A Brief History of Biological and Artificial Intelligence with Max Bennett

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Max Bennett: (0:00) Is there going to be an artificial general intelligence? Is there a single principle on which intelligence in general will emerge, or are we actually going to wake up in a decade or two decades and realize that there really was no such thing as AGI? There's different degrees of generalization that occurs across tasks, sure, but all entities are specialized for a specific suite of tasks that they're going to face. And the problem with fine tuning is you very quickly get this overfitting issue where it ends up overfitting to the small fine tuning dataset and it loses its generalization. It's forgetting old tasks that it was able to do. This is it. I don't think this is a nuanced edge case. I think this is a foundational problem with the way these models work. And so how does the mammal brain decide when to incorporate new information and how to add it to my model of the world without interfering with other information is a huge outstanding area of research that I think is a key difference between how mammal brains are simulating and modeling the world and how existing AI systems are doing it.

Nathan Labenz: (0:56) Hello and welcome to the Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan Labenz, joined by my cohost Erik Torenberg. Hello and welcome back to the Cognitive Revolution. Today I'm speaking with Max Bennett, cofounder of Blue Core and author of A Brief History of Evolution, AI, and the 5 Breakthroughs that Made Our Brains. I bought this book immediately upon seeing the title because I've been asking myself for some time now, how many conceptual breakthroughs do we still need before AI systems will achieve functional parity with humans? The answer, of course, is that I really don't know. I would be surprised, but not totally shocked, if it turned out that attention really is all we need and that hyperscaling resolves all practical problems with no major architectural innovations. And on the other hand, I would again be very surprised if the number of breakthroughs needed turns out to be more than five. My best guess would be two or three, but that leaves major follow-up questions like, what are the critical things that humans are doing which AI systems are not yet capable of? Are physical embodiment and multimodal inputs necessary to our overall cognition, or are they just an artifact of our evolutionary history? How is it that we represent memories so efficiently and so usefully, such that even a single experience can shape our behavior for life? How do we determine what new information to incorporate into our world models, what to reject, and what to remain uncertain about? Where does theory of mind come from, and is it necessary for adversarial robustness? And is some sort of hierarchical self-modeling needed for effective planning, and perhaps more importantly, for subjective conscious experience? These are huge questions. And as AI systems become more powerful and behave on the surface, at least, in more human-like ways, I believe it's also increasingly important to understand the key similarities and differences between human and AI cognition in the most literal mechanistic terms possible. For that purpose, this conversation and the book on which it's based are extremely valuable resources. I definitely find myself referring back to the five breakthroughs that Max outlines as a lens through which to ponder new developments in AI. Of course, biological systems are extremely messy and constrained in ways that engineered systems are not. Whereas life has to maintain homeostasis continuously, AI models are mostly decoupled from specific hardware, and humans maintain the data centers. And whereas evolution can only select for incremental changes which improve inclusive genetic fitness, human engineers can and sometimes do discover discontinuous step change advances. So much like the flight of birds inspired and informed the Wright Brothers' early airplane designs but were ultimately eclipsed in speed and power by today's jet planes, so the history of human cognition can inform our understanding of new AI developments and our expectations for future systems, while in my view, and I believe Max would agree, certainly not representing a limit to their overall development. As always, if you're finding value in the show, we'd appreciate it if you'd take a moment to share it with your friends. A review on Apple Podcasts or Spotify would be amazing, but a simple tweet is also worth a lot. Now, without further ado, I hope you enjoy this conversation about the history of intelligence, biological and artificial, with entrepreneur and author Max Bennett.

Max Bennett, cofounder of Blue Core and author of A Brief History of Evolution, AI, and the 5 Breakthroughs That Made Our Brains, welcome to the Cognitive Revolution.

Max Bennett: (4:55) So excited to be here.

Nathan Labenz: (4:57) Excited to have you. So I've been on a journey recently that ultimately led me to your book and to this discussion. And coming from my AI-obsessive perspective, I've been finding myself thinking more and more about what is it that the human brain is doing that the AIs lack? That was the motivation for me to say, there's clearly some stuff here that we're doing that the AIs are not doing, but I lacked a schema, a way to organize that. And so I went looking for who has done a good taxonomy of the things that humans are doing that I could compare and contrast with. And it turns out that you've written at least a version of that book, which takes us through evolutionary history from some of the simplest organisms all the way through to today. And obviously, with your experience in AI, you can't help but get AI into the title these days and certainly have some discussion of that as well. But I'd love to just take this conversation in two parts. First, really flesh out the argument in the book, and then second, see what we can take from that to inform our worldview and our expectations about AI as well. To get started, you want to give just a little bit of your background generally, but also really interested in how you've brought multiple different lines of thinking and scholarship together into the analysis that you've offered in this book?

Max Bennett: (6:21) Sure. So my background is primarily in commercializing AI technology. So I spent the bulk of my career building a company called Blue Core, where we commercialized a bunch of deep learning models for large e-commerce companies. So recommender systems, we did a bunch of work on segmentation. This is now pre-transformers AI, so the old-fashioned AI. And so that was a really wonderful experience and journey. But what always perplexed me in taking these models and bringing them to real use cases where you're trying to add business value is how perplexing the discrepancy has always been between what AI models or machine learning models are really good at and what the human brain is really good at. So back in the day, some very simple things that a human could do, like have common sense about how you might put an outfit together, were things that were astronomically difficult to get an AI model to have common sense about. These things are getting a little bit better now, but back in the day that was an incredibly big challenge. So I always had this curiosity about what is missing between the brain and modern AI systems. And that led me to this side project where I just started corresponding with neuroscientists via email. I started just reading textbooks, sort of just self-learning. And that led me to a few interesting ideas that led me to publish a few papers and collaborate with a few neuroscientists, and then they just started snowballing into me having this idea for a book. And then I was just like, alright, I'm just going to take a year off and write it. And the whole time I was collaborating with a bunch of mentors of mine: Dileep George, Karl Friston, Joseph Ledoux, David Redish. These are all people that have become mentors of mine and have guided me along the way and helped me realize ideas I had that were stupid. So that was a really wonderful journey, and hence the book was created.

To me, the kernel of the idea that makes the book unique is it really brings together three different fields of study that I think wouldn't have been done without an outsider coming in just trying to piece things together. So the three fields of study are, first, evolutionary neuroscience. So this is the idea of trying to piece together what is the story arc by which brains came to be over the last 600 million years. Even just the last decade has seen huge new pieces of knowledge emerge from this field. It's a relatively small field, actually, but there's been some phenomenal studies in trying to actually piece together through looking at different brains in the animal kingdom, gene analysis, all these things have helped us really piece that story together. The second field is comparative psychology, where we're actually analyzing what are the different cognitive and intellectual capacities of different species. And that can be used, obviously, to try and back into, paired with evolutionary neuroscience, what were the cognitive intellectual capacities of our ancestors, which is not really something that's been discussed. I wrote a few papers on that, which is how do we actually try to infer what the first mammals were capable of doing from what existing mammals can do, what non-mammalian vertebrates can do, and what evolutionary neuroscience suggests exists in their brain. And the third field is AI, which I think is really essential to this whole exploration because AI really grounds us in reality. I think fields of cognitive neuroscience, it's easy to get into philosophizing on concepts of how the mind works, which I think is great and fruitful and rich of insight. But what I like about AI is it really grounds us in, do we understand what we mean by these concepts? If so, we should be able to implement a very basic form of it and test how well it works. So I think AI is sort of a litmus test for how well we understand what we're actually saying.

AI has really taught us a lot of our intuitions are wrong. I think reinforcement learning is perhaps the best form factor here where there's been lots of intuitions about how animals learn by trial and error. And it was only by actually trying to implement these things in machines that we learned, well, that intuition can't work, because we haven't. And then we learned, well, the way we actually get it to work in machines is this other approach, which actually does seem to be how brains do it. So there's a really beautiful feedback loop. So in summary, the idea of the book is to take comparative psychology, evolutionary neuroscience, and AI, merge them together, and try and craft a first approximation of how the brain came to be and see if there's insights there for AI. I think there are some insights. I don't think it's necessarily an explicit roadmap for how to build AI, but I do think, as a first approximation, it illuminates aspects on how the human brain works and things that at least are different than how existing AI systems today are functioning.

Nathan Labenz: (11:08) Cool. Okay. Well, I want to get into each of the five breakthroughs in just a second. But one thing that did jump out to me in reading the book and also watching some of the other interviews that you've done, which some of have been great, and definitely recommend folks going deeper down the Max Bennett rabbit hole than we'll be able to do today. But throughout the book, one thing that you might expect to find, which you don't find, is a definition of intelligence. And I wonder, that would seem like a good starting point. You obviously skipped it. So why do you think we are maybe better off avoiding trying to define this term at all?

Max Bennett: (11:45) Yeah, so it's a good question. I've gotten that question a lot, so it's possible in retrospect maybe I should have spoken to it more. I'll give you my reasoning. My reasoning was, when I cataloged all the existing definitions of intelligence, some of the preeminent experts in the field define intelligence meaningfully differently, but their different definitions of intelligence don't lead them to approach the research agenda any differently. And so what to me that means is intelligence is this concept that we all have an intuitive understanding for what we generally are interested in studying. But once we try to pigeonhole it into a specific thing, it's very hard to pigeonhole into one definition. And then there's not much fruit from that because we're still going to be studying the same sort of topic. So I had a great conversation with Brett Cruz about this where he defines intelligence as learning. And I think that's a reasonable definition to go with, but I don't know if it changes anything if you go with that definition versus Fei-Fei's definition, which is a much broader one around an agent that can deal with challenges and solve problems in a changing environment. You could fathom an entity that could solve the latter without having any learning. And so I kind of avoided it because I didn't think it would change the story much. And I don't think I have a definition that I find satisfying.

Nathan Labenz: (13:19) Interesting. Well, I do think that's, you know, I do think there's a lot of time wasted certainly debating definitions and debating from definitions. So I can certainly see some possible wisdom in just skipping that step. And I do also think that one of the things I've learned from AI is just that there's a lot of weird stuff out there, so you can kind of blind yourself in some ways if you have these. I see these debates all the time. Like, does it really understand? Does it really understand? You know? So that seems to lead nowhere.

Max Bennett: (13:51) I do think it brings up something maybe deeper where there's a little bit of a schism where I think I would fall on one side of the schism. Is there going to be an artificial general intelligence? Is there a single principle on which intelligence in general will emerge, or are we actually going to wake up in a decade or two decades and realize that there really was no such thing as AGI? There's different degrees of generalization that occurs across tasks, sure, but all entities are specialized for a specific suite of tasks that they're going to face. And I kind of more fall in the latter category, which Yann LeCun talks about. And so what I think that kind of means is when we talk about intelligence, what we're talking about 80% of the time is we want something that feels very human-like. That's sort of what we're pursuing. And when we mean something human-like, what we're actually talking about is a suite of capacities. It's not really one capacity that we're trying to recreate, not only because we think it can benefit humanity, but because we're really interested in understanding ourselves. And so my hunch is that we're going to end up realizing that there really is no one panacea AGI. There's some general principles that we can probably apply, but we're going to have intelligences that are specialized to certain domains.

Nathan Labenz: (15:09) Well, let's get into the kind of main structure of the book itself. So as the title promises, you've got five breakthroughs. I will just give the one-word label for each one, and then we can kind of unpack each in turn. So the five breakthroughs. Number one, steering. Number two, reinforcing. Number three, simulating. Number four, mentalizing. And number five, speaking. So let's get started with steering. We're going all the way back to the age of small worms, and they're figuring out some of the most basic stuff. So tell us the story of that first breakthrough.

Nathan Labenz: (15:48) Hey, we'll continue our interview in a moment after a word from our sponsors. Max Bennett: (15:52) Yeah. So 600 million years ago, the world was a really fascinating place. If you woke up and you were navigating around the world 600 million years ago, there was really no life on land. So if you looked at Earth from space, it wouldn't be green. There were no trees yet. And pretty much all of life was in the oceans. Most life was single cellular, but there were some multicellular organisms starting to emerge. And the three branches of life, the three branches of eukaryotic life, which is the life that we're made out of, these really complex cells, had already sort of branched off. There was the fungi lineage, there was the animal lineage, and there was the plant lineage. We don't really have a great understanding of what fungi looked like back then, but there's some evidence that there were sort of mushroom-like structures under the ocean, probably quite small. There were some plant-like structures in the ocean, probably most resembling seaweed of today. And then there were animal structures. There's so much debate about what these early animals looked like. We don't know for sure. But most evidence suggests that these really early animals were most akin to today's cnidarians, which is a class of animals that includes sea anemones, jellyfish, coral polyps. And what's interesting about all of these creatures, which some fossil evidence suggests early animals were akin to, is they have no brains. They have nerve nets. So they have this web of neurons that are largely implementing independent reflexes. If you take a coral polyp, which is radially symmetric, if you look at a coral, if you zoom in, they're actually all these independent little polyps which have little tentacles and there's a little stomach. And they're radially symmetric, meaning they're symmetric around a central axis. And what is key about a coral polyp is they're not navigating around to find food. They're not pursuing food. They're not avoiding predators. What they're primarily doing is they have their tentacles out in the water, and they're waiting for small clumps of food to hit their tentacles, and then they grab the food in and they consume it. And what's interesting about them is they don't have a lot of cognitive capacities. Now there is independent convergence, so there's some really interesting studies that suggest today, after 600 million years of evolution, some cnidarians may have gotten a little bit smarter and gotten the ability to do some lightweight navigation. Box jellyfish may have gotten a little bit smarter. So that might have happened. But I do think most would agree that these early cnidarians didn't have these capacities. We've tried to find things like associative learning in coral polyps. We have not found them. So they can't even associate the presence of a light with a shock and then retract to the presence of that light. That very basic association doesn't even occur. So the question is, how do we go from this web of neurons to brains? And this is the first breakthrough, which is steering. And what's key about evolution, which makes evolution amazing, but then also so perplexing, is despite you getting this crazy complexity at the end of 600 million years, every change needs to be selected for. So evolution doesn't have forethought. So it can't say, oh, you know what would be really valuable is if we actually consolidate all these neurons into this design because that would enable us to have all these capacities. So let's just do that next generation. No. It has to be small incremental changes that lead to a result. Every generation has to survive. So it's a very constraining process. So the first brain needed to be simple enough to be something that could be selected upon. So it couldn't just jump from coral polyp to human brain, obviously. And it needed to have adaptive value. So when we look around 600 million years ago, 550 million years ago, the first animals with brains emerged, and there are some interesting features of them. One, they were little worm-like creatures about the size of a grain of rice, fossil evidence suggests, very, very small. And unlike the animals that came before, like cnidarians, they had bilateral symmetry. And that means symmetry across a central plane, which all humans are, arthropods are, all mammals are. And a good organism that people use as a sort of proxy organism for the first bilaterian, which is the class of animals of bilateral symmetry, which is the first class of animals that have brains, are nematodes. They're very small, worm-like animals. And so the most classic nematode, C. elegans, has 302 neurons. I mean, it has a hopelessly simple brain. But we've studied nematodes deeply, and they have a credible suite of cognitive capacities. And so the question is, what did this first brain do? So why did this first brain emerge? Why do we go from nerve nets to this consolidated brain center or head of these animals? And if you look at a nematode and compare it to a coral polyp, there's a few things that would obviously immediately emerge. First is it's moving around a lot. So the strategy of a coral polyp, which is wait for food, is very different than a nematode strategy, which is I'm going to go pursue food. And what's interesting about a nematode is it does a remarkably good job of finding food and getting away from dangers, but it doesn't see anything. I mean, if you look at the sensory apparatus of a nematode, it is hopelessly simple. It doesn't have eyes, lens-shaped eyes. It just has a very basic suite of neurons for detecting certain smells and detecting the presence of light and some very basic sensory neurons for detecting pressure and touch. And yet it does a phenomenal job finding food. And so the way it does this is with the technical term called taxis navigation. I call it steering just to be cute. But taxis navigation is this idea that you don't actually need to understand the structure of the world to find things if you have gradients of sensory information that you can pursue. So smell is a good example. So if you place a little morsel of food in a petri dish, it creates a smell gradient around it, which is just a feature of how the physics of chemicals diffused within fluid, where the presence of the smell chemicals will be in higher concentration the closer you go to the origin. And so these early bilaterians, look at a nematode, actually realize I can solve the navigation problem, how to find food, without this jump to a rich sensory apparatus if all I do is implement a very basic algorithm, which is when I make an action that detects an increasing concentration of food smell, just keep going in that direction. When I take an action that detects a decreasing concentration of a food smell, turn. And so this very simple algorithm of steering or taxis navigation enables them to turn away and get away from dangerous predator smells and get towards food smells without understanding anything about the structure of the world, without understanding, without any sensory apparatus that enables me to detect objects and recognize objects or any of that. But in order to do this, you need a brain. You need to get to the point where there's a consolidated nucleus of neurons because you need to make tradeoffs now. You can't have a distributed web of reflexes. For example, a nematode is going to face competing inputs. So one of my favorite nematode studies is they put a bunch of nematodes on a petri dish where they put food on one side and they put a copper barrier in the middle, and nematodes hate copper. What they found is they will readily make tradeoffs between, do I cross this dangerous copper barrier, which is toxic to them, to get to the other side to get food? And it depends on two things. One, the relative concentration of each of these. The higher the concentration of the food, the more willing they are to cross. And two, how hungry they are. So they incorporate their internal signals. The more hungry they are, the more willing they are to cross the copper barrier. So in order to do this, I need to integrate two different sensory neurons, one that detects bad things that triggers turning, and one that detects good things that triggers forward momentum into some central nucleus so I can make a single choice as to what is my movement. So the idea, which is not really particularly novel, I think most people in the field would agree with the general idea that taxis navigation was probably the movement algorithm implemented by the first bilaterians. And from taxis navigation came several principles of intelligence on which the rest of brain evolution ensued, which is why I think it's so interesting. First and foremost is this notion of valence, which in the AI community people would just call reward. I wouldn't call it reward yet in bilaterians, but the idea is you classify things in the world into good and bad. So in order to implement the algorithm of taxis, what you need to do is say there are certain stimuli that I deem good, and there's certain stimuli that I deem bad. They directly map to a certain movement repertoire, which is turn towards or turn away from it. If you look at a nematode, what's so interesting is the sensory apparatus directly signals valence, which is not like a human. So this is where you can start seeing simplicity become more complex. When I show a human a stimulus, the neurons in your eye do not directly encode the valence of that stimulus. Whether or not you deem it good or bad or rewarding happens deeper in the brain. But when you look into a nematode brain, that's not the case. They have neurons that detect food smells. These neurons directly map to neurons that trigger forward momentum. So effectively, it's evolutionarily hard-coded what things are good and what things are bad. It's not learned over the course of their life. Now they can change the relative weights of good and bad things based on learning, but there's a very strong bias that's implemented. So that's the idea of taxis navigation. From taxis navigation comes valence and this notion of reward. There's also something called affect, which is sort of the basic template of emotions that emerged with nematodes. This is part of the book that I find really fun to learn about. Where if you also look at how a nematode navigates, there's another complexity that emerges. So what I told you is actually not exactly correct when I said that you put a morsel of food and it creates the smell gradient. That's true in a petri dish. It's not true in the real world. If you actually look at smell gradients in the real world, the problem is there's a lot of noise because there's a lot of other noisy smells that occur, and there's lots of currents. So if you actually look at a smell plume from a source, it's actually very noisy. It dissipates not in a clean gradient. It's sort of drifting around. And that creates a problem because if you actually implemented exactly the algorithm I suggested, it wouldn't be very effective because it's very common that if a nematode passed an area rich with food, it wouldn't get a consistent smell gradient leading them to the source of that food. So what nematodes do is they actually have what people in the field would call behavioral states, which I think maps very nicely to what people who study other types of animals would call affective states, which is behavioral repertoire triggered by a stimulus, but persisting beyond the presence of that stimulus. So nematodes have dopamine neurons that, unlike us, actually have an apparatus that sticks outside of body of the animal. So our dopamine neurons don't have direct access to the external world. They're deep within the midbrain of a human, and they only get information from other parts of the brain. The nematode, which perhaps is suggestive of how dopamine evolved in early bilaterians, actually detects things outside of the worm. And what are dopamine neurons detecting? The presence of food. And so what happens is when a nematode passes a patch of food, it floods the brain with dopamine, which triggers a persistent state of what people call dwelling, which is local area restricted search. And so what this means is it's a clever, relatively simple mechanism for saying, if I detect food, it is a feature of the world that it's probably likely there's other food nearby. And that's evolution taking advantage of a feature of how the structure of our world works. That's not necessarily the case, but in our world, that happens to be the case. So a clever algorithm is to say, if I detect food, food smell, I'm going to continue doing local area restricted search for some period of time until I find other food. Dopamine is a mechanism for triggering this sort of state of dwelling or exploitation or seeking, which we see dopamine doing in early bilaterians and then persisting through our whole lineage, which is cool, I think. Then there's serotonin neurons that detect food in the throat of a nematode, which generates a state of rest and satiation. In other words, after consuming food, it's the thing that makes the nematode pause and say, okay, I've consumed enough food. So this basic dichotomy between dopamine and serotonin, obviously, it's been complexified over 600 million years of evolution, but we see that very basic template, which is dopamine is the seeking pursuit chemical and serotonin is the satiation delayed gratification chemical. And I guess one more analogy before we can jump to breakthrough two is you see this algorithm also implemented in things like the Roomba. Right? So Roomba, which is the robot that goes around and does vacuum cleaning around your house, I think has some cool analogies to the early bilaterians because Rodney Brooks, who also kind of has an evolutionary lens on building autonomous systems, built the simplest possible robot, which is, and it's akin to the bilaterian in the sense that it actually, modern ones do build a map of your world, of your home, but early ones did not build any map. All it would do is detect if it hit something, and then it would turn randomly, which is akin to taxis navigation. But what they did is they realized that a problem with the Roomba is it wasn't taking advantage of detecting dirty things and cleaning up the whole area. So it was taking too long. So they implemented something called dirt detect, which is almost exactly how dopamine works in bilaterians. If a Roomba detects dirt, it actually stops its normal behavioral repertoire and starts turning in a local region. And the reason is because it's likely the case if it runs into a patch of dirt on the floor, there's other patches of dirt nearby. So it's the same exact basic principle on which dopamine started functioning. So this is what the first brain did, and I'll pause there if there's anything. I know I just did a bunch of talking at you. Is there anything you want to add or ask?

Nathan Labenz: (30:21) Hey. We'll continue our interview in a moment after a word from our sponsors. Yeah. That's a great foundational overview. If, do I recall correctly that there are sort of neurons that encode these somewhat higher order concepts in the brain, or is that not there yet? I'm kind of making an analogy in my own mind to interpretability work on AI systems where people will go in and try to identify sort of higher order concepts. Right? And I had thought that there were sort of internal neurons that kind of fired for keep going or turn.

Max Bennett: (30:59) Totally. So the people in the nematode community call them interneurons. But yes. So there's sensory neurons that detect sort of positive valence things, negative valence things in the nematode, which all connect to downstream neurons in the brain, which then mutually inhibit each other. So there's, and I'm simplifying slightly, but there's neurons that in general code for forward momentum, and there's other neurons that in general code for turning. And these, through a somewhat complex web, end up mutually inhibiting each other. And those connect to downstream neurons that control sort of the locomotion motor program in the nematode. So yes, there are sort of intermediary neurons, which is important because what you need is to integrate inputs from multiple positive valence neurons and multiple negative valence neurons and eventually get to the point where you're making a single choice. So yes, there are intermediary neurons for sure.

Nathan Labenz: (31:52) Yeah. Okay. Cool. So even there, you can kind of see, you know, the beginning of an analogy to even a transformer, you know, which is many, many more neurons, but the same kind of structure of the input layer that sort of works up into these higher order concepts in the middle layers and then cash out to some behavior on the other end. It only takes 302 neurons in her brain to have an early form of that, which is pretty amazing. I would say let's keep going. So breakthrough number two is reinforcing. Tell us that story. Max Bennett: (32:25) If you fast forward around 500 million years from when the first bilaterians were swimming around the seafloor, you enter the Cambrian period. Probably many listeners are familiar with the Cambrian explosion, and this is where you get this massive diversification of life. For the most part, it's bilaterian life. The explosion of life that occurred during the Cambrian period descends from our bilaterian ancestors. Most of the dominant species during the Cambrian period are actually arthropods, which are the class of animals that include things like insects, although insects had not emerged yet, spiders, and crustaceans. Vertebrates, which we are, the animals with a vertebral column, were small, in general middle of the food chain creatures, and they most resembled the modern fish. These were our ancestors. Within this explosion of huge arthropod predators, some of them massive, like the size of a human, there was a little fish-like ancestor swimming around. This fish-like ancestor evolved the very basic template of the vertebrate brain.

What I found most fascinating when I started going back and reading about all of this is most studies on comparative psychology and really neuroscience in general are done on mammals. Rats are the mainstay of neuroscience research. There's some work done on fish, but it's a very small minority. But when you look at all vertebrate brains, if you want to know about the human brain, the fish brain gets you 70% of the way there because the fish brain has the very basic templates of all vertebrate brains. They contain a forebrain, a midbrain, and a hindbrain. The very basic substructures that maybe listeners have heard about. There's a cortex in a fish that gets sensory information and recognizes patterns. It projects to the thalamus and the basal ganglia, which work in largely the same way. There are midbrain structures for dopamine. All of the basic template of how vertebrate brains work you find in a fish.

What was going on in this very early brain? I think we can get insight by looking at non-mammal vertebrate brains. Mammal brains have some very unique structures to talk about in Breakthrough 3. In the vertebrate brain template, some of the most important structures that you see are something called the cortex and something called the basal ganglia. What is not as appreciated in the neuroscience community is that there is a cortex in fish brains. A lot of people think about how the cortex evolved only in mammals, but there is a cortex in fish that performs somewhat similar processes. It gets similar types of projections back and forth between the basal ganglia and the cortex.

What are all these things doing? If you look at what a fish is capable of doing, one of the most obvious things is it can learn from trial and error way better than a nematode could. You can just go on YouTube and watch people train fish to do crazy things. You can train a fish to jump through hoops. You can train a fish to navigate through a maze, and you cannot train a nematode to do any of these types of tasks. One of the interesting things about looking into the very basic template of a vertebrate brain is you start seeing the reward signal, which doesn't really exist in the same way in a nematode, now becoming dopamine.

I'm going to go back in time to the early 20th century where we were starting to learn about learning theory in general. There's this guy, Edward Thorndike. He originally wanted to study human learning, and he couldn't get his program to let him study human children. So he ended up studying chickens instead, which I think is a really funny story. He thought, in general, he was a starch Darwinist, "If we can find the principles of how simple animals like cats and birds learn, then maybe it applies to human children." What he originally was looking for was, can we teach these animals through imitation learning? He wanted to find a notion of insight and imitation. The way he set this up is he created these puzzle boxes, these famous studies that he did. He would put a cat in a cage, put some food outside of the cage that they were obviously motivated to get to, and there would be some very basic contraption in the cage that if the cat did the right thing, the cage would open and they'd get the food. Sometimes it'd be a latch that if they pulled, the cage would open. Sometimes he would just sit there and watch, and if they licked a certain paw, he would open the cage. Effectively, it was a puzzle they had to solve.

He was expecting to find two things. The first was that when they figured out how to do it, the time to get out would immediately go to zero. This was his notion of insight. In other words, once you figure out the puzzle, then you never need someone to tell you how to do it again. The second thing he thought he would find was imitation learning. If you put another animal in front of them who had already figured out how to do it, they would become faster. At least in his studies, he didn't find either of those things. But what he did find is there was a progressive decline, almost like a steady curve, of them figuring out how to get out of the cage. He thought about this, and he called it the law of effect, but really it's just trial and error learning. What he realized is what animals do is they just try a bunch of random things, and they become on average more likely to do the things that worked and less likely to do the things that did not work. In other words, after I get out of the cage, it reinforces the prior behaviors that I took and makes them more likely to occur. It's not insight in the sense of "Oh, I figured out the puzzle," but it's making me more likely to take behaviors that I just did before. Over time, eventually, the time it takes to get out of the cage went to zero.

He came up with this idea of, "I wonder what portion of learning is just trial and error learning." Then this guy, B.F. Skinner, ended up taking this to the extreme and tried to claim that all learning across all animals was just trial and error learning, which has been proven to be false, which we'll get to in Breakthrough 3. But the interesting insight is a surprising quantity of intellectual behaviors is in fact trial and error learning.

This is where the second breakthrough with reinforcement learning comes around. If you look at fish, the same sort of puzzle experiments can be done on fish and they learn in exactly the same way. Thorndike actually did this because he was interested in studying fish. You can put a fish in a tank, and you can put these walls in the tank with certain areas where they can swim through. So there's only certain sections they can get to. Then you make one area light, which they don't like, and one area dark, which they do like, and see how long it takes the fish to get through the puzzle. The same thing will happen. It first takes them a long time, then it takes them slightly less, and eventually they do it immediately. If you take a nematode, you don't see this happen. You cannot train a very basic bilaterian to learn through trial and error in this way. So something happened in the jump from bilaterians to early vertebrates.

This is where AI actually is a really fascinating part of the story. We have tried to implement algorithms the way that Thorndike believed that reinforcement learning worked, which is when a good thing happens, you reinforce recent behaviors. Marvin Minsky, I think it was in the fifties, creates something called SNARC, which I forget exactly what it stands for, Stochastic Neural Analog Reinforcement Calculator, I think. It's a very basic algorithm that was trying to teach something to get out of a maze, a very basic little AI agent to get out of a maze and just reinforce it whenever it succeeded. The idea was, "Let's take the idea that Edward Thorndike had, apply it to an AI system, and watch it learn through trial and error." Does not work. The reason it doesn't work is because of something called the temporal credit assignment problem, which is most actions that lead to good rewards are not directly proximal to the reward.

A good analogy would be a game of chess. When you play chess with someone, the actions you took that led you to win are not necessarily the actions at the very end of the game. The things that might have won you the game might have been in the middle of the game. When all you do is just reinforce the recent behaviors, you don't end up getting a very smart agent. If you try to reinforce all the behaviors, it takes way too long. You need way too much data to actually get it to learn how to play a good game of chess. This was a huge problem for decades in AI where we could not figure out how to get trial and error learning to work. When it eventually was discovered, which I'll describe in a second, it actually ends up illuminating how vertebrate brains work because we can see very similar algorithms being implemented.

This guy, Richard Sutton, came up with this brilliant idea. Maybe the way that trial and error learning works is we don't reinforce ourselves when we get the actual reward. The reinforcement signal comes from changes in predicted reward. It's this idea that when you're playing a game of chess, there's actually two opponent processes happening. There is one process that's trying to predict what's the best next move, but then there's a separate process that's trying to predict how good is my current state. How likely am I to win? Another way of saying what's my expected future reward. His idea was maybe the reward signal that trains the system, the policy network, which is the network that's deciding what move to take, is not when you win. It's when the expectancy of the move you just took increased the likelihood of winning substantially.

There's some intuition here, which is when you're playing a game of chess, when you make a move where you're like, "Oh man, my position just dramatically improved," that feeling, that rush, that's the reinforcement signal. His idea of temporal difference learning, which is the idea that the reinforcement signal is temporal differences, changes in expected future reward, is actually what reinforces brains. This is the insight that unlocks the reinforcement learning revolution. The first practical application was something called TD-Gammon, where this principle is used to make backgammon games work really well. It has since been applied to far more complicated games like chess and checkers. This is the idea that the way to get reinforcement learning to work is you need to have an ongoing prediction as to what the likely future reward is.

Now we go back to fish brains, which are eminently capable of learning through trial and error, and we look at the structure called the basal ganglia, which is one of, I think, the coolest parts of the brain. I won't bore people with the nuances of how it works, but if you're interested in neuroanatomy, I think learning about how the basal ganglia works is one of the most tantalizing structures for two reasons. One, it is a beautiful mosaic structure. The way it works is so clear, whereas if you look at a place like the neocortex, it's a complete mess. It's very hard to decode what's actually happening. And two, it's incredibly conserved across animals. The basal ganglia in a fish works almost exactly the same way as the basal ganglia in a human.

No one knows exactly what it's doing, but there's some pretty strong theories that it's implementing something quite akin to Sutton's TD learning algorithm, because what we see is this mosaic in the basal ganglia between circuits that attach to the brainstem motor system that bias actions in one way or another on the basis of dopamine bursts. When it gets dopamine, it becomes more likely to do an action, and when it gets a decline in dopamine, it becomes less likely to do an action. We actually know the exact circuitry for how that happens. There's a mosaic within the basal ganglia that projects back to dopamine neurons that seems to trigger increases in dopamine and also pause dopamine signaling. In other words, reinforcing and punishing itself based on its expectation of future reward.

There are a lot of people that have done some modeling and tried to map the neuroanatomy of the basal ganglia to Sutton's idea of TD learning. It's still an open area of research, but there's a lot of evidence to suggest that what it's doing is something quite similar to this. When you record dopamine neurons, this is the best evidence for this. This hasn't been done in fish, but if you do it in monkeys or mice, dopamine neurons seem to signal almost exactly the TD learning signal. What you see is phasic dopamine, big bursts of dopamine excitement, occurs not on reward delivery, but on changes in expected future reward. You hook up the dopamine neurons within a monkey, when a cue appears that it thinks is predictive of future reward, that's when it gets a big burst of dopamine, not when you actually give it the reward. That actually was a big perplexing finding in neuroscience until people realized that Sutton's TD learning works in practice, and they realized, "Well, this is a new interpretation of what dopamine neurons are doing."

If you go into a fish brain, there is good evidence. We haven't recorded, to my knowledge, dopamine neurons directly, but you have recorded the surrounding structures that do show these sort of prediction error signals. In other words, it's triggering excitement when something just changed that suggests you're about to get a positive reward or a negative reward. The evidence suggests that fish brains are doing this same TD learning algorithm to enable them to learn from trial and error.

What I think is so cool about this Breakthrough 2 is it's only possible if you have Breakthrough 1. You cannot implement a TD learning algorithm without having a grounding notion of valence at the bottom of it. In other words, a notion of what is good and bad in the first place. That is the baseline on which expected future rewards are built on top of, because you need the actual reward signal, which only existed with taxis navigation in nematodes, because that's what classified true good or bad things.

From this idea of reinforcement learning, you get all these other things that emerge in vertebrate brains because now once you can learn arbitrary behaviors through trial and error, all these other things become possible. For example, now it's really useful to recognize more things in the world because that enables me to have more complex suites of actions I can take on the basis of way more things I could recognize. The cortex is one of the structures that evolved in early vertebrates that seems to do things like pattern recognition. Some fish can recognize human faces, so they're very good at pattern recognition.

Another thing that emerges in early vertebrates is a map of 3D space. A simple study for this is if you have a grid of, let's say, 25 different containers, the only cue for where you are in this map of space, let's say the side of the wall of this tank maybe has a blue dot. Wherever you're placed, theoretically you have sufficient information to build a map of the space. Let's say you put a morsel of food under one of these containers, an arbitrary place in this grid of 25. Eventually, a fish will find that location. If you put a fish back in the tank without any food, it will go right back to the same location, irrelevant of where you place it.

This is an interesting biological tweak on standard reinforcement learning agents. It's not actually just learning the suite of egocentric actions to take. In other words, "What move do I turn right or left in response to a cue?" It has something a little more advanced, which is, "Given my location in a 3D space, what is a homing vector to another location that I would like to be at?" That's a much more effective way to engage in trial and error learning because it's much more robust to changing starting locations and all of that. You can go into a fish brain, you can see the homologous region of the cortex, which ended up evolving to our version of the hippocampus, which seems to encode locations in 3D space. This is just a useful tool that will end up mattering for Breakthrough 3.

With vertebrate brains, you get arbitrary trial and error learning with the basal ganglia. You get the cortex, which can learn arbitrary patterns, which enables the basal ganglia to learn more complex behaviors because it can recognize more things in the world. Any questions on that?

Nathan Labenz: (48:53) The questions I have following up on this reinforcement, I guess one is just an observation that we do see this dual model pattern across a number of different places in AI specifically. The GAN architecture is not exactly this, but it's a similar thing where you've got the generator and the discriminator and they kind of bootstrap together to higher capability. More certainly to the frontier models of today, the reinforcement learning from human feedback is predicated also on a reward model that is going to score something with its estimate of what a human would score it as. By applying that to outputs, now you can reward the better outputs from the core model.

Those are both interesting. Also, the rewarding incrementally along the way has become a big trend that was key to, and I think it's probably used in many places, but one that stands out to me is OpenAI's result from, I think, first maybe 2023, where by rewarding reasoning incrementally through the process of mathematical problem solving, they're able to get language models to do much better on math by just giving a lot finer grained reward signal step by step rather than just saying, "Did you get it right or wrong?" at the end and rewarding that. I think of that as kind of a

Nathan Labenz: (50:21) sparse reward problem.

Nathan Labenz: (50:23) Just sparse. You don't get many right, so how do you kind of get it on the right track when basically it's wrong all the time in the early days? They had this problem with web agents too when they started off trying to do a web agent some years ago. They basically just found, "We're not getting any positives, so we have nothing to reward." It kind of didn't work. But rewarding incrementally earlier definitely seems to be a big trend toward more reliable performance. Those are just some reference points, triggers in my own mind that I think I can understand a little bit better in light of this history.

The other big thing that I'm wondering, this is maybe a question we could ask across all five breakthroughs, but particularly here I'm thinking, there seems

Nathan Labenz: (51:09) to be a cluster of breakthroughs. There's a lot

Nathan Labenz: (51:12) of discussion right now in AI too about the notion of emergence, which is to say, and there's a lot of definitional questions about emergence as well. Without necessarily trying to provide the proper definition of emergence, one candidate definition is that certain capabilities come online suddenly. We can look at loss curves and say the loss curve is dropping smoothly, but sometimes these individual capabilities seem to come up comparatively much faster. There's a lot of debate as to whether that's exactly happening or maybe if you look at it the right way, you can still find a smooth loss curve for a lot of these things.

I guess I wonder, how sharp do you think these breakthroughs are? Do you have a mental model for one thing happened and it unlocked these other things? Because I could, you could also say, well, 3D spatial modeling sounds like a pretty good candidate for a breakthrough, right?

Max Bennett: (52:12) I think this was the task, this is why I look at the whole book as a first approximation, because it is clearly the case that all of these were broken down into small incremental generation by generation changes. It was not the case, of course, that two nematodes had a baby and then a fish was born. The goal is to, which there is some taste that comes into this, where other people could have different tastes and look at the same story and say, "I think I would look at this as seven different steps as opposed to five," or "I would look at this as three steps." I think all could be equally right. It's a trade-off between complexity and predictive power.

I think 3D spatial modeling may have emerged at an importantly separate time period. Part of this taste is constrained by the evidence we have available today. For example, we don't have good existing animals that show the gradual developments between nematodes and vertebrates. There's only a few chordates still alive which are non-vertebrate. In other words, animals that are in this middle ground. There's very few of those animals alive, and there's not a lot of comparative psychology studies on them. We don't have really good comparative psychology studies on the most primitive vertebrate, which is the lamprey. I have tried tirelessly to find this. I don't think we even know if they have map-based navigation. It would be a fascinating finding if we found that a lamprey is incapable of knowing locations in space, but a teleost fish is. That would suggest that there was an important milestone that occurred between the first vertebrates and the first jawed vertebrates as an example.

The point is we're constrained a little bit on trying to come up with a first approximation that gives us a basic understanding of the steps by which things evolved without adding so much complexity that we lose the intuition. The goal is a first approximation, not exact accuracy. We're also constrained by the evidence we have available, which is we don't have rich comparative psychology studies across all of these diverse species. Many of the animals that would show us the gradual development of brains are died out. We don't have modern animals that show what these intermediary steps would be. But you're absolutely right. The basal ganglia clearly evolved in steps. The cortex evolved in steps. These things probably did not all emerge all at once, 100%. But I do think there's insight to be garnered by even looking at the clustering of, within this 50 million year window, you get all of these things coming at similar times. I think there is insight to garner about how do these things actually relate to each other.

What I found so interesting about writing the story in the book is, it is often the case, not always, but often the case, that suites of new abilities that seem to emerge at a given lineage tend to come from common and similar underlying neural structures and algorithms. That's really interesting because what seems like very different things are actually different applications of the same technique. I think this actually emerges far more with Breakthroughs 3 and 4, where you see that a lot of the new abilities that emerge in mammals are in fact, or could be conceived as, different applications of really one new algorithm or new ability that emerged. Vertebrates are a little bit harder because there's very differing types of abilities like map-based navigation, pattern recognition, and trial and error learning. But those are all related.

For example, pattern recognition is not relevant unless you have trial and error learning. If you don't have the ability to learn that this arbitrary pattern is something I should do this behavior to, then it's not going to be particularly useful to be able to recognize a pattern unless I have some clever mechanism to map the recognition of this thing to what behavior should I take in response to it. They are related. It's also not very useful to have arbitrary trial and error learning unless I can recognize rich things in the world. Because I'm so constrained to the tiny amount of sensory neurons in a nematode, this deep, very expensive brain to learn to take arbitrary behaviors where I can't even recognize much about the world at all is probably not that relevant. It's probably cheaper to just toss that away and stick with a simple low-energy brain that can't do that. You can see why these things benefit from each other. But yeah, definitely do not think it happened where one generation emerged and then all these abilities were there.

Nathan Labenz: (56:25) Yeah. Help me understand that just a little bit better. This may be, probably is, I mean, there's obviously a lot of major divergences between the biological intelligences and the artificial intelligences. But I could imagine, especially taking a little bit more of an AI lens on things for a second, if I go back to the worm, I could imagine bolting a ConvNet detector onto the side of it that would pattern match on a predator or something, and feed that signal into the rest of the system to say, "Get the hell out of here because I just recognized the face of a predator or something." Now that may just be implausible to develop. Obviously, these are small things. They don't have a lot of luxury of carrying around extra weight. But it does seem like you could have value in these detectors, higher order detectors, still without the reinforcement. But I might be missing something there.

Max Bennett: (57:25) I think what you would suffer from is the same problem that Minsky had with his Snark. If you don't have the ability to reinforce yourself on the basis of changes in expected future reward, it's not so clear that you're going to end up having intelligent behaviors emerge. Because if I can recognize something and the only way I'm learning about it is whether something good or bad happened within a small window of 2 seconds. The nematode, simple bilaterians, their associative learning is constrained to effectively a 2 second window. So they can make associations between a bright light and something bad happening, like they get a tap to their side or something painful, and then they are more likely to recoil or turn away from that light in the future. But it has to happen within 2 seconds. Contingencies beyond that 2 seconds are not learned because they don't have this arbitrary reinforcement trial and error system. So maybe it would be valuable, but now we're into energy tradeoffs, which is how valuable is it to be able to have this ConvNet that maps to turn towards or away when all you can do is make a mapping within 2 seconds of something occurring relative to the energy costs of that thing. So maybe one outcome is it's not energetically worth it, and that's why it didn't evolve. The other outcome could simply be that it's just not evolutionarily available, where some other things had to happen that are just lost to time to get the brain complex enough where this type of pattern matching is even possible. In other words, the reason that didn't happen is not because it wouldn't have been useful, but because of evolutionary constraints. You needed to have a bigger body. You needed to have a bigger base brain with more complex motor behaviors. Who knows what very basic neurobiology had to be there for that to be an available iteration to emerge. The thing that's really interesting about evolution is each iteration needs to be adaptively valuable or at least not adaptively bad. It's possible to have things that emerge that are what's called a spandrel, where it's not adding adaptive value yet, but it evolved for some other reason, and then later it'll have value. But for something really energetically costly, it's unlikely to emerge unless each iteration adds value. That puts a big constraint, which is you need to conceive. This is why understanding how lens shaped eyes evolved is still such a fascinating thing, which is this incredibly complex structure. But what are the incremental steps by which every step of the way was either neutral or positive on adaptive value? So, yeah, I think that would be my thinking around why maybe early bilaterians didn't just add a convolutional neural network.

Nathan Labenz: (1:00:06) This certainly does have interesting implications for the relative evolutions of AIs versus biological systems. It just strikes me that, you know, the separation of hardware and software is so, I mean, there's possibly a distinction to be made on the biological side there too. But certainly on the AI side, that distinction is so complete that these questions of, like, does something have to be energy justified? We don't have to maintain homeostasis in AI systems. There's just a lot more degrees of freedom and ability to kind of remix without concern for these practical constraints.

Max Bennett: (1:00:49) Which is an interesting reason why I think it's likely, you know, in the near term at least, we're going to see meaningful divergence between the way that our AI systems work and the way biological brains work. And this is where I think there is a very fair critique of, you know, we need to have good taste when we take inspiration from neuroscience and apply it to AI. Because neuroscience, the way brains work, as magical as they are, is a process that is constrained in ways we don't need in our systems. For example, we perhaps have access to far more energy, and we're willing to be way less energy efficient. And so that means that the parts of the brain that are optimized for energy efficiency, perhaps, at least in the near term, don't matter to us. So it's not worth the tireless effort to try and figure out how brains do that. Brains also have huge evolutionary constraints, so it's very likely that's not an optimal design. Versus when we implement things in silicon, maybe we can find an optimal design, throw away a previous design from scratch. So I think it's likely that we're going to end up with AI systems that work in very different ways because they don't have the same constraints that brains do. And so the taste question, which is a hard one, obviously I don't have all the answers to it, thoughts on, is what are the aspects of how brains work that are relevant, which is not obvious. The goal is clearly not to recreate the human brain wholesale, but what exists in the neuroscience of what we know about brains or don't yet know that we think will be useful and practically relevant to building smart AIs? That's sort of the hard question.

Nathan Labenz: (1:02:13) Okay. Cool. Well, let's do the next 3 breakthroughs still, and then I have some directions that I want to take us down on some of those future oriented questions too.

Max Bennett: (1:02:23) All right. So these will, I think, go faster because they're actually, in some ways, algorithmically more complicated, but conceptually much simpler. So in mammal brains, which evolved during the era of dinosaurs, there's a really interesting story, which I won't go into, on like how we went from fish to little squirrel-like mammals. It's a fascinating story, the series of extinction events, making our way onto land, dinosaurs emerging, this whole rich history. But eventually, around 150 million years ago, we find ourselves as little 4 inch long mammals in an ecosystem with gargantuan massive dinosaurs. We are very close to the bottom of the food chain. We're hiding in burrows, only coming out at night to hunt for insects and then run back inside. But what is fascinating is inside this tiny little brain evolved a new structure, an area of the cortex. Early vertebrates had a cortex which is a 3 layered structure. So if you looked at a lamprey brain and you looked at the cortex, there are 3 layers of neurons wrapped around each other. But if you look into any mammal brain from human to monkey to rat, you see a structure of their cortex which has 6 layers, and this is called the neocortex, which actually on the edges goes back to the old structures, which have 3 layers. So the olfactory cortex of mammals still has 3 layers, just like early vertebrates, and the hippocampus of mammals still has 3 layers, like early vertebrates. But this neocortex, this area in between, got really, really much more complicated. And so the question is, what did this new neocortex do? And so in human brains, this is where evolution, I think, is so interesting and instructive. Because if all we did is study human brains, we would say the neocortex does practically everything. I mean, if you go into the brain of a human, pretty much everything that you consciously experience seems to occur in the neocortex. For example, if you get a concussion to the back of your head, which is the visual cortex, so the neocortex is this 6 layer sheet around the whole brain, all the folds you see when you look at a picture of a brain, that's neocortex. And there's different subregions that seem to do different things, as an example. The back region is called the visual cortex. And if you get damage to the visual cortex, people will report being blind. So they will say, I cannot see anything. And if you wave, they will not respond. But we know that their brain is still processing visual information. For example, if you throw something at their face, they will blink. If you show a scary picture in front of them, they will report feeling fear or their heart rate goes up, even though they won't be able to tell you why. And we know exactly why this occurs. It's called blindsight. And we know this because the optic nerve goes into different parts of the brain. There's one pathway that goes through the thalamus to visual cortex, which seems to be a place where we consciously experience things, recognize rich objects, etc. There's another path that goes directly to the amygdala, which is an older structure. There's a homologous region in vertebrates, which is sort of like a basic pattern detector, which can trigger emotional responses. So the neocortex seems to be where the complex stuff is happening that you consciously experience, but there's still lots of other older regions of the brain that are doing stuff. If you go to the front part of the neocortex, there's a ring called motor cortex. If that gets damaged in a stroke, you become paralyzed. You lose your fine motor skills. There are regions in the brain that at least classically have been language areas. If you get those damaged, you lose the ability to speak or to understand language. There are auditory areas. So when you look at the human brain, it's like, man, this one structure seems to do everything. It enables complex movement, planning, object recognition, all that stuff. But if you go all the way back to early mammals, if you look at a rat, it's not so clear how important their neocortex is to all of those things. For example, you can lesion a rat's motor cortex, and it can still move around just fine. The paralysis associated with motor cortex damage is actually unique to primates. And I think that's a fascinating finding because what it suggests is when we think about the neocortex as being able to do things like it's where we control movement, there might be something more complicated going on because when we go back to early mammals, it wasn't the region that controlled movement. That was actually an adjustment over evolutionary time. So when we go into a rat, what is the neocortex doing? Well, if you damage the motor cortex of a rat, they become unable to learn new motor skills, but they can execute well learned motor skills. And they become much worse at fine motor behaviors, in other words, things that require in real time motor planning. A cat becomes much less able to look out at a bunch of small platforms and plan its steps throughout that. And so I think this is a very interesting suggestion that really what motor cortex was originally doing is enabling things like motor planning. In other words, simulating your actions before you do them. And this also goes to another area of research around what the neocortex is doing where people think about it as a generative model. In other words, it is a model that does 2 things. It can both recognize things in the world, but it can also generate its own samples of things in the world. And so the original intuition for this came from visual illusions that were identified in the 19th century where I'm sure everyone is familiar with this. You can show someone something that is ambiguously a cat or a rabbit or a duck, and you can only see one or the other, or you can show someone like a Dalmatian set of patterns that they don't recognize. But then you say, actually, do you see the dog there? And then all of a sudden, I cannot not see the dog. Or there are examples where you show arbitrary images that people can't help but see a sphere or a triangle there, even though there's not in fact a triangle there. And so what all of these suggested, there's this guy, Helmholtz, who came up with, he used different words, but his idea was perception as inference, which is what happens when we're seeing things in the world. What we're actually doing is we're trying to infer the true state, and then we use that as our prior when we predict new things. So in other words, when we see this ambiguous picture of a triangle, if it were the case that there were in fact a triangle there, that would well predict the observation I'm seeing. And the reason why this is so important is if you think about an animal navigating in the dark, once I have a prior about, oh, I remember seeing the branches over there, I think I now have the base understanding of the world that contains these branches. Even as I start moving, if the sensory stimuli doesn't prove to me that it's there but is consistent with my prior, then I'm happy with it. I'll keep going with my prior until new information emerges. So this type of, this is the Bayesian brain hypothesis. This idea that we try to infer a model of the world, we try to infer what's the state of the world in my head, and then I'm going to hold it as a prior and compare predictions from this against what actually happens. There's a lot of evidence that's what the neocortex is doing. And so a lot of people talk about this neocortical generative model through the lens of it being really good at things like object recognition, which the neocortex is good at. But I think from an evolutionary lens, a sort of alternative way to look at it is perhaps what's most useful about the neocortex is not the recognition mode, which is the mode where I'm receiving sensory stimuli and comparing it to my prior about the world, but the generation mode, which is I'm cutting myself off from the actual world, and I'm exploring the representation of the world and generating my own data. In other words, simulating possible things. And the reason why I think from an evolutionary lens this is really interesting, it's hard to argue. I think it's hard to argue. One could argue this, but I think it's hard to argue the neocortex evolved for object recognition. Because without a neocortex, a lot of vertebrates that just have a 3 layered cortex are unbelievably good at object recognition. And they can do it with 3D perturbations and 3D rotation. Like, all of the things that we would think a good object recognition system should do, fish seem eminently able to do. So it's, unless you argue there's some energy efficiency or there is some unique improvement in performance the neocortex offers, I think it's sort of hard to argue that the driving adaptive value of neocortical evolution was superior recognition. I think what's much more compelling and consistent would be it was the first time when animals could actually render a simulation of a state of the world that's not the current one. In other words, building a model of the world based on experiencing and perception by inference, but then having the ability to pause and play out possible futures before I act. And that is something that we do see across mammals. So if you take a rat and you put it in a maze, unlike fish, we haven't found this yet in fish. It would be a fascinating observation if we did. This is where a lack of comparative psychology studies creates challenges. But with mammals, we know this happens. A rat navigating a maze, you'll see it if there's a choice point, you'll see a rat pause and actually look back and forth. And so this was called vicarious trial and error as a hypothesis, this view that rats seem like they're thinking about their possible actions before they make a choice. But it was a hypothesis. There's no way to verify that rats are actually doing this until this guy David Redish came around and actually recorded parts of the hippocampus, which, if you remember from early vertebrates, render maps of space. And there are certain neurons in the hippocampus called place cells. So as a rat navigates around a maze, there are specific neurons that activate at each location in that maze. You can actually look into a rat and see, oh, there's a neuron that detects that no matter which direction they come from, it's always going to activate at this location in the maze. Same thing for this other location, etc. And so, usually, place cells are primarily active for the location that the rat is actually in. So as it's navigating the maze, you can see the place cells just change for the location it's in. But when it reaches these choice points and looks back and forth, you see something mind blowing. The place cells cease to activate only the location they're at. You literally can watch it playing out possible futures. You can see the place cells start playing out different paths down the maze until it actually makes a decision. So you can watch rats imagine the future. And that's a really fascinating finding. And when you look at other abilities that you see emerge with mammals, it's consistent with this idea of being able to simulate states of the world that's not the current one. For example, counterfactual learning. So counterfactual learning is being able to learn what would have happened had I taken a different action. There's a key limitation with trial and error learning. In trial and error learning, you can only learn from the actual actions taken. So I think a very simple example of this is rock, paper, scissors. So if you teach a monkey to play rock, paper, scissors, if the monkey plays rock against paper and loses, with trial and error learning, what you would expect is it's now going to punish the behavior of rock. So the next time, it's going to be equally more likely to do paper or scissors because one of the 3 actions has been punished. But if it has counterfactual learning, if it can look at paper and say, well, if I had played scissors, I would have won the last round. In other words, if it's able to simulate possible actions, you would expect it to be more likely to play scissors. And if it was really, really smart, you would realize there's actually no relationship between these, so it's all random, which is what humans can realize. But what monkeys do is they become more likely to play scissors, which demonstrates an ability to consider what would have happened if I had done something different. And in rats, you can do sort of a similar thing where you can, David Redish did a study. I won't go into the details, but you can watch a rat actually play out different past choices and then change its decisions on the basis of it. You can go into the brain and watch its orbital frontal cortex rendering a different outcome had it actually made a different choice. You can literally watch that happen, which, orbital frontal cortex is another region of neocortex. The other thing that emerges is something called episodic memory. This is a loaded term. Some people call it episodic-like memory, but the idea to render past events, which we also see in rats, which lots of recent studies have shown, episodic memory, remembering the past, is actually the same process as imagining the future. Because all we're doing is we're rendering another state of the world that's not the current one. So this idea, and again, in this breakthrough model, what I think is so cool is you cannot have simulation without reinforcement. There is no way to benefit yourself from imagining possible futures unless you have some mechanism by which to change your behavior based on those outcomes. For a rat to be able to say, let me imagine possible futures and then reinforce the ones where I get the best outcome in my imagination, you need the same apparatus that's learning through trial and error. And you see this when you go into brains as they imagine possible futures. They're effectively reinforcing themselves vicariously based on their imagination. And thus, then when they go back into acting, they're playing out their imagined action sequence. So the hypothesis here is that the neocortex evolved. It gave a new mechanism for perception, perception by inference, but the core adaptive value is the ability to simulate a model of the world, which we see across mammals. And this is why mammals are good at fine motor skills because, you know, a cat can plan where to place its paws before it does so versus a lizard. If you watch a lizard run, there's lots of evidence. They're not actually planning their movements ahead of time. That's why, you know, most non-mammalian vertebrates, with the exception of birds, are quite clumsy because they don't have this ability. So the neocortex seemed to have enabled simulation, which was again bootstrapped on the trial and error learning, which we see emerge in neocortex.

Nathan Labenz: (1:15:56) Yeah. This is kind of where, I guess, I don't know if you would agree with this characterization, but it seems like we have pretty good AI versions of the reinforcement learning breakthrough at this point. Let's say there could be further improvement in sample efficiency and so on, but it largely works. On this one and then on the next one, we're kind of in the realm of frontier AI capability. We can sort of squint and see that we can kind of get language models to do some of this simulating if we set it up and scaffold it the right way. And you have techniques like chain of thought, and then that gets elaborated into tree of thought. And there's the kind of, well, you know, let me go down this path and then this path. Let me evaluate those paths. So there is something pretty analogous there, although it feels like kind of a hack by comparison. Maybe a broad question is, like, how would you compare and contrast the state of AI simulation counterfactual ability versus biology? And also, specifically within the biological systems, how much do we know about, like, how you get into the sort of counterfactual mode? So it's almost like there must be some sort of switch or gating mechanism. And how do the inputs for the hypotheticals get generated? You know, if I'm thinking about my range of possible futures, there's this fundamental mystery of, like, where did the starting points even kind of arise from? And then I can kind of, I know what I think it feels like to follow those down a simulated path. But, like, how I even got on to these various simulated paths, you know, I don't really feel like I have access to that.

Max Bennett: (1:17:36) Lots of fun thoughts there. So planning in general. So the notion of simulating possible futures is a very old idea. I mean, in the fifties, we were building systems that would engage in some form of planning. It's not the existence of planning that's missing. It's how do we do planning in a way that works in practice? And we haven't really, that's the nuance. And the question is, are we a few architectural tweaks away from that? So will, you know, OpenAI's tree of thought system actually get us very far? It's possible. Or is there some fundamental aspect of how mammalian brains do it that's missing, and that's also possible that we're just missing some key insight. So I think one thing that is clearly different and that's going on in mammal brains, I don't know the open question on how far away this is in AI, but in mammal brains, you're continually active and you're switching between these behavioral modes, as you're saying. So in mammal brains, you have this sort of what might be called model free, which means I'm not actually simulating possible futures. I'm just directly responding to the stimuli I'm getting. Default, which, you know, kind of Daniel Kahneman would say, like, fast thinking. This is like the immediate response to stimuli, and we are going through that process. And then sometimes, for some reason, we intelligently decide when to stop and think. So that's the first intellectual thing that somehow mammal brains are cleverly deciding when is this a choice point that I'm going to think about. I have a general thesis on this, I don't think I'm sure there are other people that have this idea, where it's some notion of uncertainty measurement that drives this pausing. And so uncertainty could be measured in different ways. It could be measured in the basal ganglia where there are divergent predictions of model free actions. It could be measured in the neocortex, which is uncertainty in a generative prediction about the next state of the world. We don't know that yet. But uncertainty triggering pausing, I think there's some good evidence that that is something that's going on in mammal brains. Then there's the question of how do I prune the search space to select possible futures to evaluate, which is your question of, like, when you pause and think, you're obviously not doing a comprehensive search of all the possible futures. That would be ridiculous. So how do mammal brains actually cleverly prune the search space in a three-dimensional world? This is one of the leading problems in robotics, which is the dimensionality of controlling even a basic robot arm is gargantuan. Right? The actuators and all the locations of where you put all the fingers and the arms. It's just such a gargantuan search space that people have not yet figured out. How do you prune that thoughtfully so that it can plan movements ahead of time? A lot of research going into that. And then the last question is how do you decide which outcome you select? And so how do you evaluate possible outcomes after you're simulating? It's not just the notion of planning, but there might be unique breakthroughs in each of these steps or unique technical insights that we need to find and then bring into other systems. Now you could imagine, I mean, I could imagine chaining together an LLM to do each of these things. Right? You could imagine just giving an LLM, you know, a small model, a set of stimuli and just say, should you simulate? Should you simulate? Yes. No. Yes. No. Yes. No. If yes, then you engage a tree of thought LLM that then selects things, and you have another LLM that's evaluating the outcome, saying it's accurate. So you could imagine chaining together an LLM to do these 3 steps. Question is how well will it work? I'm sure we'll find that out soon.

Nathan Labenz: (1:20:51) I think for Max Bennett: (1:20:53) The way that simulating works in the mammalian brain, I think one of the key things that we're going to have to figure out in AI systems is this notion of continual learning. This is where I think the big next insight, if I were to do more neuroscience research, this is where I would focus my time. Because what happens with LLMs, and this goes to their lack of episodic memory too, is I can't talk to it and it just remembers as I'm speaking to it. Whereas I can explain a new concept to you and now you just know it, and you didn't forget all the things you knew before. This notion of catastrophic forgetting has been a problem in deep neural networks for a very long time. I mean, this is why we don't let weights get updated continuously with new information because we know it risks catastrophically losing its abilities. We see this in fine tuning. I've been doing a lot of fine tuning experiments for a variety of different things I've been working on on the side, and the problem with fine tuning is you very quickly get this overfitting issue where it ends up overfitting to the small fine tuning dataset and it loses its generalization. It's forgetting old tasks that it was able to do. This is not a nuanced edge case. I think this is a foundational problem with the way these models work.

And so how does the mammal brain decide when to incorporate new information? I mean, how to add it to my model of the world without interfering with other information is a huge outstanding area of research that I think is a key difference between how mammal brains are simulating and modeling the world and how existing AI systems are doing it. We have some unique ideas in neuroscience. For example, it seems to be the case that it's possible that weights are only updated in times of surprise or new information. So there's some suggestion that there are certain neuromodulators that get released only in moments where you have failed predictions, which then sort of douse synapses in a neuromodulator that enables them to update their weights. So one way we reduce catastrophic forgetting is we're not actually continually learning everything. Only in certain instances do we update weights.

Another thing that I think is unique that people haven't really figured out is this sort of eureka moment that happens with humans, where if you teach me something that's confusing, I don't just incorporate it into my model of the world, which happens with a neural network. I can give a neural network, a transformer, any fine tuning dataset I want, even if the dataset makes no sense relative to its prior information, and it will just start learning because I'm backpropagating through. If you tell me one plus one equals four and you start trying to explain that concept to me, I'm not just going to take that as fine tuning data. I'm going to take my model of the world and then desperately try and grasp how the information you're telling me fits into my model of the world. And until I get it, I'm going to reject the information. It doesn't make sense in my model of the world.

And so something nuanced is happening where robustness is clearly occurring in mammalian brains. There's something where I reject new information unless I can somehow fit it into my established, rich, and robust existing model of the world. And somehow this is enabling, I think, a more generalization across tasks, and somehow this is enabling continual learning in a more robust way because we're not just arbitrarily letting new information update weights. But how that actually works, I don't think we know yet. But yeah, that would be my sidebar on what I think is missing in AI systems that exists somewhere in the brain of mammals.

Nathan Labenz: (1:24:12) Yeah, that's very interesting. And that does bring to mind the state space model moment for me as well, which in the interest of time, I'll keep this brief, but I had a long monologue about it late last year. And basically, I think it at least promises a partial answer to some of these challenges where basically, the addition of a state which is long lived and propagates through time creates a third aspect of the overall system. Right? In a transformer, you've got the weights and you've got the inputs and it's kind of input and output, but the addition of the state gives you this extra thing that can store information, can evolve through time without having the weights themselves have to change. You have this additional degree of freedom in terms of how you want to store information, how you want to represent it.

And the Mamba paper triggered me to go down the rabbit hole on this for a little while, crossed the threshold of basically being as expressive, achieving very similar high level loss metrics to transformers, but doing it in a way that A, is a lot more efficient computationally, but B, also has this additional thing where you can encode memories longer term, I think. We're still in the very early phases of figuring out how we're going to use the state space models in the broader architecture of AI systems. I don't think it's just a replacement. If there's anything to learn from this, well, there's a lot of things to learn. I don't mean to diminish it, but one thing I definitely take away from the five breakthrough model is that we still have all the earlier breakthroughs. Right? We did not, when people say, is this going to be the thing that kills the transformer? I think one thing you could take away from this history of the biological intelligence is probably not. It's probably something that gets combined with and remixed with and hybridized with the existing capabilities and adds something new. Then over time, that will evolve further and maybe bleed together and become inseparable even maybe, but it doesn't feel like we are, when you think about is this going to totally replace that, that seems like a very rare and kind of temporary, strange time in the evolution of these things. More so now we have this new ability and it's another thing you said too is when to accept and when to reject information.

The mechanism of the Mamba paper they call it the selective state space model. Earlier state space models just had, I don't want to speak for all state space models, but broadly, an explicit encoding of the history. So you're just compressing with a known engineered algorithm all history into a state to the best of your ability. You can have different trade-offs there. Do you want to remember recent stuff more, or do you want to weight all history the same, whatever. But the selective mechanism now allows you to update state depending on the overall dynamic. So you're not just explicitly accepting any new information in with a hard coded encoding, but if stuff doesn't jive or doesn't seem relevant, then you can just not update the state accordingly. I think we're probably going to see a lot of little variations on first of all, multi-state models and then a lot of little variations on the selective mechanisms to sort of figure out do we need a bucket, a state to put all the outliers in, or a state to, whatever. There'll be a lot of things there. But I do think this third, some of your comments about it definitely suggest to me that, which I already believe is coming in, but I see a lot of parallels to what the state space models may unlock for AI systems as a whole.

Number four is mentalizing. Cool.

Max Bennett: (1:27:57) All right, I'll try to make this fast paced. So mentalizing, when we look at primates, this is an area where the comparative psychology gets more controversial. So I'll try and caveat things where, in general, most primatologists or comparative psychologists would agree with this, but some would disagree because there's a lot of debates around what non-human apes are capable of doing. But there are three abilities that you see at least good consensus around or at least some evidence for within non-human primates that are consistently not found in non-primate mammals and other animals. And so this is a good way to try and back into what evolves in early primates.

So one is this idea of theory of mind. So theory of mind is this idea of I can try and infer what your intent is based on observing your actions or try to infer what knowledge you have on the basis of what I can see you doing. And so there's lots of really cool studies with non-human primates where you can see these types of things emerge. For example, there are studies where if you let a chimpanzee play with two different goggles, one goggle that you put on, you can't see through and one goggle, you can see through. And then you let two human experimenters put these goggles on and they both hold food. The chimpanzee always goes up to the one with the goggles they can see through. So somehow they're inferring, and there's no reason why you would expect any sort of Pavlovian association with one of them or the other. But clearly, there's some association of this person can see me and this other person can't.

Another good example that you see in non-human primates is there's a study of them inferring the difference between people intentionally doing something versus not. So there's been studies where you teach a non-human primate that of the two boxes placed in the room, the one with a red marker mark on it is the one with food in it. So you train them about that fact. Then you have an experimenter come in and bend over and mark one of them and then stand up and then pretend to accidentally mark the other one. In other words, drop the marker. Stimulus is identical. They always go to the one that was intentionally marked. So there's some notion of the humans are giving me this food and this is the one they meant to mark, so I'm ignoring the one that was an accident, which is really hard to understand without some notion of I understand what you're intending to do.

There's been lots of experiments like this where one of my favorite ones, which is a little bit less rigorous, more of an anecdote, but I think it beautifully describes how rich the lives of non-human primates are. This guy, Emile Menzel, was doing studies trying to just show the presence of map based learning in, I think it was chimpanzees. And so what he would do is there's a one acre forest these chimpanzees were living in, and he would show one of the chimpanzees, hey, I'm going to put this little morsel of food under this bush, and then just see if the chimpanzee would go back to the same location. And okay, great, they learned that, obviously, they go back to the same location. But then he started observing something that he did not expect, which is when, I think her name was Belle, that chimpanzee started sharing the food with the other members of the group, there was another chimpanzee named Rock that ended up being really mean and taking all the food from her. So she started trying to trick him. She would wait till he looked away and then go and get the food. So then he started intentionally looking away so she would go to the food, then he would turn around and run after it. Then she started trying to lead him in the wrong direction until he was far enough away, then she'd run back. There was a cycle of deception and counter-deception that folks have found with the primates, which is only really possible to conceive of if what's happening is I'm trying to trick you by changing the knowledge in your head. I'm trying to give you information that's wrong so you think one thing so I can do something else. And this is incredibly hard to imagine how an animal would do this without some notion of understanding the mind and intentions of others. So that's one new ability we see with primates.

The other one is imitation learning. So non-human primates are really good. Remember those studies with Edward Thorndike where it's sort of hard to find imitation learning in the animal kingdom, but with non-human primates, they do it exceptionally well. You can teach, they've done studies where they take one member of a primate group, and they teach it to do this special trick with a rake to get food. And then they send the primate back out into the world, and pretty soon, the whole troop is using the same exact technique. So it's very clear evidence that they're learning through observation. There's also some evidence, this is more controversial, that chimpanzees do actively teach. So there's been some new evidence that chimpanzees will literally correct the mistakes of youngsters when they're trying to do things like termite fishing, which also clearly requires an understanding of what someone else's knowledge is and how to modify the knowledge.

And so, okay, so we have theory of mind. We see this improved imitation learning emerging in primates. And then the other one, which is the least evidence for, but there is some evidence for, that primates are uniquely good at what's called anticipating future needs, where they can take an action today for a need that they don't currently experience. Humans do this all the time. We go to the grocery store, we buy food for the week, even though I'm not hungry at all right now. And as obvious as that seems to us, it's actually quite challenging to understand how animals do that. And one of my favorite studies on this was done comparing squirrel monkeys and rats. And what they did is they said they're going to give each of these animals a choice between two options. One option is a low treat option, so it's either dates or raisins. But if they go with that route, they get water right afterwards. The other is a high treat option, but they don't get water for a long period of time. And they baseline these between the two animals to make sure that they are quantifying it to induce a similar amount of thirst. And so the point is, am I willing to forego the high treat option because I know in the future I'm going to be thirsty even though I'm not thirsty right now? And what they found is squirrel monkeys would do that. They would go with the low treat option, but rats would not. So some suggestion that they're capable of anticipating future needs.

So, okay, you have these three seemingly totally distinct abilities. I mean, if you go into the brain of primates, you don't see a very different brain from early mammals. There's really two main things that change other than size. The brain obviously is meaningfully bigger, but they got an area of frontal cortex called granular prefrontal cortex, and then they have a few subregions of posterior sensory cortex that are new. And if you look at the connectivity, a reasonable first approximation is what these areas of neocortex are doing is they get input from older areas of mammalian neocortex. In other words, it seems to be building a model of the older model. It's a hierarchical layer above it. For example, agranular prefrontal cortex that exists in early mammals, that exists in rats, gets sensory input directly from areas like the hippocampus and the amygdala. And if you look at granular prefrontal cortex, it gets not enough. It only gets information from agranular prefrontal cortex. So you can think about it as a hierarchical layer above. And the idea here is if you, and there's actually a big mystery about what these structures even did, because there was lots of early studies in the twentieth century that showed people with damaged granular prefrontal cortex didn't seem to, were fine. Some cases, were mostly normal other than their personality being weird.

But then people started realizing that one of the main things they're really bad at is engaging with other people in a socially reasonable way because they really struggle on these theory of mind tasks. So if you start giving them tasks where they need to reason about the changing knowledge of other people, they become quite bad at it. One of my favorite studies on this is they compared people with damage to granular prefrontal cortex to baselines to damage to hippocampus. And they asked them to do something very simple. They said, just can you write a little story about some state of the world in the future? People with hippocampal damage could describe themselves in these imagined stories, but the richness of the world was very basic. They couldn't describe key features of their imagined world. People with granular prefrontal damage were eminently capable of describing aspects of the world in the future, but they were always absent. They really struggled to imagine themselves in this imagined state of the world, which suggests that part of what's happening is it's the granular prefrontal cortex enables you to model you. In other words, it's the source of metacognition, thinking about thinking.

And what's interesting is you see these three abilities emerge along with granular prefrontal cortex. They can actually be understood as three applications of the same thing. So modeling your own inner simulation is effectively creating a model of one's mind, able to simulate the simulation. Another way to think about this is being able to simulate thinking about different things. And so what that enables one to do is I can put myself in someone else's shoes because I can now imagine myself in a different situation and try and predict what would I know and what would I think. You can also much more easily learn through imitation because I can see someone doing a behavior and I can imagine myself doing the same behavior and vicariously train myself doing it and figure out what their intention is.

One of the key things with imitation learning is you can't just wholesale copy. In the interest of time, I won't go into this whole area, but there's a really fascinating area of imitation learning and AI, where we found is one of the key things to making imitation learning work is not direct copying, but inferring the intent of the behavior in someone else. In other words, what are they trying to do? And then vicariously reinforcing yourself to do what they're trying to do. That's a key thing that makes imitation learning work, which would explain why primates able to simulate someone else's intentions become very good at imitation learning.

And anticipating future needs, you could conceive of this simply as for me to imagine myself being hungry, I need to be able to simulate myself in a different mind state than my current. In other words, I need to be able to imagine, just like I would try to figure out why is person A acting this way. They must be really thirsty. Because when I do that, the reason is because I'm thirsty. I can also imagine, well, what am I going to feel if I take this? I know I'm going to be thirsty because I can simulate that behavior, and I know I'm going to regret and want this other thing. I'm going to engage in counterfactual learning and be upset. So actually what I'm going to do is take a different choice.

What I think is cool about this breakthrough four idea is you see one common suite of things happen in the neocortex, which seems to model the self, and you see three new abilities emerge, which can be conceived of as a first approximation. It's just different applications of the same thing, which is simulating your own inner simulation. Mechanistically, how does this actually work? Can we, is what I'm describing rich enough in detail to create algorithmic blueprints to build an AI? Definitely not. But in principle, it suggests that modeling the simulation itself might unlock some interesting things. It also suggests some interesting things in how we relate to AI systems. If what I'm saying is correct, and I'm not the only one that conceived of this, but if the general story I'm telling is correct, one of the key grounding constraints that enables humans to engage and relate to each other is the fact that our brains are not that different. Because there is a synergy between me imagining myself in your shoes being a relatively good proxy for how you actually will feel. And there's lots of cognitive biases that we see happen where I try to project things onto other people when they don't feel that way just because I would. Our differences do create problems with this sort of mental projection.

But if we create an AI system with a brain that works woefully differently than humans, then we're going to perhaps need very different mechanisms to ensure that they understand why we're doing certain things and the intent of what we say. Because the way in which human brains pull that off is at least in part, I think, based on the idea that we have similar reward structures and similar mechanisms by which the brain works so that this sort of mental projection on average tends to be relatively effective at predicting what's going on in the brains of others.

Nathan Labenz: (1:39:45) Yeah, theory of mind is a fascinating topic in every dimension. I've done a little bit of a deep dive at one point into just how good is GPT-4's theory of mind for humans. And it's still controversial, but I would say it's definitely off the zero line at this point quite clearly. And then our theory of mind of the AIs is also an extremely, maybe even more difficult in some ways because our instincts for understanding them are so often misled by our instincts for understanding ourselves and one another. We'll keep then the fifth breakthrough, which is speaking for the book. People can read, or I listened actually to the audiobook, which I recommend. How about some just very kind of high level conceptual questions? I guess one that stands out to me is the architecture of biological brains is very messy, dense connections, not differentiable, right, not linear. It's this sort of mess that has all these feedback loops that are kind of cyclical and all sorts of different geometries. AI, much more linear. Everything has to be differentiable for back propagation, at least in today's paradigms. We've got other kind of evolutionary paradigms, but they're not currently advancing the state of the art nearly as much. Do you have an intuition for how that will shape up over time? Like, do we need more complicated geometries to achieve some of the advantages? I'm specifically thinking about robustness, adversarial robustness that the biological systems seem to have at a much stronger level than the AIs today.

Max Bennett: (1:41:28) Backprop has been so incredibly successful. The thing that brains might be doing that, it's hard pressed to argue that it's going to replace backprop, but it might add a modification to how backprop is used is brains seem to be just being clever about when weights are updated, which enables it to do continual learning. And I think one of the key things we're going to want is continual learning in these AI agents, being able to have them continuously get a stream of information and us be confident that they're not going to lose knowledge about prior things that it had information about.

So in order to do this, the thing with backpropagation is it's almost too perfect because it updates all the weights to be the exact partial derivative relative to the gradient or if you're doing stochastic or batch or mini batch, fine. It's not exactly right, but it's a very accurate computation. The human brain might be doing something less accurate but with some other trick that enables it to engage in continual learning. And so the answer, I would be surprised if the answer ended up being a wholesale we don't do backprop at all anymore. We use this clever mechanism that we figured out the brain worked. But if we figure out the general principle on which how brains maintain representations robustly while learning, my hunch would be, my intuition would be that we can apply that and still use backpropagation.

For example, this is definitely not the answer, but if it were the case that it was as simple as there's some measure of uncertainty, and when uncertainty passes a threshold, there's some process of assimilation of this new information. So some active learning process of take the uncertain new object and try and manipulate it to understand until I feel like I've generated enough data that I understand the thing, and then I go on with my life. In other words, there's an active learning process. You could conceive of doing that with backpropagation, but what you're doing is you have some other mechanism by which there's a pause, a process activated where you're going to allow weights to be updated. Maybe there's a generative replay component where maybe we're going to learn that these AI agents have to sleep. There needs to be some mechanism just like brains use to maintain representations robustly where we're replaying things to make sure there's some consolidation.

So it seems likely to me that to make continual learning and robustness work, there's going to be something new. And it's not clear to me whether it's going to come first from biology or first from AI, but my guess is there's going to be a beautiful cross pollination. If it's discovered first in AI, it might end up being another Richard Sutton story where we go back into brains and realize brains are doing something quite similar to this. Or if someone goes first into brains and figures out something clever, and then we go to AI and we try to apply it. My guess is probably the ideas are already in neuroscience. There's so many ideas on how brains do this. It's going to take someone reading through that literature and coming up with, a good, taking the right things from those ideas to implement them. So yeah, so I would be very surprised if we end up in 10 years, and it's like we've come out with a completely new paradigm. We're not using backprop. It's not deep learning. I think that's unlikely.

I do think there's some interesting things that will go on with macro structures. So which is already sort of happening, where a lot of the work that's been going on is on single models, and we scale up a single model, single input output. And we know brains don't do this. It's not just a broad soup of neurons. There's specialized subsystems that talk to each other in interesting ways. If I imagine a world where we all have autonomous robots walking around our house, I think it's a reasonable prediction that that's not going to be a single model. There's going to be subsystems. There'll be some ConvNet that does object recognition. There'll be a language model. Maybe there'll be some separate world model system. Maybe there'll be some other continual learning episodic memory system, these things sort of come together. I mean, a whole brain macro architecture that's not just one scaled up foundation model.

Nathan Labenz: (1:45:17) Yeah. So many interesting little parallels and connections I'm drawing there. Very early language model things like the pause token, think before you speak, I think that paper was called, and the backspace seems to kind of relate where you're getting out of distribution, your confidence is getting low. We're not yet at the point where we're really figuring out how to internalize that, but with the backspace, you're just kind of like, I think I've taken the wrong path here. Let me back up and try a different path forward where I can hopefully stay more in distribution. I was just thinking too, very much along the lines of what you just said, that it seems like we lack the kind of robustness, the ability to move forward in the broadest sense through the changing environment. That's kind of what the language models despite all the scaffolding can't do. But I think about something like the Open Worm project or I think recently, the whole connectome for the fruit fly was mapped. And I start to think, jeez, what if you took that as a digital blueprint and started to Frankenstein it where you'd go in and maybe replace a neuron with a module or kind of like we talked about with, you know, bolt that on in place of a single food smell detector. Could you put sophisticated object recognition there? And you can almost imagine taking this high level architecture that sort of has the right feedback loops already evolved, but then just soup up a lot of the components. And that could get extremely weird. Probably we're headed for a future of both powerful and weird systems. Does that seem like a viable path, or do you expect things like that to start to happen?

Max Bennett: (1:46:58) I still think it's going to take taste from the side of the modeler to take a connectome of a fly brain and then figure out how to model it for the following reasons. Even knowing the connectome of a brain still only gives you a small percentage of understanding of what is computing, because even if you want to do a whole brain model of it, 1, neurons have different base firing rates, different thresholds for firing, different firing modes between phasic and tonic. So just knowing that 2 neurons connect to each other doesn't tell you how it's going to translate information. 2, the learning rates of synapses are totally different. So if you're on one dendritic spine versus another, if you're close to the soma that has different effects. I don't know specifically about the connectome of the fly brain, but if they don't have knowing what type of neuron is each, I would imagine they do. But if you don't know whether it's an excitatory, inhibitory, or neuromodulatory neuron, then you're not going to know what it actually signaled across the connection. If you don't know what receptor expression is on the post and presynaptic neurons, you're not, it's not going to be easy to model how the synapses will in fact change. So all of that is to say that knowing how things connect to each other doesn't actually give you enough information to do a wholesale model of how it will process information. So that means one of two things. One, we're eventually going to get to the point where we do have enough information to just wholesale model brain, which will be amazing, but knowing connectivity is not sufficient. Or it's going to take a modeler, which is probably going to end up happening first. It's going to take someone with really great taste to say, okay. Well, I'm going to guess that this is what this module is doing, and that's what this module is doing, and this is broadly the information that's being communicated between them. And so I'm going to take the basic idea of it and render a simulation of that and see if it behaves in a similar way to a fly brain. I think that's an interesting approach, but it's still going to take some good intuitions from the sake of the modeler given just knowing the connections is really not enough.

Nathan Labenz: (1:48:55) Yeah. That reminds me of the Wright brothers and their observations around the subtle movements of the bird wings as they were soaring. They didn't obviously just go in and do surgery on birds, but they were able to create something that was inspired by and captures some of the principles of, but obviously, it's been a lot more extensible in important ways for us as well. Okay. So last one. Do you have a unified theory of value or moral patienthood that you could apply to these five breakthroughs? How much should I care about a worm versus a fish versus an ape? And then, if you'd be so bold, do you have any intuition for how that would extend to AI systems?

Max Bennett: (1:49:40) I have an intuition, but I don't want it to be used as a moral authority for how we should treat animals. So I'll describe my intuition, but I'm not an ethicist, so I don't claim to have actually thought through all of the philosophical implications. But my intuition is there is something important on the jump from a model free reinforcement learning system to a model based reinforcement learning system, in other words, mammals, which I think there's independent convergence with birds, by the way. So it seems to be the case that birds independently evolved these different types of brain structures that do the same type of thing. So I think birds seem to also have this. So there seems to be something unique about an animal that can actually pause and think about things and create a model of itself. Now how that maps to the notions of consciousness and all that unclear, but it seems to be the case that is rendered in the neocortex, which would be consistent with this idea of, or this neocortex is essential to rendering, which would suggest that rats are experiencing the same thing. There might be something unique also that emerges with primates when you actually have an identity and a sense of self. My intuition, this is pure speculation. My intuition would be that qualia, the experience of things, is something that evolves in mammals, which is separate from identity and thinking about who you are and your place in the world. And I would put more moral weight on the experience of things than the identity of things. So some basic and speculative intuitions. When one is meditating, I am not, in fact, the joy of meditation is removing myself from obsessing over my identity, who I am, how I, where I fit in the world, and ruminating about ourselves, which I would think is a very primate like activity, and just engaging in raw perception by inference and experiencing the present, which is just not engaging in the simulation of other things, but only just rendering a simulation of the current state. I think that the experience of things is where things can be, one can suffer. So whether or not I have a sense of self, I can still experience suffering. Again, all speculation, but to me, my intuitions lie in, a rat really should have very similar moral weight to a human if you believe that where moral weight is applied is in the actual experience of things. It doesn't need to have a notion of its own identity for it to feel pain and feel fear, et cetera. Whereas for a fish yeah. I feel like the fish psychologist won't love this. But if I look into a fish brain, I don't see, it's hard to correlate the same sorts of things because their brains look so different. But, of course, we could find out that what's happening in the fish brain does correlate to what's happening in mammal brain, then we'll realize we're committing atrocities. So there's risks associated with it. But if you're asking my speculative intuitions, I do think it's at least very hard to argue that humans have strong moral weight over nonhuman primates. I think that's a very hard argument to make. And I also my intuitions would say it also would apply to mammals. In terms of AI systems, this is the trillion dollar ethical question that people are, I think, not talking about. There's the question of how they treat us, which is a problem. But the question of how we treat them is usually problematic because we don't even know I mean, we don't even have philosophical grounding to infer that anyone other than oneself is conscious. You know what I mean? We the only argument we really have, which is not based on observable data, is mechanistic similarity. I just say it seems to be my consciousness emerges from this thing in my head, and I look in your brain, it looks quite similar. That's problematic because we're clearly not going to be able to apply that paradigm to AI systems. And I think most people in AI and in philosophy would hold the materialism view, which is you don't need the biological gooey stuff of neurons to implement the same process. So now we have this big, we have a challenge of how do we know when an AI system is now garnering ethical and moral weight? And I don't have good answers to that, but I do think that that is going to be an emergent challenge of the 21st century.

Nathan Labenz: (1:53:57) Well, that could be a possible challenge for you to take up in the next book. But for now, the book is a brief history of intelligence. Max Bennett, author, thank you for being part of the Cognitive Revolution.

Max Bennett: (1:54:08) My pleasure. Thanks for having me.

Nathan Labenz: (1:54:11) It is both energizing and enlightening to hear why people listen and learn what they value about the show. So please don't hesitate to reach out via email at tcr@turpentine.co, or you can DM me on the social media platform of your choice.