing I did was emailing the principal of the elementary school my kid was in, I said, I would like to come and guest lecture for your students and teachers.

It's not because I'm so special, it's because I want them in real time to know what's happening because nobody, nobody in Silicon Valley, no investors, multi-billion dollar investment firms or multi-million dollar, multi-trillion dollar companies, when CHAP GPT came out, the first thing is, what about our teachers in the neighborhood? nobody thinks like that. But we need to, we need to be talking to teachers. We need to show teachers, of course they're going to ask the question about what if kids cheat. It's okay they ask those questions. Let's show them, let's work with them and empower them to come up with ways to deal with that. They are smart too. They are eager to change. They're just forgotten. So I have hope for kids, kids, but in order not to have a blind hope, I think we should all remember our teachers and help our teachers and parents so that we can help our kids.

I absolutely love that answer and I know that sentiment is shared by many, many people listening. God bless the teachers and they need help, support and information. Because now they turn on, not yours, but most podcasts, they're just scared. They're so scared, they hear this doomerism. They hear the doom say, or they say, oh, don't worry, it's utopian. Neither of these messages can help our teachers. If they're not helped, our kids are not helped. Couldn't agree more. Couldn't agree more. Fei-Fei, thank you so much for taking the time out of your incredibly busy schedule. I'm so glad to hear your father's okay, and that is also part of your schedule, taking care of your parents, kids, and all the rest, to come educate us on this thing that's not just important, it's a major wedge of where we're at and where we're headed, and I share great optimism with caution, even more so on the basis of what you shared today.

And also, thank you for teaching us more neuroscience as we went along, because these machines are informed by the brain, and the brain is informed by these machines, and this is the world we're living in. And I have great optimism in no small part, thanks to the fact that you exist in this world. And thank you for taking the time to come here to share. I know many people are very grateful. So thank you. Thank you, Andrew. And I really appreciated this conversation. It's a civilizational moment. Thank you for joining me for today's discussion with Dr. Fei-Fei Li. To learn more about her work, please see the links in the show note caption. If you're learning from and or enjoying this podcast, please subscribe to our YouTube channel.

That's a terrific zero cost way to support us. In addition, please follow the podcast by clicking the follow button on both Spotify and Apple. And on both Spotify and Apple, you can leave us up to a five star review and you can now leave us comments at both Spotify and Apple. Please also check out the sponsors mentioned at the beginning and throughout today's episode. That's the best way to support this podcast. If you have questions for me or comments about the podcast or guests or topics that you'd you'd like me to consider for the Huberman Lab Podcast, please put those in the comments section on YouTube.

I do read all the comments. For those of you that haven't heard, I have a new book coming out. It's my very first book. It's entitled, Protocols, An Operating Manual for the Human Body. This is a book that I've been working on for more than five years, and that's based on more than 30 years of research and experience. And it covers protocols for everything from sleep, to exercise, to stress control, protocols related to focus and motivation. And of course I provide the scientific substantiation for the protocols that are included. The book is now available by presale at protocolsbook.com. There you can find links to various vendors.

You can pick the one that you like best. Again, the book is called protocols, an operating manual for the human body. And if you're not already following me on social media, I am Huberman Lab on all social media platforms. So that's Instagram, X, threads, Facebook, and LinkedIn. And on all those platforms, I discuss science and science related tools, some of which overlaps with the content of the Huberman Lab podcast, but much of which is distinct from the information on the Huberman Lab podcast. Again, it's Huberman Lab on all social media platforms. And if you haven't already subscribed to our neural network newsletter, the neural network newsletter is a zero cost monthly newsletter that includes podcast summaries, as well as what we call protocols in the form of one to three page PDFs that cover everything from how to optimize your sleep, how to optimize dopamine, deliberate cold exposure.

We have a foundational fitness protocol that covers cardiovascular training and resistance training. All of that is available completely zero cost. You simply go to HubermanLab.com, go to the menu tab in the top right corner, scroll down to newsletter and enter your email. And I should emphasize that we do not share your email with anybody. Thank you once again for joining me for today's discussion with Dr. Fei-Fei Li. And last, but certainly not least, thank you for your interest in science.