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 the arc of history, of humanity, by and large, we advance for the better. Now, I'm not denying the atrocities. I'm not denying the setbacks. 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/events and use the code protocols to get early access. Again, that's hubermanlab.com/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 chat bots 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 yes, 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
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 ways. 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, trilobytes and their 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, but there's absolutely no nervous system. But the first photoreceptive cells created an evolutionary force that propelled animals to evolve because sensing the external world changes your self-perception, changes your relationship with the external world. To put it simply, if you 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. Fossil studies have told us that 10 million years after the first light for animals was what we call the big bang 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 dabbled with by computer scientists in the early 1950s. And Andrew, you might remember 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 neural information across these hierarchy. And it goes from 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 algorithms. Now today's neural network algorithms run on hundreds of billions and even trillions 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, and that comes closer to my own work. 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 an 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. Humans 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 for 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 led this ImageNet project that collected the first ever internet scale large data set 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 algorithms as well as in GPU computing. And by 2012, that work, the convergence of the three elements of modern AI became the defining moment of what modern AI is.
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 could... or to the point where now it is exquisitely precise. How do we get here? I want to definitely double triple click on the convergence of this technology. I think around the second decade of the 21st century. So like you said around 2012
The huge convergence was the capability of GPU 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, after many decades of research, neural network algorithms getting more mature. You know, starting as we said, 1950s people started to create these very simple algorithms that behave 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 it's just a function, a very simple function. So you stack them together. That's what neural network is. But by the time it's around 2010ish, 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 fueled that. It made data more available. But the reckoning moment of, wow, big data needs to be part of that equation. We need to use big data to drive these algorithms to learn these patterns. So this 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 ImageNet challenge. My lab put forward that starting 2010, after we collected this humongous data set, we at that point GPU was not yet mature, and we put out a public challenge for the research community for multiple years in a row and invited people to solve this major computer vision problem called object recognition. The task was very easy. We have a data set of a thousand 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 ImageNet 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, ImageNet data set, and GPU. Even that year, even though the error rate was cut to... oh 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.
Yeah. But 2012 was so momentous that year because the error rate from previous algorithms 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 to 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'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, the human brain as you know has limited memory whether it's long-term or short-term, right? So retaining the patterns of a thousand object classes, even if some classes you're familiar with, is not that easy. You know, so I think there is the confusion and also for example different species of dogs gets really close.
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I can see the rationale for doing this in the vision domain. But has a similar thing been explored with hearing, with sounds? I mean, as humans, we are amazing at recognizing speech inflection, emotional tone, things like that. But if I had to discriminate, you know, even 15 different sound frequencies, I can tell you as a non-musician, it would be very difficult for me.
Absolutely. I think that what you see is the floodgate 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 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, 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 still took about five years from 2017 to 2022 to get to the ChatGPT 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 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. 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, etc. But this object constancy piece, that if something is moving, you're only getting a partial image—this isn't what most people 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, 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—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 algorithms. You can imagine a common sense way of thinking about this is, oh, maybe we should recognize all the furniture to know it's indoor so it's unlikely to be a fox. So there are rules like that that were built into previous generations of algorithms. There are also rules like, well, instead of guessing it's a cat, let's only guess one out of the ten potential animals, you know, cat being one of them. That limits the search or guess space and that would help. So many ideas were tried.
So when was the moment it became much more reliable? It's this current era when the huge data that these algorithms have learned—let's take Gemini or GPT—have really created the capability in the machines' learned space, so much knowledge, so much pattern, that when presented with this more or less maybe a newish photo of a cat's tail sticking outside of a bookshelf, that pattern activated the learned what 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 just tail, because there's just so much data.
Got it. This is where, Andrew, as neuroscientists, I think we depart from human brain, because that child who learns about what you say, kitty cat, would not have the chance to download the internet of images of cats. They likely have seen three cats, ten 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 the 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 and I would call higher order brain functions, like moving more towards what most people when 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, that 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. And so it seems that the next layer up in terms of quote-unquote intelligence is to assign likelihoods of directions to move, directions not to move, other objects that that object is likely to interact with. This all sounds very basic to people, but 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, etc. 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 and you put it beautifully. I never thought to put it in this way for a public audience, but that moment came when video became part of the training data. So again, I'm going back to the training data. So around 2023, very shortly after the ChatGPT moment, multiple research teams started 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. People 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 the mouse.
At that time there were still mistakes, still to 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, but overall, if you zoom out, it's still part of this great neural network era, right? But what happened is that we're now able to process video data in a way—again, 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 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 cats, so it learned what it should look like. So in a way, humans do that. Most of us, without education, would 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 gotten so used to seeing cats moving this way that 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. If you use apple as an example, we could have gone from low level seeing an apple, to middle level seeing apples always drop, not fly off, at the highest level what is the equation that governs the apple's movement. Right, so that's ascending to like a higher order, more reductionist analysis. What do you think about the idea that while AI is indeed intelligent, it can do things that brains can do, maybe 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, whether or not it's ChatGPT or 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. So, for instance, you could put a symphony there and it follows certain rules of music and mathematics and sound 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 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. And 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. 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 peel this one step at a time. First of all, TL;DR, short answer: I agree with you that we do have to be very careful recognizing what AI can do, is likely to do—not conjecturing over a hundred years or whatever. I 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, then they were not uploaded on the internet, and today's AI doesn't have a way to do that. So when you call internet 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—at this point—multiple decades. That typing is a sensing mechanism that captured everything from teenager chitchat all the way to deep scientific articles that 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. Humans love taking photos—from the cat in your house to selfies to beautiful BBC-captured photos. Those also got uploaded in our digital sphere. On top of that, there's videos. Videos now have sound, have 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, 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 thought come from, you don't know, right? Is it Broca? 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 ways.
Did you remember Move 37?
Right. 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 Lee Sedol, and in I think the third game out of the five games, 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 humans collectively, these masters, never thought about it. But if you really go deep into what AI did there, it was because first of all Go is a highly mathematical game. It has very clear mathematical objectives, very clear mathematical rules in terms of move. So when AI, having bigger compute and ways to retain how many moves it can remember, was able to do things that human brains don't typically do. So is that called 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 of 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 Fields medalist—I probably have forgotten—there are known methods in math that can solve these problems, because I have a human brain. I don't remember, I don't know all of math's solutions in the past hundreds of years, even if I were a Fields 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—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 gray cup 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, and no matter how mighty AI is today, cannot access that. So 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 you know, like I might even imagine in five, ten years, I'm wearing something on my head right now, you can't see it. It's a very fine hair—it makes it sound like 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 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 can manage 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 and 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 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 going to be used against them—you're actually learning about yourself.
And I feel most people have an inherent interest in what's going on for them, also with other people, thank goodness. But I think like 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 going to know how to do that except okay, one cup of coffee good, one and a half a little better, two is too much. 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, we're not. And 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 smart hairnet accessing your brain waves. Just 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 things, Andrew, that as a neuroscientist and also faculty, we know is 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 become such nerds of that thing that we forget that it can be scary to people, and that the languaging around it is essential.
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. They tend to talk whether the motivation is a positive one or negative one. There's a rhetoric of 'you guys don't know what this is and I will tell you, 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 really 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 it's important for individual humans, whether you're a student, a teacher, doctor, a policy maker, is learn about this. Not necessarily learn about how to code. I don't think that's necessary, depend 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.
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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, that there are several of them. But the big names in this field, you know, they are also in a developmental process where they're learning how to be public-facing and it happens very fast. Like, you know, 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.
Yep. 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's a power in not sharing. There's also a power to say, 'Just trust me, I will tell you.' And 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 podcast is so important as part of public communication education of knowledge. I also think that we need to hear voices of different backgrounds, 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 can 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—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 do a randomized control trial. 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-or-none. And the all-or-none—it's in every single textbook. So now if I take a bunch of neural activity and I give it the rule, oh well, you know, action potentials can be big, they can be small in the same neuron. It completely confuses everything we understand about neuroscience and 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 this 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 health care, 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, you know, scientific discovery directly connects to human health and diseases. I think we're ready for complete 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 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. We absolutely—I was just thinking 150 or I don't know exactly when years ago, we electricity changed everything in our life, right? I'm sure that's a moment we were 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 opportunity 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 an ENT who works on the vestibular system.
What information did you provide? Just your subjective—
My subjective experience over a day or two.
Um, turns out it was a medication that a doctor had prescribed me that I had 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, like feels like vertigo, but I remember dizzy and lightheaded are different. So I started like looking into that and then, um, sure enough, it was a—it was a blood pressure issue. It brought my blood pressure, excuse me, down too low. And but I consult—we know some smart doctors, um, none of these were at Stanford, I will say that, this is the truth. But it was—
We should just be intellectually honest.
But 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.
Doctor can use this tooling. By the way, I have a very interesting example. You know that we had to reschedule this 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 Da Vinci robot system, because it was a liver surgery and the surgeon—incracible 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 surgeon, but human surgeon—but let's collect all of human surgeons for this liver, this type of liver surgery data. Can we possibly train an 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 algorithms. 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, having a human collaborating with the robot is way better than an underlearned robot doing the surgery by itself. But the same issue might be true for surgeons because how many surgeries can a surgeon get trained on? So these are opportunities that humans and AI can totally collaborate with and 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 are 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 lost 10x less blood than a typical surgery, thanks to the laparoscopic capability of a robot surgery.
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 mystical, very powerful thing that we own, that no one can take from us, that can't be mimicked. 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, things didn't work out that way so I'm going to 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, who 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, you know, based on everything you know about my sister who I love, you know, 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, given 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 going to keep asking me questions so I'm actually doing the work.
Such an interesting question, Andrew. So I do want to 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 an AI chatbot, 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 going to be different. If I type that I'm a 14-year-old teenager, you know, loving race cars, even if we ask the same question, it'll have a customized answer. That is a mathematical, I wouldn't call it energy. I want to be clear, that is just a mathematical fact of how these algorithms take 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 the mixture of mood? Is it 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 a moment that you're just rubbing each other in the wrong way.
Never. No, I'm just kidding. Yeah, of course.
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. I mean, by the time we do those maybe they become accessible. So we have to recognize. So what I'm trying to say here is it's not what's not very deep, is the data accessible, you know, 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 is 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, I 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 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 the machine learning world we call them objective functions, you can build certain things into math. For example, now you go to say ChatGPT, it has different mode like think deeper mode or 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 thinks 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 becomes 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 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 don't want to see you suffer. They have that empathetic feel of, 'Oh, wow. If you're in pain, I've experienced pain.' So 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 ticks human, what triggers human 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.
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Certainly, we've gotten very accustomed to receiving communications in fairly deprived language. Texts are not like extensive prose. Language has 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 an 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 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 come 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.
Mhm. 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, he 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.
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 an escapee, and you get the actual rabies virus.
