Huberman LabUsing AI to Increase Your Intelligence & Enrich Humanity | Dr. Fei-Fei Li
EVERY SPOKEN WORD
105 min read · 20,526 words- 0:00 – 3:46
Fei-Fei Li
- FLDr. Fei-Fei Li
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, they're, they're forgetting the past. But if you look at 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, I'm not denying this, but fundamentally, I'm a 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, 'cause I think our society today, and especially Silicon Valley, are not doing them a service.
- AHAndrew Huberman
Hmm.
- FLDr. Fei-Fei Li
We're forgetting about them.
- AHAndrew Huberman
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. [upbeat music] 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
- 3:46 – 12:11
Vision & Intelligence; Human Vision & Contribution to AI
- AHAndrew Huberman
Dr. Fei-Fei Li. Dr. Fei-Fei Li, welcome.
- FLDr. Fei-Fei Li
Thank you. I'm excited to be here, Andrew.
- AHAndrew Huberman
Yeah, this is a long time coming, and-
- FLDr. Fei-Fei Li
Yes
- AHAndrew Huberman
... 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-
- FLDr. Fei-Fei Li
And fellow colleagues
- AHAndrew Huberman
And fellow colleagues-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... at Stanford. So I'd like to start in vision.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
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.
- FLDr. Fei-Fei Li
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, and the, the cousins. And before that, there was very little sensing. Uh, around that same time, tactile and haptics was starting also to emerge in animal bodies, but n- but there was no hearing, there was no, you know, smelling, there is no p- p- 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, your life, right? From a evolution point of view, and you become someone else's food, and also you're actively seeking food, you're actively seeking mates and, and, and all that. So really, because of sensing and perception, evolution took a incredibly accelerated pace in terms of, uh, animal speciation. Fossil studies have told us that 10 million years after the first, uh, light for animals- was what we call the, 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, um, advanced intelligence and how that emerged. You and I are both vision student and, and scientist. It is estimated half of the cortical aca- 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, human life. Now, in parallel, vision as a s- uh, as a discipline or as a s- a area of c- uh, artificial intelligence was really played a pivotal role in what we see as this modern AI moment in a couple of ways. First of all is the, the, uh, 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 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 this 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 trillions of parameters. It has the complexity that departs from what we recorded in the mammalian g- uh, brain or the visual pathway. But the origin is very close to each other about half a century ago, um, a little more than half a century ago. That's one aspect of, uh, 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, 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, uh, I was a very young faculty, um, 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, p- uh, namely vision literature, and started to study how much humans learn, how much humans can see. And the numbers were incredible. Humans 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 the mo- most of the time, the moment they are 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 led this, um, ImageNet 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 50 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, uh, that, that convergence of the three elements of modern AI became the defining moment o- of what, um, what modern
- 12:11 – 18:34
Computer Vision & the AI Revolution
- FLDr. Fei-Fei Li
AI is.
- AHAndrew Huberman
I recall somewhere around 2012-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... 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 faces as well as humans? Now I think most people would say computers are actually much better at it-
- FLDr. Fei-Fei Li
Yes
- AHAndrew Huberman
... than humans are, even though you have these super, super recognizer people who are exceptional at this.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
Could you tell us how is it that this technology went from a state basically where it would confuse you and maybe a, a cousin or, or even someone that looks somewhat like you-
- FLDr. Fei-Fei Li
A cookie [laughs]
- AHAndrew Huberman
... to the, uh, right, or to the point where, uh, to the point where now it is exquisitely precise.
- FLDr. Fei-Fei Li
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, 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 a, um, after many decades of research, neural network algorithm is getting more mature. Um, you know, starting, as we said, 1950s, people start to, um, 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, uh, 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, by the time it's in the, um, after, you know, around 20, uh, 2010-ish, the maturity of these algorithms have, have gotten to a level that it's, 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 this algorithm to learn these patterns. So this convergence of these three things really set off, um, the, the, the revolution of AI. The specific moment is also worth mentioning 'cause 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. Uh, um, and, uh, we put out a, uh, public challenge for the research community, uh, for, 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 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, uh, the task for the algorithm is I'll show you a picture. You have to name the, the main objects inside. And if you guess right, you're, you, you get a point. If you guess wrong, you don't get a point. So that ImageNet challenge, uh, 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 1,000.
- AHAndrew Huberman
Mm.
- FLDr. Fei-Fei Li
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, was cut, um, to... Oh, by the way, the human per-per-performance error rate was 4%. Sorry, I, I need to correct that. The error rate was cut down to, to the tens. It wasn't where human performance was.
- AHAndrew Huberman
So this is looking at images and, and assigning a, a, a-
- FLDr. Fei-Fei Li
One out of 1,000 labels.
- AHAndrew Huberman
Got it.
- FLDr. Fei-Fei Li
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, it means a inflection point. But it still took another three years, I remember, by 2012, 2016, for the algorithm to beat humans in, in naming 1,000 objects.
- AHAndrew Huberman
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, a recognition against time pressure? Like, they have to-- They're being fed images fast enough that occasionally they do an incorrect assignment.
- FLDr. Fei-Fei Li
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 wanna do this forever.
- AHAndrew Huberman
Mm-hmm.
- FLDr. Fei-Fei Li
Um, but I think, you know, the, the human brain, as you know, um, has limited memory, whether it's long term or short term, right? So retaining the patterns of 1,000 object classes, even if some classes you're, you're familiar, is, is not that easy. You know, so, so I think there is the confusion and, and also, for example, different species of dogs gets really close
- AHAndrew Huberman
Mm-hmm
- FLDr. Fei-Fei Li
And that, that's a challenge.
- 18:34 – 21:19
Sponsors: Lingo & Wealthfront
- AHAndrew Huberman
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- 21:19 – 23:36
Speech, Sound & AI Development
- AHAndrew Huberman
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, you know, as humans, we, we are amazing at recognizing speech inflection, emotional tone, things like that. But if I had to discriminate, you know, even 15 different f- sound frequencies, I can tell you as a non-musician, um, it would be very difficult for me.
- FLDr. Fei-Fei Li
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, uh, natural language processing, which is more than recognition, uh, vision, all areas got really a boost in terms of the technology. We have colleagues at, uh, Stanford who are studying whale s- sound, right? Uh, whale songs using, uh, machine learning and AI now. And speech recognition is another area that did so well in the early days of this AI, AI revolution. And of course, the technology continues to, um, advance. By the time the transformer paper was, uh, 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, uh, progress. It's the field of natural language processing. So because the recipe hasn't changed, now we have a even more powerful neural network algorithm called Transformer, but we have even more data on the internet from, uh, at least more readily available data on the internet in the form of texts.
- AHAndrew Huberman
Mm-hmm.
- FLDr. Fei-Fei Li
And now we have more powerful GPUs, so companies like OpenAI and Google quickly rallied beyond th- this, this very important technology, and, um, it still took about five years, from 2017 to 2022, to get to the ChatGPT moment in natural language. But that's yet another
- 23:36 – 33:43
AI & Contextual Learning, Human Intelligence
- FLDr. Fei-Fei Li
step forward.
- AHAndrew Huberman
So I think for people who are not computer scientists nor neuroscientists, the, um, natural human, uh, experience will perhaps resonate with them and, and maybe I can just frame my question through that lens. So when a child learns that there's something called a kitty cat-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... they go, "Oh, cat." Then they usually drop the kitty part. They may say, "Kitty," and then they learn cat.
- FLDr. Fei-Fei Li
Mm.
- AHAndrew Huberman
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, you know, 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-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... just based on their experience. They're-
- FLDr. Fei-Fei Li
Uh-huh
- AHAndrew Huberman
... making a probability judgment. And, uh-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... that's essentially what, uh, AI can do.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
That's essentially what machine learning can do.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
But it seems to me that there's a key moment that had to happen in the progression of, you know, 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-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... 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-
- FLDr. Fei-Fei Li
Mm
- AHAndrew Huberman
... that if something is moving, you're only getting a partial image, this isn't what most people think of as in, 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-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... and why we consider ourselves probably the smartest species on Earth, and if not the smartest, then certainly the best at technology development.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
So when did AI achieve this, and how was that scripted into these computers to allow them to do that?
- FLDr. Fei-Fei Li
So let's just take the problem very-- You, you have described it so well, this problem of seeing a glimpse of a cat tail and being able to recognize cat, right? Or, or, or assign a 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 could reliably do it, there were different algorithm. You know, you can imagine a common sense way of thinking about this is, oh, maybe we should recognize all the furniture to know it's, uh, indoors, so it's unlikely to be a fox. So though there are rules like that, that, uh, it, it was built into, uh, 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, you know, cat being one of them. That limits the, the, the, the search or guess, uh, 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, uh, have learned really created the capability in the machine's, uh, uh, learned space so much knowledge, so much pattern, that when presented with this more or less maybe a new-ish 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, put the machine's, um, assessment or, or guess of this, this object closer to what it has seen, which is likely to be a cat tail or, or just tail, because there's just so much data.
- AHAndrew Huberman
Got it.
- FLDr. Fei-Fei Li
This is where, Andrew, as neuroscientists, I think we depart from human brain, 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, I do wanna point out that departure between today's AI algorithm that is learned with a humongous amount of data versus how, uh, humans have evolved.
- AHAndrew Huberman
If we continue to, um, 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, they hear the word intelligence, and they just think, "Oh, it must be some higher order thing," creativity, imagination. L- let's go to, um, a middle step and then a, and then a much further step out.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
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-
- 33:43 – 45:48
Current AI Gaps, Emotion & Creativity
- AHAndrew Huberman
Let's go to a really far out there-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... aspect of brain function that we know exists in humans, which is thoughts-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... and creativity.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
Now, there are probably rules for thoughts and creativity. Uh, they're a little bit harder to tack down, uh, than, um, 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.
- FLDr. Fei-Fei Li
If you use apple as an example-
- AHAndrew Huberman
Mm-hmm
- FLDr. Fei-Fei Li
... we could have gone from low level, seeing an apple-
- AHAndrew Huberman
Mm-hmm
- FLDr. Fei-Fei Li
... to mid-level, seeing apple always drop, not fly off.
- AHAndrew Huberman
Mm-hmm.
- FLDr. Fei-Fei Li
And the highest level, what is the equation that governs the apple's movement?
- AHAndrew Huberman
Right. So that's ascending to, like, a higher order-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... more reductionist analysis.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
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.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
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, 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, uh, you know, you could put a symphony there, and it follows certain rules of music and mathematics and sound. Like, that, 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-
- FLDr. Fei-Fei Li
[laughs] Let's do it
- AHAndrew Huberman
... 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, uh, experience does this reveal?" And it only has access to what's on the internet. So h- how can you capture a, 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, 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.
- FLDr. Fei-Fei Li
Totally. Andrew, I, I know you put a lot of thoughts into this question, and I, 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, 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, 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 chit-chitchat all the way to deep scientific articles who digi- 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, uh, 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, 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 v-videos, human expressions or even nature's ex- whatever in, 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, can synthesize patterns because so much of this is already there. But the thing that you just talk about, that when, let's say Picasso had that incredibly profound thought about that particular way of expressing that, that portrait of the, 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 way. Did you remember Move 37?
- AHAndrew Huberman
This is in AlphaGo, right?
- FLDr. Fei-Fei Li
Right.
- AHAndrew Huberman
Yeah.
- FLDr. Fei-Fei Li
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 it's a third game o-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 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 off, Go is a highly mathematical game. It, it has very clear mathematical objective, very clear mathematical rules in terms of move. So when AI having a bigger compute and, um, ways to retain how many moves it can, uh, it can remember, it 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 a 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 Field Medalist, I probably have forgotten there are known methods in math that can solve these problem, because I have a human brain. I don't remember, and I don't know all of math's, you know, solutions in the past hundreds of years, even if I were a, 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 s- math problems require solutions that have not been invented, that will push creativity to a whole different level." And this is where y- you know, I'm, we should be curious. Is it gonna be a human creativity or m- AI would go through its iterations of, uh, 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.
- AHAndrew Huberman
Hmm.
- FLDr. Fei-Fei Li
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 deduct- deductive reasoning. This is maybe, Andrew, you look at this cup and say, "It's a gray 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 that my reaction to this cup and potentially what w- 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 it, AI cannot
- 45:48 – 53:04
Computers Enhancing Humanity; Tool: Personal Agency & Learning about AI
- FLDr. Fei-Fei Li
access.
- AHAndrew Huberman
I feel like at some point in the not too distant future, uh, computers will have access to our brain activity in non-invasive ways.
- FLDr. Fei-Fei Li
Mm.
- AHAndrew Huberman
So what, you know, like I might even imagine in five, 10 years, I'm wearing something on my head right now.
- FLDr. Fei-Fei Li
Right.
- AHAndrew Huberman
You can't see it. It's a very, very fine hair net. Hair net makes it sound like it was... Uh, 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 is going to happen. You and I both know this.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
And it's probably already starting to scare people, but let's, let's, 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, 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.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
And I can decide to collaborate with this aspect of me and say, "Let's, let's come up with a really interesting, uh, picture that, uh, I've never seen before but comes from some experience of mine that's important based on whatever." Like, w- and, and it could reveal that to me because-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... it has access to my, to unconscious-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... features of my brain activity.
- FLDr. Fei-Fei Li
Yes.
- AHAndrew Huberman
I think this is very likely to happen-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... in, 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 of them, you're actually learning about yourself.
- FLDr. Fei-Fei Li
Of course.
- AHAndrew Huberman
And, and I feel most people have an inherent interest in what's going on for them, also with other people, thank goodness.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
But there 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 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 s-... Like right now, if you think about how primitively we go about this, it's kinda 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.
- FLDr. Fei-Fei Li
No, the, the, 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 i- it doesn't even have to go as sci-fi as a small hair net, uh, accessing your brainwaves. Just AI learning your patterns of writing-
- AHAndrew Huberman
Hmm
- FLDr. Fei-Fei Li
... can already help you to be, you know, a better communicator, a more effective communicator, a more efficient communicator, and that is a empowering capability that we could unleash in today's AI. I think one of the most important thing, 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 do- it should not take away our agency, and people who lead in today's AI should not try to talk like that this, this work will take away agency from people.
- AHAndrew Huberman
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, w- we, they become such nerds of that thing that we forget that it can be scary to people.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
And that the languaging around it is essential.
- FLDr. Fei-Fei Li
It is.
- AHAndrew Huberman
And I remember a time in the early '90s, I'm sure you remember this too, when genetic testing was viewed as-
- 53:04 – 55:37
Sponsors: AG1 & LMNT
- FLDr. Fei-Fei Li
better.
- AHAndrew Huberman
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. AG1 Pro takes the clinically backed AG1 formula, which is a blend of vitamins, minerals, probiotics, and adaptogens, and adds three important new ingredients, creatine monohydrate, calcium HMB, and zinc carnosine. Each serving has five grams of creatine monohydrate to support muscle strength and performance, as well as brain health, calcium HMB to support muscle recovery and reduce muscle breakdown, and zinc carnosine to support and improve the lining of your gut. All three of these ingredients have compelling science to support them, and therefore, I love seeing them added to the existing AG1 formula. As most of you know, I've been taking AG1 every day for nearly 14 years now. I started taking it long before I even knew what a podcast was. It's a great product, and it's now made even better with the new AG1 Pro formula. If you would like to try AG1 Pro, you can go to drinkag1.com/huberman to get a special offer. AG1 is giving away a free bottle of omega-3 coenzyme Q10 with your first subscription. Again, go to drinkag1.com/huberman to get a free bottle of omega-3 coenzyme Q10 with your first AG1 subscription. Today's episode is also brought to us by LMNT. LMNT is an electrolyte drink that has everything you need and nothing you don't. That means the electrolytes, sodium, magnesium, and potassium, all in the correct ratios, but no sugar. Proper hydration is critical for brain and body function. Even a slight degree of dehydration can diminish your cognitive and physical performance. It's also important that you get adequate electrolytes. The electrolytes, sodium, magnesium, and potassium, are vital for the functioning of all cells in your body, especially your neurons or your nerve cells. Drinking LMNT makes it very easy to ensure that you're getting adequate hydration and adequate electrolytes. My days tend to start really fast, meaning I have to jump right into work or right into exercise. So to make sure that I'm hydrated and I have sufficient electrolytes, when I first wake up in the morning, I drink 16 to 32 ounces of water with an LMNT packet dissolved in it. I also drink LMNT dissolved in water during any kind of physical exercise that I'm doing, especially on hot days when I'm sweating a lot and losing water and electrolytes. LMNT has a bunch of great-tasting flavors. In fact, I love them all. I love the watermelon, the raspberry, the citrus, and I really love the lemonade flavor. So if you'd like to try LMNT, you can go to drinklmnt.com/huberman to claim a free LMNT sample pack with any purchase. Again, that's drinklmnt.com/huberman to claim a free
- 55:37 – 57:34
Public Discourse about AI
- AHAndrew Huberman
sample pack. The idea that technologies can be connectors as opposed to separators, I think has to sit at the center of the discussion.
- FLDr. Fei-Fei Li
Yes.
- AHAndrew Huberman
And we all know who they are. The, there, there m- several of them, but the big names in this field, you know, they, 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, the microscope is on them, and the cameras are on them, and, and so every, 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, 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-
- FLDr. Fei-Fei Li
Yep
- AHAndrew Huberman
... but it doesn't serve anybody well.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
At the end of the day, like, you pull back the veil and let people in, and people feel safer.
- FLDr. Fei-Fei Li
Yeah. There is a power in, in not sharing. There's also a power to say, "Just trust me. I will tell you." And the, neither as educators, that is, we don't go to our s- 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, uh, 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
- 57:34 – 1:07:38
AI to Enhance Scientific Discovery & Healthcare; Human Collaboration
- FLDr. Fei-Fei Li
so important.
- AHAndrew Huberman
Well, certainly I'll take names of people to, to host-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... in addition to you, but since, uh, you're here, I'm gonna 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 very complicated set of rules, but if you learn them, there's a constrained set of rules.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
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-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... there are rules of medicine. There are rules of science. Y- you have a question, you pose a hypothesis. You test the hypothesis. You try and rule out your hypoth- and so on, like the f- 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 controlled 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, 'cause 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-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... 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.
- FLDr. Fei-Fei Li
Yep.
- AHAndrew Huberman
Like, I learned that action potentials are unitary. They always look the same. You either fire or not.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
But there was a paper not but 12 years ago that showed that the shape of an action potential can vary quite a lot.
- FLDr. Fei-Fei Li
Mm.
- AHAndrew Huberman
It was published in Nature.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
Everyone saw it, and then no one wanted to deal with it.
- FLDr. Fei-Fei Li
[laughs]
- AHAndrew Huberman
It's just too much.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
It changes the rule.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
Neurons are supposed to be either graded or all or one, and the all or... I mean, 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.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
And it just, our understanding of the brain just breaks down to zero.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
But if you gave AI the rule that it could be, you know, 100 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.
- FLDr. Fei-Fei Li
[laughs]
- AHAndrew Huberman
I don't think it, it, it can do it, and it can do it, like, in the duration of this question-
- 1:07:38 – 1:19:18
Intuition, Motivation & Human States Beyond AI
- FLDr. Fei-Fei Li
surgery.
- AHAndrew Huberman
Amazing. 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-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... 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, eh, 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.
- FLDr. Fei-Fei Li
Mm-hmm. Yep.
- AHAndrew Huberman
Could do an imaging experiment, but you're not gonna collect all the neurons and hormones and everything simultaneously, so we don't really have, like, a location or even a network to, to point to. Like, things like creativity, intuition, premonition, the idea that, you know, y- 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'm, wanna 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're 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, uh, 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? Giving it... And it only has access to the internet. Can it actually cr- 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.
- FLDr. Fei-Fei Li
Such a interesting question, Andrew. So, um, I do wanna 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, what, what's gonna happen, right? Is it really intuition? So today, when you go to a AI chatbot, you're gonna prompt, you know, "I'm a Stanford professor and a, um, a, um, neuroscientist. Um, 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, [laughs] you know, loving race cars.
- AHAndrew Huberman
Mm-hmm.
- FLDr. Fei-Fei Li
Even if we ask the same question, it'll have customized answer. That is a mathematical, uh, I wouldn't call it energy, I wanna be... That is just a mathematical, uh, fact of how these, um, these algorithms takes these context and tailor the, the, the outputs. And it's called context. It's not that deep in, uh, 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 a image," that, that also is, 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?
- AHAndrew Huberman
Mm-hmm.
- FLDr. Fei-Fei Li
Like, is it because I smell something? Is it hormones? Is it, [laughs] you know, the, the mixture of mood? Is it my breakfast? That intuition, w- 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. [laughs]
- AHAndrew Huberman
Never. No, I'm just kidding.
- FLDr. Fei-Fei Li
Right?
- AHAndrew Huberman
Yeah, of course.
- FLDr. Fei-Fei Li
So if you're really familiar with each other, you ki- 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, 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, a, a different human nor a, a machine can, can do anything about it 'cause 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, you know, skin conductance, um, sensors. I mean, by the time we do those, maybe they become accessible.
- AHAndrew Huberman
Mm-hmm.
- FLDr. Fei-Fei Li
So we have to recognize... So, so what I'm trying to say here is it's not, what's not very deep is, is the data accessible? You know, either through language or through picture or through imaging or through brainwaves, whatever it is, it needs to be a accessible piece of information. If it's accessible, then if we have collected enough of that, you can train machines with, or if a tr- machine is well-trained, it can, like you said, in a private way, forget about privacy, uh, uh, a breach, but in a private way, the machine can probably u- use it. What I'm trying to do, Andrew, here is not to make it sound mystical-
- AHAndrew Huberman
Mm-hmm
- FLDr. Fei-Fei Li
... but try to give it a scientific process to describe if it were to happen, how would that happen?
- AHAndrew Huberman
Yeah, because, um, pattern recognition based on big data sets and rules get us a long way-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... is what I'm hearing. And we, 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, y- you spend a lot of time looking at cells at some point in your career, and it's amazing how, like, the electrophysiologists for s- decades, if not longer, d- you develop an intuition.
- FLDr. Fei-Fei Li
Yes.
- AHAndrew Huberman
I, I'm not really a physiologist, but I learned to recognize cells based on, like, kinda these things that were not written up in any papers.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
But, like, if there was kind of a, like a, like straighter edge along this thing and it had a certain shape and roundness, like, I'd tell you right now, that's a transient off alpha cell in the retina.
- FLDr. Fei-Fei Li
Yeah. [laughs]
- AHAndrew Huberman
Eventually, we, 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-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... 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?
- 1:19:18 – 1:20:37
Sponsor: David
- FLDr. Fei-Fei Li
the public about this.
- AHAndrew Huberman
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- 1:20:37 – 1:27:38
Social & Ethical Considerations for AI
- AHAndrew Huberman
I feel like people assume there's an emotion, a person, or whatever inside of the AI chatbot because we're so language-oriented.
- FLDr. Fei-Fei Li
Yes.
- AHAndrew Huberman
It's talking to us.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
It's writing things to me, and we do that more now than we did 30 years ago.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
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.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
But at some point soon, I'm guessing faces are going to start to enter the picture.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
Uh, no pun intended.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
Um, like, how far off are we from, like, if you or I were to text the other person, "Oh, uh, 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.
- FLDr. Fei-Fei Li
The technology is there.
- AHAndrew Huberman
Mm-hmm.
- FLDr. Fei-Fei Li
But we have to now look, uh, zoom out a little bit and look, 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.
- AHAndrew Huberman
Mm-hmm. Well, let's do that because one thing that I know about biologists or technologists is they like to go fast 'cause it, 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, uh, 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.
- FLDr. Fei-Fei Li
Oh my God.
- AHAndrew Huberman
Now, that's fine and good, in my opinion, if you are absolutely certain, 100% certainty, that that is a non-functional version of the rabies virus because-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... 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 o-one fruit fly that somehow-
- FLDr. Fei-Fei Li
Mutated
- AHAndrew Huberman
... is an escape or-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... and you get the actual rabies virus, there's the potential it mates 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.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
But it was exciting for them to think about, and then they got denied, right? For good reason.
- FLDr. Fei-Fei Li
Yes.
- AHAndrew Huberman
I was grateful, right?
- FLDr. Fei-Fei Li
Yeah.
- 1:27:38 – 1:35:04
Kids, Development & AI Tools; Tool: Prompt AI Effectively
- AHAndrew Huberman
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 being shaped by our understanding of the human brain. So f- 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 chatbot." But if you look back in the history of learning, similar arguments were made about calculators, um, 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.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
So like if anything, we were gifted with a, 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-
- FLDr. Fei-Fei Li
Yeah. [laughs]
- AHAndrew Huberman
... here's Pong and there's the Apple IIe and like we had and 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 Haidt have pointed out, have created a situation where most people, like they love these technologies for the ease and convenience-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... but we're all a little bit more aware now or a lot more aware that we're giving up something, too.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
And that there are traps that people in particular, young people, can fall down.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
So what is the very optimistic meh and very pessimistic view in your, in your mind, if three, if three flavors actually exist there, of how young brains can be enriched or unaffected or can be, uh, harmed by AI as it exists now? Let's just kind of stay with what we've got.
- FLDr. Fei-Fei Li
Great question, Andrew, and the answer almost fall out of our previous conversations, 'cause you used 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 move in 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 what, 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 brain. The other kind of danger is in the name of agency and, and, uh, and motivation, the tools are denied to our students because we're worry you cheat. We're worry you only got your answer from ChatGPT. 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, I was just thinking about I was a pre-med student for, for a while. Man, organic chemistry was hard. [laughs] You know? I remembered trying to learn the, the molecules, their orientations, but the TA hours are too short, or it overlaps with my other class, and my professors only have certain number of office hours. It was just a struggle to learn that, right? If today I were to have a, a AI companion, I would ask so many questions about organic chemistry 'cause I know what, where I'm stuck, right? I have the motivation to learn. I just needed to, uh, 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 a tool or taking away agency and motivation.
- AHAndrew Huberman
Mm-hmm.
- FLDr. Fei-Fei Li
Of course, the flip side is, is great, is let's find a way to keep our children and students' motivation and agency. Let's 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. 'Cause they are super powered
- AHAndrew Huberman
I love that answer. Um, I have great faith in neuroplasticity and the younger generations, too.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
To-
- FLDr. Fei-Fei Li
Even our own. I know we're old, [laughs] but-
- AHAndrew Huberman
Not so old. Let's give ourselves some credit.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
Plasticity does exist throughout the lifespan.
- FLDr. Fei-Fei Li
Right.
- AHAndrew Huberman
Yeah.
- FLDr. Fei-Fei Li
Even our own neuroplas- plasticity, right? Like, I, I find AI a great tool for my learning.
- AHAndrew Huberman
I mean, for me, it's been a remarkable discovery of what it can do.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
But I, 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, uh, knowledge set-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... in- inside of whatever it is I'm asking.
- FLDr. Fei-Fei Li
Well, I actually have another thing. A Stanford undergrad taught me something last year, and I realized before ChatGPT, sometimes I get lazy. I, 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, that's, that's too lazy.
- 1:35:04 – 1:43:52
Next Frontier for Robotics & AI; Human Agency
- FLDr. Fei-Fei Li
[laughs]
- AHAndrew Huberman
... those discussions.
- FLDr. Fei-Fei Li
Yes.
- AHAndrew Huberman
[laughs] Which actually is a, a good transition perhaps to this notion of embodied AI. You know, it's a world apart to attach a face speaking to hearing words. Uh, my good childhood friend, um, who I h- I hope you'll meet soon, because y- you both would benefit from the conversation so much, and I just wanna be a fly on the wall, um, Dr. Eddie Chang, chair of neurosurgery, bioengineer, 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.
- FLDr. Fei-Fei Li
Wow. Wow.
- AHAndrew Huberman
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, uh, 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 s- frozen real face.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
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-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... the sort of Stephen Hawking thing.
- FLDr. Fei-Fei Li
Mm.
- AHAndrew Huberman
And it's constantly being updated-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... through machine learning.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
What, and, and now also paying attention to the people she's speaking to and their responses. I mean, this is, this is embodiment.
- FLDr. Fei-Fei Li
Yes.
- AHAndrew Huberman
It's on a 2D flat, 2D screen-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... admittedly, but this is, like, a exponential leap over just robot sound-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... or even accurate sound alone.
- FLDr. Fei-Fei Li
Mm-hmm. 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, you know, 500 million years without verbal-
- AHAndrew Huberman
Mm-hmm
- FLDr. Fei-Fei Li
... communication. And, uh, and also the world would, in the right version, would be a lot better place with robots helping humans.
- AHAndrew Huberman
Could you give me some examples? I love this idea, but again, I'm, 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-
- FLDr. Fei-Fei Li
[laughs]
- AHAndrew Huberman
... my beautiful little six-month-old puppy.
- FLDr. Fei-Fei Li
Oh. [laughs]
- AHAndrew Huberman
How could you not stop when he wants to cross the street? But a lot of people won't stop.
- 1:43:52 – 1:50:10
Human-Centered AI Future
- FLDr. Fei-Fei Li
the future together.
- AHAndrew Huberman
Like with your example of your father's surgery, to cross the, the, the robot with the physician, right?
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
If we cross a problem where there's a vulnerability with a robot that clearly makes things better-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... the picture changes, uh-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... in the right direction. So I'm thinking of a few examples off the top of my head. Like, um, 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.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
But if a robot was really a good guardian of your kid-
- FLDr. Fei-Fei Li
Definitely
- AHAndrew Huberman
... to the point where they could alert the authorities or maybe even protect, physically protect-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... your child, that would be awesome.
- FLDr. Fei-Fei Li
Yep.
- AHAndrew Huberman
Give them more agency in the world. You think about, um, some of the d- 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 a kind of an avatar in there with you that's really-
- FLDr. Fei-Fei Li
Mm
- AHAndrew Huberman
... advocating for you that can-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... spot things and keep predators at bay.
- FLDr. Fei-Fei Li
Here you go. That's a great startup idea.
- AHAndrew Huberman
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, you know, probably HR wouldn't look too kindly upon him nowadays, but he understood-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... that these things we call computers needed to have rounded edges.
- FLDr. Fei-Fei Li
Yes.
- AHAndrew Huberman
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 would 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.
- FLDr. Fei-Fei Li
Yes.
- AHAndrew Huberman
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's guilty of k- of, of building this chasm, 'cause it's like these technologists are coming for us. I, I think that's, 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 Stacy, Stacy whoever it's... I mean, it could be a man, could be a woman, someone who really understands human nature.
- FLDr. Fei-Fei Li
There are many of them.
- AHAndrew Huberman
But-
- 1:50:10 – 1:54:12
World Labs, Spatial Intelligence
- FLDr. Fei-Fei Li
promote a lot of those work.
- AHAndrew Huberman
I would love to learn more about your startup, um, because you don't pick projects haphazardly. So what is the, what is the project? What's the goal?
- FLDr. Fei-Fei Li
So my startup, uh, co-founded with, um, a, a couple of other co-founders, is called, uh, World Labs.
- AHAndrew Huberman
Mm-hmm.
- FLDr. Fei-Fei Li
We co-founded it at the beginning of, uh, 2024. It really is for, for me, a kind of my life's work. You know, we both come from vision, and the recognition of, uh, there's more beyond language intelligence is what really motivated me to, to think hard about what's the next chapter of AI frontier. And, uh, 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, um, uh, devote more Time to build, um, models or build eventually products that can help unlocking capabilities in spatial intelligence, like generating 3D, 4D worlds that are, um, deeply useful for creators, for robot training, for, uh, architecture design, design, or to en- 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-
- AHAndrew Huberman
Mm-hmm
- FLDr. Fei-Fei Li
... per se. And, uh, so World Labs was founded based on that premise. We are still a young company. We're very much a, um, a model-focused company where we're building this, this foundation model, and we're started by a lot of PhDs. [laughs]
- AHAndrew Huberman
Mm-hmm.
- FLDr. Fei-Fei Li
But now we, we, we, we're starting to build products. And, uh, so it's, uh, it's still the beginning. It's-
- AHAndrew Huberman
Mm
- FLDr. Fei-Fei Li
... very exciting. And as a technologist, I feel deep in my heart I'm a builder.
- AHAndrew Huberman
Mm.
- FLDr. Fei-Fei Li
You know, it's, maybe it's because also I'm a immigrant, so that, that rolling your sleeves up and just get in with the young generation that's so incredibly smart and just build something from scratch is just so exciting.
- AHAndrew Huberman
I recall a time not, what, 15, 20 years ago when there were cars driving around-
- FLDr. Fei-Fei Li
Oh, yeah
- AHAndrew Huberman
... taking images.
- FLDr. Fei-Fei Li
Still, still driving around.
- AHAndrew Huberman
Still driving around, taking images. But I imagine that there... And there are certainly aerial views as well, but you can imagine little tiny drones, like the type that could fly through a neuron, and just kinda look at everything. Or, um, so to speak, or drones picking up information about every nook and cranny of the fjords in Norway.
- FLDr. Fei-Fei Li
Right.
- AHAndrew Huberman
Has that been done to sort of map the f- the three-dimensional world?
- FLDr. Fei-Fei Li
First of all, let's not make it sound scary that drones are getting to people's homes and, uh, 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 World Labs is doing is not just taking real-world images. It's we allow people to imagine what's in their mind's eye. 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-
- AHAndrew Huberman
Mm-hmm
- FLDr. Fei-Fei Li
... and environments. Um, why is it useful? Because, uh, entertainment industry will use it.
- AHAndrew Huberman
Mm-hmm.
- FLDr. Fei-Fei Li
Design industry will use it. Robotics industry, uh, very much would use it for training environments and, and, and all that. So the combination of capturing what's in the real world as well as capturing what's in your imagined world
- 1:54:12 – 1:59:51
Concerns about AI & Creativity; Movies, Art, Storytelling
- FLDr. Fei-Fei Li
is the new frontier.
- AHAndrew Huberman
If you don't mind, I'd like to just take a couple of more minutes and talk about this, uh, moving from imagination to-
- FLDr. Fei-Fei Li
Mm-hmm
- AHAndrew Huberman
... something. Because this is Los Angeles, it occurred to me that a lot of people write scripts.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
And then they try and get them, their movie made.
- FLDr. Fei-Fei Li
Mm-hmm.
- AHAndrew Huberman
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. Um, and you could maybe edit it a little bit here and there where it needed help-
- FLDr. Fei-Fei Li
Right
- AHAndrew Huberman
... of course. Has that been done? Has a, a successful movie been made start to finish using AI?
- FLDr. Fei-Fei Li
So this is a very nuanced topic. This is where we also get into people's wariness of AI and creativity when, if not careful, it might sound like we're taking away from storytellers' and creators' job-
- AHAndrew Huberman
Mm.
- FLDr. Fei-Fei Li
Right? So-
- AHAndrew Huberman
Mm.
- FLDr. Fei-Fei Li
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, is getting really good. We have seen short movies, even almost feature-length films, being assembled by AI, AI tools. We have. And, 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 peop- characters. A lot of that is what Hollywood and, and novel writers is about. So how do we meet the human need and human desire of storytelling with modern tools is actually a, 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, in a constructive way? You know, this is not my industry per se, but I would love to see much more nuanced work-
- AHAndrew Huberman
Mm-hmm
- FLDr. Fei-Fei Li
... in, 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, um, 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 and storytellers or movie makers at this moment is critical.
- AHAndrew Huberman
Yeah. I feel like in every example of technology, there's some crossover point-
- FLDr. Fei-Fei Li
Yes
- AHAndrew Huberman
... that m- when somebody who's truly an insider embraces a technology, and then it just kind of takes off, like-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... you know, uh, Steven Spielberg or something like that.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
Or these are probably aren't the best examples, but like the, the Steve Jobs-Wozniak crossover, kind of a designer technology curious guy and a, and a real, forgive me to the Jobs fan, but a real computer scientist.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
Right? That merge, it's, 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-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... and how to include-
- FLDr. Fei-Fei Li
Yeah
- AHAndrew Huberman
... the industry, the, the people.
- 1:59:51 – 2:05:38
Younger Generation & AI, Teachers
- AHAndrew Huberman
question is about the young generation. How do they feel about AI? Because there is this-
- FLDr. Fei-Fei Li
How young are you talking about?
- AHAndrew Huberman
I'm talking about kids between the age of, uh, seven and 20.
- FLDr. Fei-Fei Li
Okay. That, that's-
- AHAndrew Huberman
Are they, are they, you know-
- FLDr. Fei-Fei Li
That's literally my kids. [laughs]
- AHAndrew Huberman
Yeah. So I might have asked that question for a reason. You know, h- how do they feel about it? Are they excited by it? Because there is this phenomenon where, like, computers come along and, you know, your handwriting teacher is getting nervous that-
- FLDr. Fei-Fei Li
Yes
- AHAndrew Huberman
... 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 for... There's these, 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, uh, embrace-
- FLDr. Fei-Fei Li
[laughs]
- AHAndrew Huberman
... 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?
- FLDr. Fei-Fei Li
This is actually my pet project as a educator and technologist. Everywhere I go, I try to talk to, to students, parents, and teachers, 'cause 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 a 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, they're, they're forgetting the past. But if you look at 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. I'm not des- denying this. But, you know, humanity, they're... Fundamentally, I'm a optimist in humanity, right? So, so that's where I come from. So if you're a total pessimist, maybe we're a- already on the wrong footing. But I look at kids. They're curious. That's why they're kids. They're curious. They, 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, 'cause I think our society today, and especially Silicon Valley, are not doing them a service.
- AHAndrew Huberman
Hmm.
- FLDr. Fei-Fei Li
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, ChatGPT 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 and 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, multibillion-dollar investment firms or multimillion-dollar, multitrillion-dollar companies, when ChatGPT came out, the first thing is, what about our teachers in the neighborhood? Nobody think like that. But we need to, we need to be talking to teachers, we need to show teachers. Of course, they're gonna ask the question about, what if kids cheat? It's okay they ask those questions. Let's just 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, 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.
- AHAndrew Huberman
I absolutely love that answer, and I know that sentiment is shared by many, many people listening. Um, God bless the teachers, and they need help, support, and information.
- FLDr. Fei-Fei Li
Because now they, they turn on, not yours, but most [laughs] podcasts, they're just scared. They're so scared. They hear these doomerism, they hear the doomsa- or they say, "Oh, don't worry, it's utopian." Neither of these messages can help our teachers. And if they're not helped, our kids are not helped.
- AHAndrew Huberman
Couldn't agree more.
- FLDr. Fei-Fei Li
Yeah.
- AHAndrew Huberman
No, 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, t- 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, 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-
- FLDr. Fei-Fei Li
[laughs]
- AHAndrew Huberman
... uh, as we went along, uh, because these machines, uh, are informed by the brain and the brain is informed by these machines, and this is the world we're living in. And, uh, 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, I know many people are very grateful, so thank you.
- FLDr. Fei-Fei Li
Thank you, Andrew, and I really appreciated this conversation. It's a civilizational moment.
- AHAndrew Huberman
Thank
- 2:05:38 – 2:08:12
Zero-Cost Support, YouTube, Spotify & Apple Follow, Reviews & Feedback, Sponsors, Protocols Book, Social Media, Neural Network Newsletter
- AHAndrew Huberman
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 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 pre-sale 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 hubermanlab 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 hubermanlab 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. [outro music]
Episode duration: 2:08:12
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