Speaker 1: Humans are really smart? But how did intelligence evolve? If
Speaker 1: we're trying to look back at the history of intelligent brains,
Speaker 1: do we have to look all the way back to
Speaker 1: our common ancestors with the apes, or all mammals or
Speaker 1: all reptiles, or can the origins of intelligence be traced
Speaker 1: back even further? And now that our species is good
Speaker 1: and smart, what does the knowledge of our past mean
Speaker 1: for us as we work to build intelligence artificially? Welcome
Speaker 1: to Inner Cosmos with me David Eagleman. I'm a neuroscientist
Speaker 1: and an author at Stanford and in these episodes, we
Speaker 1: sail deeply into our three pound universe to understand why
Speaker 1: and how our lives look the way they do. Today's
Speaker 1: episode is about intelligence and the history of intelligence. How
Speaker 1: did human intelligence arrive on the scene?
Speaker 2: Now?
Speaker 1: This is an important question because we seem to be
Speaker 1: operating at a different level than our neighbors in the
Speaker 1: animal kingdom. We are the only ones, as far as
Speaker 1: we can tell, who compose symphonies and launch mars rover
Speaker 1: missions and discover DNA and build courthouses and have congresses
Speaker 1: and construct windmills and write novels and build screws and
Speaker 1: screwdrivers to hold things together, and so on and so on,
Speaker 1: none of which any other animal does. And this is
Speaker 1: how we've taken over the whole planet. But how the
Speaker 1: heck did this happen? Why are humans such a runaway species? Well,
Speaker 1: traditionally the explanation has been something like this is a
Speaker 1: special gift from your deity, whichever deity your family believed
Speaker 1: in at whatever moment in history. But centuries of people
Speaker 1: looking at this carefully, sometimes in a microscope, sometimes in
Speaker 1: the brain scanner, sometimes at autopsy, careful examination has made
Speaker 1: something very clear. When you look at the brains of
Speaker 1: other animals, those brains are very similar to our own. Now,
Speaker 1: this shouldn't be surprising. It's the same when you look
Speaker 1: at other animals hearts or lungs or kidneys. It's the
Speaker 1: same good idea, and it's conserved throughout evolution, and so
Speaker 1: it goes with brains, with neurons and cerebellum and thalamus
Speaker 1: and hippocampus and cortex and blah blah blah. It looks
Speaker 1: pretty similar everywhere. And this leads to a point which
Speaker 1: should be fairly obvious when you look across the evolution
Speaker 1: of the vast Kingdom of animals. You don't find that
Speaker 1: there was no intelligence and suddenly humans popped up like
Speaker 1: hairless geniuses. That's not what happened. Instead, what you find
Speaker 1: is there are versions of intelligence all around us. As
Speaker 1: one example, I always admire the squirrels hopping in my
Speaker 1: tree in the garden. They perform these sophisticated acrobatics and
Speaker 1: do the kind of stuff that human gymnasts would never
Speaker 1: even attempt. And crows show intelligence that's closer to our own.
Speaker 1: They can solve really sophisticated puzzles, and dolphins have some
Speaker 1: sort of societies and language, though again not quite as
Speaker 1: sophisticated as ours. And in episode thirty four, I explored
Speaker 1: what it would be like to have different levels of intelligence,
Speaker 1: So please check out that episode if you're interested in that.
Speaker 1: So back to this question. When we ask how intelligence
Speaker 1: got here, it ends up being a question about an
Speaker 1: evolutionary journey, like when we ask how did Homo sapiens
Speaker 1: start walking on our rear legs? Or how did we
Speaker 1: become hairless? Or why do we get pimples and other
Speaker 1: primates don't, or even deeper things like how did any
Speaker 1: of us we and other land dwelling animals, how did
Speaker 1: we get kidneys or lungs? We can ask the same
Speaker 1: sort of questions about the brain. The brain has a
Speaker 1: very rich evolutionary history, a long and sometimes branching pathway
Speaker 1: that has led from early brains swimming around looking for
Speaker 1: food to brains now that build skyscrapers and launch rocket
Speaker 1: ships and try to figure themselves out. This is the
Speaker 1: kind of stuff that none of our neighbors in the
Speaker 1: animal kingdom do, as far as we can tell. And
Speaker 1: there's clearly something special about the human brain that allows
Speaker 1: that to happen. In other words, we find smarts all
Speaker 1: across the animal kingdom, but there is something very special
Speaker 1: about human intelligence. There's an evolutionary biologist named Theodosius Dubzanski,
Speaker 1: and he once said all species are unique, but humans
Speaker 1: are the uniquest. So I've just told you two things.
Speaker 1: On the one hand, we have very similar brains to
Speaker 1: all our animal cousins, and on the other hand, we
Speaker 1: have a runaway intelligence. So what has happened here? One
Speaker 1: person who has devoted himself to this question is Max Bennett,
Speaker 1: who wrote a wonderful book called A Brief History of Intelligence,
Speaker 1: And in this book, Max distills an enormous amount of
Speaker 1: data about the history of animal species to reveal a
Speaker 1: clear path that stretches from very ancient ancestors to us.
Speaker 1: He attributes the story of human intelligence not just to
Speaker 1: a single breakthrough, but to five breakthroughs. I really loved
Speaker 1: his books, so I called him to join us today. So, Max,
Speaker 1: when we're talking about the origins of intelligence, you might
Speaker 1: think that what we need to do is look all
Speaker 1: the way back to our common ancestors with the apes,
Speaker 1: or maybe farther back to mammals, or maybe even as
Speaker 1: far back as reptiles. But you suggest in your book
Speaker 1: that we have to look back much farther than that.
Speaker 1: Even so tell us where you think the sparks of
Speaker 1: intelligence began.
Speaker 2: So what's so interesting in trying to understand how the
Speaker 2: human brain works is not only how much we've learned,
Speaker 2: but also how much we've still failed to learn because
Speaker 2: of how complicated the human brain is. I mean, the
Speaker 2: human brain has eighty six billion neurons and one hundred
Speaker 2: trillion connections, and so one strategy for trying to understand
Speaker 2: the brain is to look at the series of steps
Speaker 2: by which it came to be. Even if we only
Speaker 2: go as far back as the first vertebrates, with whom
Speaker 2: our common ancestors are around five hundred million years ago.
Speaker 2: Our ancestors had brains somewhat akin to a modern fish,
Speaker 2: and even in a fish brain there are a lot
Speaker 2: of complicated structures and a lot of neurons. So I
Speaker 2: think it behooves us to go back all the way
Speaker 2: to the very first brains, which have brains akin to
Speaker 2: a modern nematode and a modern Some species of modern nematodes,
Speaker 2: like C. Elegans, only have three hundred two neurons, and
Speaker 2: we can learn a lot about what the very first
Speaker 2: brain did by understanding what a nematode brain does.
Speaker 1: So tell us what a nematode is for listeners who don't.
Speaker 2: Know, there's many different species of nematodes, but the most
Speaker 2: well studied is something called Cea elegans, and it is
Speaker 2: a small wormlike creature. You could fit a few on
Speaker 2: your fingertip. And they have no eyes, they have no ears,
Speaker 2: they can't render an image of the external world. They
Speaker 2: only have three hundred two neurons in its entire nervous system,
Speaker 2: and yet it can do some really impressive stuff and
Speaker 2: that teach us a lot about the foundations of the
Speaker 2: very first brains.
Speaker 1: Okay, so give us a sense of what C. Elegans
Speaker 1: can do.
Speaker 2: One thing that's really interesting about C. Elegans is how
Speaker 2: well it navigates the world and the absence of a
Speaker 2: complex sensory apparatus. So one might think that in order
Speaker 2: to find food or avoid predators, one needs to build
Speaker 2: a map of space, or have eyes that enable them
Speaker 2: to see into the distance, or have complex ears that
Speaker 2: allow them to detect things through sound. But the elegance
Speaker 2: has none of this. And yet if you put sea
Speaker 2: elegans in a peatrie dish, it finds food rapidly. And
Speaker 2: if you put them in the wild, they eminently find
Speaker 2: optimal temperatures, and they eminently find ways to avoid predators.
Speaker 2: And so the ways that their brain does this seems
Speaker 2: to be quite similar to the way that a rumba works.
Speaker 2: So a rumba, if folks aren't familiar, is the sort
Speaker 2: of classic vacuum cleaning robot, and it also has no
Speaker 2: eyes or ears, and yet somehow it cleans up everything
Speaker 2: in your house. And so what a rumba does is
Speaker 2: when it hits the wall, it sort of backs away
Speaker 2: and turns randomly, and it keeps doing this randomly enough
Speaker 2: until it reaches all the corners of your house. And
Speaker 2: what nematoad does in some ways actually more advanced, where
Speaker 2: it has sensory neurons around its head, and all these
Speaker 2: sensory neurons do is they get excited when a good
Speaker 2: thing like a smell, is increasing in concentration like a
Speaker 2: food smell, and those drive forward movements, or another set
Speaker 2: of neurons gets excited when something bad increases or something
Speaker 2: good decreases, in other words, a decreasing concentration of a
Speaker 2: food smell. And just by detecting these changes, a brain
Speaker 2: can decide I'm going to keep going forward if good
Speaker 2: things are increasing, or I'm going to turn randomly if
Speaker 2: good things are decreasing, And this is classically called taxis navigation.
Speaker 2: In simpler terms, you call this just steering. And in
Speaker 2: the absence of any site, nematoads can find the origin
Speaker 2: of food smells because food creates this gradience in water,
Speaker 2: where the concentration of the smell is higher towards the source.
Speaker 2: So the very first brain, its core function, was just
Speaker 2: to categorize things in the world and too good and bad,
Speaker 2: such that it would turn towards good things in a
Speaker 2: way from bad things.
Speaker 1: Now bacteria do that too, yes they do.
Speaker 2: Clinokinesis absolutely what's almost mesmerizing about evolution is how this
Speaker 2: exact same algorithm seems to have been recapitulated in a
Speaker 2: completely different substrate. So single celled organisms do this exact
Speaker 2: same type of taxis navigation, but it's implemented in sort
Speaker 2: of the protein machinery of a single cell. And animatode
Speaker 2: does the exact same algorithm, but not implemented within a
Speaker 2: single cell, but through a web of neurons.
Speaker 1: And so what you've proposed in your book, which is
Speaker 1: an amazing book, is five breakthroughs that happened in evolutionary
Speaker 1: time scales that led to intelligence the way that we
Speaker 1: have and care about intelligence. So tell us about breakthrough
Speaker 1: number one.
Speaker 2: So breakthrough number one was this idea of steering. So
Speaker 2: the animals before the first animals with brains, which are
Speaker 2: classically called bileatrians because they have bilineateral symmetry, meaning they're
Speaker 2: symmetric across the central plane. It is interesting to real
Speaker 2: people don't realize this until they think about it, but
Speaker 2: all animals that we think of as animals are symmetric
Speaker 2: across the central line through their body.
Speaker 1: So you mean they have a left side on the
Speaker 1: right side, and they are a mirror image.
Speaker 2: Yeah, and so, but not all animals have that. So
Speaker 2: the very very first animals, we think, we don't have
Speaker 2: perfect evidence for this, but we think we're probably more
Speaker 2: akin to a coral polyp or a jellyfish, which has
Speaker 2: radial symmetry, so they're symmetric across a central axis. And
Speaker 2: so the transition from radial symmetry to bilateral symmetry seems
Speaker 2: to be in part driven by the need to navigate. So,
Speaker 2: although jellyfish are an interesting exception because some of them
Speaker 2: independently seem to have evolved relatively complex navigational systems, most
Speaker 2: evolutionary neuroscientists think the very first animals were more sensile,
Speaker 2: like a coral polyp, where they sit in place. They
Speaker 2: have tentacles and they just try to detect food that
Speaker 2: pass by the tentacles. But the very first animal with
Speaker 2: brains are bilateral ancestors. They use this brain to categorize
Speaker 2: the world and to good and bad. To implement this
Speaker 2: taxis navigation to find food and avoid predators.
Speaker 1: So the existence of a brain correlates with having this
Speaker 1: left right side. Is that correct?
Speaker 2: There are all animals with brains descend have bilateral symmetry,
Speaker 2: or descend from the bilaterally symmetric ancestor in which the
Speaker 2: first brains evolved. And so we also see a suite
Speaker 2: of other interesting things emerged with this first breakthrough of steering.
Speaker 2: One is classically called affect, which is sort of the
Speaker 2: first template of emotional states. And so a nematode actually
Speaker 2: has dopamine neurons, and what these dopamine neurons do is
Speaker 2: they detect the presence of bacteria outside of the nematode.
Speaker 2: And what it does is it changed their behavioral repertoire
Speaker 2: to search in their local area. And we see why
Speaker 2: this exists in the rumba. So a rumba has something
Speaker 2: called dirt detect and what dirt detect does is if
Speaker 2: it bumps into dirt, it starts turning randomly in that area.
Speaker 2: And the reason it does that is because the world
Speaker 2: is clumpy, So if you detect dirt, it's likely that
Speaker 2: there's other dirt nearby, even though you're not. Maybe detecting
Speaker 2: dirt in the moment. So what anematod does is the
Speaker 2: exact same thing. If it runs into food, even though
Speaker 2: it might not detect food a second later, it's probably
Speaker 2: the case there's other food nearby, and so this rush
Speaker 2: of dopamine drives this local search in these very early brains. Similarly,
Speaker 2: there are serotonin neurons, but they're in the throat, and
Speaker 2: so what serotonin signals is the consumption of food, and
Speaker 2: serotonin in these very early nematodes drives sort of satiation.
Speaker 2: And of course those chemicals do much more complicated things
Speaker 2: than human brains. That basic template of dopamine being the
Speaker 2: seeking exploitation nearby reward signal and serotonin being the sort
Speaker 2: of satiation consumption satisfaction signal. We do see hints of
Speaker 2: that basic even in human brains. So we see categorizing
Speaker 2: the world into good to bad, we see bilateral symmetry,
Speaker 2: we see these very basic behavioral states. And then the
Speaker 2: last thing we also see emerge in this breakthrough of
Speaker 2: steering is the foundation of associative learning, and this is
Speaker 2: the first form of real learning that we see emerge
Speaker 2: in animal evolution. And anematode can associate a stimulus with
Speaker 2: a good or bad thing. So if you put a
Speaker 2: nematode in a peach redish and put salt on one side,
Speaker 2: nematoads typically steer towards salts because salt tends to correlate
Speaker 2: with food. But if you leave them in a peach redition,
Speaker 2: starve them for a long period of time in the
Speaker 2: presence of salt water, they change their opinion and they
Speaker 2: will start steering away from salt in the future. And
Speaker 2: it makes sense why associative learning would emerge if the
Speaker 2: very first brain of steering, because you want to tweak
Speaker 2: the goodness and badness of things, because deciding what to
Speaker 2: turn towards and away from is a life or death
Speaker 2: decision for anema TOD. So this first breakthrough of steering,
Speaker 2: we see the suite of of new abilities from associative learning.
Speaker 2: Bilateral symmetry categorizing things in a good and bad emerged
Speaker 2: with the very first brain. So that was breakthrough number one.
Speaker 1: Okay, terrific. And what was break through number two?
Speaker 2: So if we fast forward about fifty million years or so,
Speaker 2: we enter what's famously known as the Cambrian Period and
Speaker 2: the Cambrian Explosion, is this huge diversification of life, which
Speaker 2: actually is all of the children of this first bilateral animal.
Speaker 2: So if you were to swim around the Cambrian Ocean,
Speaker 2: you would see many ancestors of this bilateral wormlike creature
Speaker 2: who had proliferated into what would look like crustaceans and
Speaker 2: arthropods of today. There were huge insect like creatures in
Speaker 2: the ocean, and then there were also our ancestors, which
Speaker 2: were much smaller, modest creatures, but they were most akin
Speaker 2: to a fish of today, and they were called the
Speaker 2: first vertebrates. And the reason they're called vertebrates is because
Speaker 2: in fossils, the most salient feature is the vertebral column,
Speaker 2: so they had a spine. And in these first vertebrates,
Speaker 2: we can get insight into what their brains did by
Speaker 2: looking into the brains of fish today, because there's many
Speaker 2: species of fish that evolutionary neuroscientists think have brains that
Speaker 2: were quite similar to the very first vertebrates. And what
Speaker 2: I found most surprising when I first started looking into
Speaker 2: this is how similar fish brains are to human brains.
Speaker 2: So I would have expected a fish brain to have
Speaker 2: almost none of the features of a human brain, but
Speaker 2: counter to that, intuition. Fish brain have all, with the
Speaker 2: exception of a few things, have all of the major
Speaker 2: brain structures that a human brain does. And also, counter
Speaker 2: to what my expectations would have been, there's sort of
Speaker 2: a stereotype that fish are really dumb, but the more
Speaker 2: you look into the comparative psychology work done on fish,
Speaker 2: fish are way smarter than we think. And for example,
Speaker 2: fish can learn how to navigate out of a maze
Speaker 2: and remember exactly how to do it a year later.
Speaker 2: You can go to YouTube and find really funny cute
Speaker 2: videos of people training fish to jump through hoops for treats,
Speaker 2: and you can train a to push levers for food
Speaker 2: and all of these sort of fun things. And so
Speaker 2: when we look at these brain structures that emerged, there's
Speaker 2: a lot of really good evidence that the key thing
Speaker 2: that happened was these early vertebrate brains enabled the ability
Speaker 2: to learn through reinforcement and AI. This is called reinforcement
Speaker 2: learning and behavioral psychology is typically called trial and error learning.
Speaker 2: So they could learn to perform arbitrary sequences of actions
Speaker 2: on the basis of whether or not it led to
Speaker 2: a reward at the end. So when we go into
Speaker 2: the fish brain. There are two key structures that are
Speaker 2: useful to know about because they will keep coming up
Speaker 2: through our story and the evolution of the human brain.
Speaker 2: One is something called the basal ganglia, and the basil
Speaker 2: ganglia of a fish has almost exactly the same structure
Speaker 2: and network as the basil ganglia of a human, and
Speaker 2: computational neuroscientists have gone to painstaking efforts to show that
Speaker 2: the basil gangly is implementing a reinforcement learning algorithm almost
Speaker 2: identical to the reinforcement learning algorithm we use in AI
Speaker 2: system today. And the way that it works in principle
Speaker 2: is it trains itself based on the exciting the excitement
Speaker 2: of dopamine, and it learns to repeat behaviors that drive
Speaker 2: dopamine release and inhibit behaviors that drive dopamine decreasing. And
Speaker 2: what's so fascinating is if you look at how this
Speaker 2: system came to be, you can see how reinforcement learning
Speaker 2: is only possible if brains first had the foundation of steering.
Speaker 2: Because the foundation of steering gives us the categorization of
Speaker 2: things in the world and to good and bad, and
Speaker 2: that is repurposed to create this reward signal that the
Speaker 2: basal ganglia then can use to create arbitrary sequences of
Speaker 2: behavior on the basis of what leads to reward or none.
Speaker 2: And this is how a fish can learn really complex
Speaker 2: sequences of actions on the basis of what leads to reward.
Speaker 2: In the end, the second key structure in a fish
Speaker 2: brain is something called the cortex, and we do have
Speaker 2: a version of a cortex. There's a portion of our
Speaker 2: cortex that we'll talk about that's way more advanced. But
Speaker 2: a phish cortex can still do something incredible that the
Speaker 2: first nematodes could not, which is it recognizes things in
Speaker 2: the world on the basis of patterns. So in the
Speaker 2: first nema, in the first bilateral brain, it could not
Speaker 2: detect things in the world on the basis of a
Speaker 2: pattern of activation. So when you look at a horse,
Speaker 2: you recognize a horse not because of any single neuron
Speaker 2: in your brain, but because your brain is somehow decoding
Speaker 2: the pattern of activation on your retina, the neurons in
Speaker 2: your retina. And so the first brains could not do
Speaker 2: anything like this. They only detected things when a single
Speaker 2: neuron got excited in the presence of some stimulus. But fish,
Speaker 2: fish can even recognize human faces. There have been some
Speaker 2: amazing studies that show a fish can recognize a human
Speaker 2: face and learn which face leads to a reward in
Speaker 2: which face does not. Even when that face is rotated
Speaker 2: in space, they still recognize it. So the cortex somehow,
Speaker 2: this is still an outstanding mystery in the field of neuroscience.
Speaker 2: Somehow the cortex recognizes patterns and fish eminently well. And
Speaker 2: in some ways the cortex of a fish recognizes patterns
Speaker 2: better than even our our best vision systems in AI,
Speaker 2: because we can. They've done studies that show that a
Speaker 2: fish can recognize objects in one shots even though it's
Speaker 2: been rotated in space, and AI systems typically don't do that.
Speaker 2: You need a lot of data to get into that.
Speaker 2: So at the first fish brain, we see reinforcement learning emerge,
Speaker 2: which can recognize patterns in the world and can learn
Speaker 2: to take actions in the presence of those patterns based
Speaker 2: on rewards. We see reinforcement learning as breakthrough number two.
Speaker 1: Excellent, Okay, how about number three?
Speaker 2: Then we're going to fast forward through a long period
Speaker 2: of evolutionary time, all the way until about one hundred
Speaker 2: and fifty million years ago. Between hundred and hundred million
Speaker 2: years ago. This is the era of dinosaurs. Our ancestors
Speaker 2: were very, very humble, tiny squirrel like creatures that lived underground,
Speaker 2: and we only came out at nights to hunt for insects.
Speaker 2: But these were the first mammals. We know a lot
Speaker 2: about mammal brains, way more than we actually know about
Speaker 2: fish brains, because the main stay of neuroscience research typically
Speaker 2: happens in rats and mice when we go into these brains. Interestingly,
Speaker 2: the fundamental difference between a mammal brain and a fish
Speaker 2: brain is the presence of one key new structure, which
Speaker 2: is a part of the cortex elaborates into what's famously
Speaker 2: called the neocortex NEO for new, and under a microscope
Speaker 2: there's some really interesting things. So we have remnants of
Speaker 2: the old cortex of fish they are called the olfactory cortex,
Speaker 2: and humans and mammals they're called the hippocampus, and they're
Speaker 2: called the cortical amygdala. These are all ancestral remnants of
Speaker 2: the very first cortex. But the neocortex is entirely new.
Speaker 2: This is something that only occurred within mammals, and it
Speaker 2: looks way more complicated under a microscope and so there's
Speaker 2: this grand question what did this neocortex do? And classically,
Speaker 2: when we study the neocortex, we look at a lot
Speaker 2: of humans, and when you look at a human brain,
Speaker 2: the whole thing seems to be neocortex. So when you
Speaker 2: look at human brain, all of this all is full,
Speaker 2: that's all neocortex bunched together. It's this sort of has
Speaker 2: this sort of surface area, and the neocortex seems to
Speaker 2: do everything, which is this funny perplexing thing in neuroscience.
Speaker 2: Because there's one region that seems to do vision. If
Speaker 2: it gets damaged, people can't see. There's another area that
Speaker 2: seems to do audition. If it gets damaged, people struggle
Speaker 2: to hear things. There's a region that seems to do attention.
Speaker 2: If it get damaged, you can't perceive things on one
Speaker 2: side or visual of view. There's an area for movements.
Speaker 2: If it gets damage, you get paralyzed, so on and
Speaker 2: so forth. So it's this grand sort of mystery of
Speaker 2: what the neocortex does, but most of it seems to
Speaker 2: have been based on this idea of perception. A lot
Speaker 2: of the neocortex seems to enable us to perceive things
Speaker 2: in the world, But what's odd is if we think
Speaker 2: about this from an evolutionary perspective, there's no clear perceptual
Speaker 2: improvements or very salient at least perceptual improvements, and a
Speaker 2: mammal relative to a fish, so a fish can recognize
Speaker 2: faces as well as a rat can. It recognizes them
Speaker 2: when rotated in space, So it's not so clear from
Speaker 2: an evolutionary perspective that the neocortech evolve for better perception.
Speaker 2: If we really examine the fundamental differences in the abilities
Speaker 2: of simple mammals with fish. There are, however, four things
Speaker 2: that are seen, and I think these are great clues
Speaker 2: as to what the first the neo cortex did. One
Speaker 2: thing that mammals can do very well is they can
Speaker 2: imagine the future. So there's some really wonderful studies done
Speaker 2: by David Reddish that show you can put a mouse
Speaker 2: in amaze and you can watch a mouse imagining its
Speaker 2: possible futures. Another thing you can do is mammals, even rats,
Speaker 2: are eminently good at having regret. So if you put
Speaker 2: them in a situation where they have to make irreversible choices,
Speaker 2: they will often regret their decision and you can watch
Speaker 2: them in their brain imagining themselves taking prior past choices.
Speaker 2: Mammals also have something akin to episodic memory. You can
Speaker 2: put rats in experiments where they have to imagine some
Speaker 2: recent past event in order to solve a puzzle in
Speaker 2: front of them, and you can watch them do that.
Speaker 2: And then the fourth is they have really great fine
Speaker 2: motor skills. So in the reptile literature, there's some good
Speaker 2: evidence that most lizards, with the exception of birds, which
Speaker 2: is a non mammalian vertebrate that has amazing find motor skills.
Speaker 2: But reptiles don't even sort of anticipate our movements to
Speaker 2: get over obstacles. They're very sloppy in their movements. And
Speaker 2: yet a squirrel, watch a squirrel run across sort of
Speaker 2: tree branches, has find motor skills that blow away any
Speaker 2: modern robotic system. So these four things actually can be
Speaker 2: seen as different applications of what I would call simulating
Speaker 2: an AI. This is called planning. Typically, so mammal brains
Speaker 2: are good at simulating possible states of the worlds and
Speaker 2: then making choices on the basis of that simulation. They
Speaker 2: can simulate the future, that's imagination. They can simulate past
Speaker 2: events that's episodic memory, they can simulate and plan their
Speaker 2: hand motions, which is effectively enabling them to find motor skills.
Speaker 2: And so this mental simulation we even see in humans.
Speaker 2: I mean, we are eminently capable of doing this. Close
Speaker 2: your eyes. You can imagine things in your mind's eye.
Speaker 2: This lights up your neocortex the same way as if
Speaker 2: you perceived those same objects. And so simulation was this
Speaker 2: incredible skill given to these early mammals because it enabled
Speaker 2: them to plan their movements ahead of time and sort
Speaker 2: of outsmart the dinosaurs. In AI today, this is classically
Speaker 2: called model based reinforcement learning. And so in AAI there's
Speaker 2: this big division between model free, which means learning to
Speaker 2: take actions without any planning at all. You just see
Speaker 2: sort of the current state and then you make a choice.
Speaker 2: Our self driving cars, the AI algorithm that keeps you
Speaker 2: in the lane is a model free system just sees
Speaker 2: a picture of the road and decides how to put
Speaker 2: the seering wheel. Model based systems are ones that imagine
Speaker 2: possible futures before making a choice. So Alpha Go, that
Speaker 2: one classically be the best go player in the world,
Speaker 2: was a model based reinforcement learning system. It actually within
Speaker 2: a matter of seconds simulated thousands of possible games before
Speaker 2: making a choice, and so there's this really nice synergy
Speaker 2: with AI. Where in early vertebrates, with breakthrough two, we
Speaker 2: see model free reinforcement learning. There's no evidence of fish
Speaker 2: being able to imagine the future, but with early mammals
Speaker 2: we see model based reinforcement learning, which is them being
Speaker 2: able to imagine futures before acting. And what is also
Speaker 2: really interesting is how you can't have simulation without first
Speaker 2: having trial and error learning, because the way that simulation
Speaker 2: cascades into action is you're training yourself in your mind's eye.
Speaker 2: When a rat closes its eyes and imagines itself taking
Speaker 2: multiple paths, a little dopamine gets released when it imagines
Speaker 2: taking the path that actually leads to food. And so
Speaker 2: then the way that the simulation leads to action is
Speaker 2: because you already have this trial and error system in
Speaker 2: place that you're training vicariously with your mind. This is
Speaker 2: also why they've shown this with athletes too. This is
Speaker 2: why mental rehearsal dramatically improves performance. Surgeons also, they've done
Speaker 2: studies that show mental rehearsal improves performance. Okay, so that's
Speaker 2: break through number three.
Speaker 1: Yeah, this is something I talked about on this podcast
Speaker 1: A lot is the way that we unhook from the
Speaker 1: here and now and we go to the therein and then,
Speaker 1: whether that's in the future or the past. As the
Speaker 1: philosopher Carl Popper said, this is what allows our hypotheses
Speaker 1: to die in our stead. And we're going to come
Speaker 1: back to internal models a little bit tell us about
Speaker 1: the next breakthrough.
Speaker 2: Okay, So moving forward from early mammals, a huge asteroid
Speaker 2: hits Earth, which tragically kills off all the dinosaurs and
Speaker 2: opens up the world for what is sometimes called the
Speaker 2: Age of mammals because our ancestors took over from that
Speaker 2: point forward. It is an interesting quirk that if that
Speaker 2: asteroid never heard Earth, there would almost certainly be no humans,
Speaker 2: and it would likely be that we would still be
Speaker 2: tiny little squirrels hiding in the dirt. So that is
Speaker 2: just an interesting accident of the universe. But as mammals
Speaker 2: started proliferating throughout Earth, our ancestors were the ones that
Speaker 2: stayed in the trees and they became the first primates.
Speaker 2: And primates are known for having really really big brains,
Speaker 2: you know. The modern primates include monkeys, non human apes,
Speaker 2: and of course humans, of whom are apes? And these
Speaker 2: primates have really big brains for a perplexing reason. So
Speaker 2: it's been open question in primatology for a lot, or
Speaker 2: was an open question for a long time, why do
Speaker 2: primates have such big brains. They don't seem to have
Speaker 2: such a complex lifestyle that requires them this massive neocortex
Speaker 2: that evolved. But several decades ago some theories emerged that
Speaker 2: have been proven out, which it seems to be something
Speaker 2: about the social lives of primates that drive their really
Speaker 2: big brains. And so Robin Dunbar is one of the
Speaker 2: early people that came up with this idea, And what
Speaker 2: he did is he looked at the size of the
Speaker 2: social group of primates and compared it to the relative
Speaker 2: size of their new cortex relatives to the rest of
Speaker 2: the brain. And you see this almost beautiful curve where
Speaker 2: the bigger the social group, the bigger the relative size
Speaker 2: their neocortex. This relationship does not hold for other mammals.
Speaker 2: So this is not some universal principle, but something about
Speaker 2: primate societies are such that they require really big neo courtices.
Speaker 2: And so the more we examine the primate society, we
Speaker 2: see some interesting features primate societies are very political, so
Speaker 2: unlike a troop of gazelles and a troop of gazelle's,
Speaker 2: whoever is the top ranking gazelle is typically the one
Speaker 2: that's the strongest. So there's very explicit hierarchies in many
Speaker 2: mammal groupings, but they're based on who's the toughest and
Speaker 2: the strongest. But if you look at primate societies, it's
Speaker 2: typically not the strongest. It's the most socially savvy one.
Speaker 2: It's the one that cozies up to the most allies,
Speaker 2: it's the one that builds the most friendships, that build
Speaker 2: sort of this political regime that enables them to be
Speaker 2: the top ranking chimpanzee, their top ranking bnobo. So there's
Speaker 2: been some also amazing studies of the ways in which
Speaker 2: these apes and monkeys reason about other people's mind states
Speaker 2: when making choices on how to befriend them or how
Speaker 2: to deceive them. So you can see non human apes
Speaker 2: do things like they will hide transgressions from other people
Speaker 2: to try and prevent themselves from getting in trouble. There's
Speaker 2: this famous study that I love by Emil Menzel. I
Speaker 2: think it was in the seventies where he put two
Speaker 2: chimpanzees in the sort of one acre forest, and he
Speaker 2: showed the location of treats to one of the chimpanzees
Speaker 2: named Belle, and she initially would share the treat with
Speaker 2: another chimpanzee named Rock, but then Rock started stealing the
Speaker 2: treat from her. So what she started doing is, when
Speaker 2: she knew the location of the treat, she would wait
Speaker 2: for a rock to look away, and then she would
Speaker 2: run over and grab it. So then Rock, in response
Speaker 2: to this, decided to pretend to look away so that
Speaker 2: when she started running, then he would turn around and run.
Speaker 2: Then respect to this, what she would do is she
Speaker 2: would pretend to run in the wrong direction, lead him
Speaker 2: to the wrong place, and then run back. And so
Speaker 2: this cycle of deception and counter deeception is very very unique,
Speaker 2: with impossible exceptions of a few very very smart non
Speaker 2: primate mammals like dolphins, seems to be unique to primates,
Speaker 2: and so this gives us a clue as to what
Speaker 2: might be new in the brains of primates. When we
Speaker 2: go into the primate brain, we see these suite of
Speaker 2: new neocortical regions sort of The most sizable one is
Speaker 2: something in the front of the brain called the granular
Speaker 2: prefrontal cortex, and when we do sort of neuroscience to
Speaker 2: try and understand what does the structure do, it lights
Speaker 2: up a ton when we reason about our own mind,
Speaker 2: so how we would feel in certain states, or we
Speaker 2: reason about other people's minds. So in tests of what's
Speaker 2: called theory of mind, when I need to guess what
Speaker 2: someone else is thinking about, or what their intention is,
Speaker 2: or what knowledge they might have, this part of the
Speaker 2: brain lights up a ton. And they've done some cool
Speaker 2: study on macaque monkeys that show that in order for
Speaker 2: a monkey to make a correct assessment of what someone
Speaker 2: else knows or doesn't know, they need this part of
Speaker 2: their brain active. If you temporarily inhibit it, they lose
Speaker 2: their ability to reason about other people's minds. So you
Speaker 2: get theory of mind. And so the idea is break
Speaker 2: through four is mentalizing, which is also called metacognition, thinking
Speaker 2: about thinking, reasoning about your own mind and other people's minds.
Speaker 2: But there's two unique things about primates that are not
Speaker 2: classically thought about as being related to mentalizing that I
Speaker 2: would argue are are only possible in prime it's because
Speaker 2: of mentalizing. One is imitation learning we know that primates
Speaker 2: are exceptionally good imitation learners. So if you take a
Speaker 2: chimpanzee out of their group and teach them how to
Speaker 2: open a puzzle box or do some clever motor skill,
Speaker 2: and then you release them back into their troop, within
Speaker 2: thirty to sixty days, the whole troop will know the
Speaker 2: same exact skill. So chimpanzees are very good at learning
Speaker 2: skills through observation. This is part of why apes are
Speaker 2: such good tool users, because once one member learns how
Speaker 2: to use a tool, they all adopt the skill, and
Speaker 2: then they cascade it through generations. In AI, we have
Speaker 2: tried to teach systems through imitation. We've discovered something really interesting.
Speaker 2: We've learned that direct imitation of other people's actions does
Speaker 2: not work. So we've tried this in self driving cars,
Speaker 2: where we try to teach an AI system to drive
Speaker 2: a car by watching a human drive a car. And
Speaker 2: the reason it fails is because when you watch an expert,
Speaker 2: you never see the expert recover from mistakes. So the
Speaker 2: second this AI system started veering off the road, nothing
Speaker 2: in its training set taught it how to recover from
Speaker 2: veering off the road, because it only watched from an
Speaker 2: expert of who never veered off the road. The way
Speaker 2: we get this to work in AI systems, which was
Speaker 2: most famously invented by Andrew Aang, it's called inverse reinforcement learning.
Speaker 2: And so what you do is you first try to
Speaker 2: infer what the person you're imitating is trying to do.
Speaker 2: You infer their reward function. So if you watch someone drive,
Speaker 2: you say, oh, they're trying to stay in the center
Speaker 2: of the road, and then I train myself in my
Speaker 2: mind's eye to do the same thing that they're trying
Speaker 2: to do, and that works. So Entering in the early
Speaker 2: two thousands trained a helicopter to do all these crazy
Speaker 2: aerobatic tricks through watching other trained experts do those tricks,
Speaker 2: but not by directly copying them, by first inferring what
Speaker 2: they're trying to do, and so it eliminates all the
Speaker 2: extraneous behaviors. This is part of why imitation learning requires mentalizing,
Speaker 2: because in order for me to really understand what you're
Speaker 2: trying to do with certain tool usage behaviors, I need
Speaker 2: to reason about your mind and infer what your intent is.
Speaker 2: And that's part of why I would argue that primates
Speaker 2: are so good at imitation learning, they repurpose. It's mentalizing
Speaker 2: for that. The last one is something called anticipating future needs.
Speaker 2: So when we go grocery shopping for the week, we're
Speaker 2: actually doing something really remarkable. We are taking an action
Speaker 2: today to satiate a need that we do not currently have.
Speaker 2: I might not be hungry, and yet I'm going to
Speaker 2: take an hour out of my day to fill up
Speaker 2: my refrigerator. And it's not so clear how many animals
Speaker 2: are capable of doing that. So, for example, in mice,
Speaker 2: you see hoarding behavior before the winter, but we now
Speaker 2: know that that is genetically hard coded. They're not mentally
Speaker 2: imagining the winter and realizing they'll be hungry. A rat
Speaker 2: that is, or a mouse who has never experienced hunger
Speaker 2: in the winter, never even experienced a winter at all.
Speaker 2: If you turn down the temperature, we'll start hoarding. But
Speaker 2: primates seem to be capable of doing this, So they've
Speaker 2: done some fun studies on squirrel monkeys that show that
Speaker 2: they will actually choose having less treats today to reduce
Speaker 2: their future thirsts even when they're not thirsty today, whereas
Speaker 2: a rat is incapable of doing that, And so this guy.
Speaker 2: Tom and Sudendorff came up with this theory that maybe
Speaker 2: anticipating our own future needs uses the same machinery in
Speaker 2: our brains as reasoning about other minds, because if you
Speaker 2: think about it, it's really the same thing. For me to ask,
Speaker 2: what will David feel like if he didn't drink for
Speaker 2: a week is really the same question as what I
Speaker 2: feel like if I didn't drink for a week, And
Speaker 2: so this might also explain why apes and other primates
Speaker 2: are so good at anticipating their own future needs and
Speaker 2: making these really long term plans. So breakthrough FORO is mentalizing.
Speaker 2: It is the building a sort of model of your
Speaker 2: own inner mind, and it enables you to reason about
Speaker 2: other minds. It enables you to learn through imitation, and
Speaker 2: it allows you to anticipate your own future needs.
Speaker 1: Great tell us about the final breakthrough that led to
Speaker 1: the kind of intelligence that we enjoy.
Speaker 2: So there's been throughout the ages so many thinkers, philosophers,
Speaker 2: and scientists have tried to draw a hard line between
Speaker 2: humans and other animals and articulate what is the thing
Speaker 2: that makes humans unique? And after writing this book, one
Speaker 2: of the most like clear things to me is how
Speaker 2: little difference there really is between us and other animals.
Speaker 2: So people used to think only humans could imagine things.
Speaker 2: I think the evidence is very strong that other mammals
Speaker 2: and probably birds regularly have imagination. Some people thought only
Speaker 2: humans think about thinking. I think there's pretty good evidence
Speaker 2: that other primates do the same, and so there's been
Speaker 2: this long laundry list of stuff. I think the main
Speaker 2: feature of human intelligence that there is this good evidence
Speaker 2: is uniquely human, or at least uniquely evolved in the
Speaker 2: human lineage and was not present in other primates is language.
Speaker 2: And so it's language is not the same thing as communication.
Speaker 2: Even single celled organisms engage in communication, but language is
Speaker 2: unique on two counts. Human language has what's called declarative labels.
Speaker 2: It allows us to assign an arbitrary symbol to a
Speaker 2: thing or an action in the world. So when you
Speaker 2: tell a dog to sit, now what it's learning is
Speaker 2: when I hear the symbol sit, if I take this
Speaker 2: action sit, I get a reward. That's something linguists call
Speaker 2: imperative labels. A declarative label is if I say sit,
Speaker 2: we're all imagining the action of sitting. And it's not
Speaker 2: clear that other animals are capable of these types of
Speaker 2: declarative labels. There's been painstaking attempts to train non human primates,
Speaker 2: specifically apes, to use language. Typically it's sign language because
Speaker 2: they don't actually have the sort of vocal apparatus for
Speaker 2: verbal language, And it's still controversial the extent to which
Speaker 2: what they were able to do could be called language.
Speaker 2: But even if you would classify it as a primitive
Speaker 2: form of language, it's very clear that non human apes
Speaker 2: are not nearly as good at learning languages as human children.
Speaker 2: The second thing that's unique about human language is grammar.
Speaker 2: So we can switch the ordering of these symbols to
Speaker 2: change their meaning in seemingly arbitrary ways. So Max jumped
Speaker 2: over Charlie means something different than Charlie jumped over Max,
Speaker 2: and by ordering the symbols, the meaning totally shifts. And
Speaker 2: so one might think, okay, language is this unique thing,
Speaker 2: that there'd be some unique structures in the human brain
Speaker 2: that enabled language, and to my surprise, also looking to
Speaker 2: the neuroscience, that's not at all the case. So there
Speaker 2: are two regions of the neocortex and humans that are
Speaker 2: very implicated in language, famously called Wernicke's area and Broker's area.
Speaker 2: But interestingly, those same exact neocortical regions exist in other primates,
Speaker 2: they're just not used in communication. So for some reason,
Speaker 2: it wasn't that some new structure emerged in the human brain.
Speaker 2: It's that we repurpose an existing structure to use in language.
Speaker 2: And what seems to have happened is a new learning
Speaker 2: curriculum evolved in humans that enabled us to learn language.
Speaker 2: And so if we compare chimpanzee children to human children,
Speaker 2: there's two very unique traits of human children. One is
Speaker 2: they engage in something called joint attention at a very
Speaker 2: very young preverbal age, which means children get a unique
Speaker 2: burst of excitement when they can confirm by looking at
Speaker 2: your eyes that we are that they and you are
Speaker 2: attending to the same object. So they've done lots of
Speaker 2: painstaking studies to show that the child is not excited
Speaker 2: because they think they're going to get the object. They're
Speaker 2: not excited because the parent is excited. They are specifically
Speaker 2: happy and satisfied when they confirm that they are looking
Speaker 2: at the same object that the parent is looking at.
Speaker 2: And what does this enable us to do? This enables
Speaker 2: us to render a simulation of the same object in
Speaker 2: our head, so we can assign a symbol to it.
Speaker 2: If we all look at a cat and I confirm
Speaker 2: you're looking at a cat, and then the parent says
Speaker 2: the symbol cat, whether it's verbal or a sign or
Speaker 2: a written word, it creates this sort of basic foundation
Speaker 2: for labels to be constructed. And the other thing that's
Speaker 2: unique in human children is proto conversation. So they've shown
Speaker 2: that very young human infants will match the duration of
Speaker 2: babbling before words with their parents. So if the parent
Speaker 2: babbles for four seconds, the child tends to bet for
Speaker 2: four seconds and then pause and wait for the parent
Speaker 2: to do that. These two things are not naturally occurring
Speaker 2: in non human primates, so it's very hard to get
Speaker 2: a chimpanzee to attend to the same object and for
Speaker 2: them to confirm that we're all attending to the same thing. Okay,
Speaker 2: so we get language, But why does language make humans
Speaker 2: so special? So this has been well discussed in linguistics
Speaker 2: in Uvall's books Sapiens, I think he speaks to a
Speaker 2: lot of this. What makes language so incredible? This enables
Speaker 2: us to share our inner simulations, and so it transforms
Speaker 2: the human brain from just sort of the epicenter of
Speaker 2: intelligence to being the medium through which ideas can flow
Speaker 2: through time. So because I can share what's going on
Speaker 2: in my mind, culture canform or a more advanced form
Speaker 2: of culture because I can learn certain skills and then
Speaker 2: describe the skill to you, or the five of us
Speaker 2: can go on a hunt together, and I can imagine
Speaker 2: a plan and then share the plan in my mind
Speaker 2: with you through symbols, and then we all have the
Speaker 2: same plan in in our minds, and then we can
Speaker 2: coordinate and do the same thing together. Without the ability
Speaker 2: to share inner simulations, you don't get this type of flexibility.
Speaker 2: So that's one of the fundamental things that enables language
Speaker 2: to make humans so powerful, because as generations go on,
Speaker 2: the ideas sort of ratchet up and get more and
Speaker 2: more complex over time, versus in chimpanzee societies. Because they
Speaker 2: can't reliably share ideas, they can only observe through learn
Speaker 2: from each other through observation. There's a limit to how
Speaker 2: complex these ideas can get over generations. And so that's
Speaker 2: one of the leading theories, not my theory. Lots of
Speaker 2: linguists and primatologists talk about this as to why humans
Speaker 2: sort of took over the world, which is ideas got
Speaker 2: to get more complex over time until they reach this
Speaker 2: sort of critical point. And so break through five was
Speaker 2: speaking or language. And the last point I'll make on
Speaker 2: this is how you one can see how even speaking
Speaker 2: in language is dependent on the prior breakthroughs. So as
Speaker 2: we now know in AI systems, when the leading problems
Speaker 2: with an AI system bound by just language is how
Speaker 2: hard it is to actually describe our desires in the
Speaker 2: form of language. So Nick Bostrom has this really great
Speaker 2: allegory where suppose there is an AI that manages a
Speaker 2: paper clip factory, a super intelligent AI, and the instruction
Speaker 2: US humans give that AI is maximize paper clip production.
Speaker 2: That's the we give that a natural language, maximize paper
Speaker 2: clip production. In his allegory, what he imagines if the
Speaker 2: superintelligent AI were actually to just optimize for the explicit
Speaker 2: request it was given, it would start to take over
Speaker 2: Earth and convert everything it could observe into paper clips.
Speaker 2: And when it was done with Earth, it would expand
Speaker 2: to Mars and it would start to try and take
Speaker 2: over the universe to convert all of it into paper clips.
Speaker 2: And as silly as that example is, as almost nonsensical
Speaker 2: as it seems, it reveals why mentalizing is required for
Speaker 2: language to work. Because when you tell a human maximize
Speaker 2: production of paper clips, what a human is doing is
Speaker 2: it's they're inferring what you actually mean by what you say.
Speaker 2: I'm simulating your mind and I'm trying to infer your preferences,
Speaker 2: and I'm doing this really complex inference task to take
Speaker 2: the symbols that you gave me and convert it into
Speaker 2: a really complex reward function that I'm going to try
Speaker 2: and optimize for. But if all system does is take
Speaker 2: our words for what we say them to be and
Speaker 2: doesn't have a model of our minds, then you can
Speaker 2: get these really wacky outcomes where they would try and
Speaker 2: convert Earth into paper clips. And so the reason why
Speaker 2: language requires mentalizing is when we're going back and forth
Speaker 2: trading symbols all the time, we're trying to guess what
Speaker 2: the other person means by what they say. We're trying
Speaker 2: to tell them information to update their knowledge given what
Speaker 2: we know they know and they don't know. It's so
Speaker 2: natural for us we don't realize it. But this is
Speaker 2: one of the key things that human brains are so
Speaker 2: good at that. Aisystems, at least in the same way,
Speaker 2: don't solve.
Speaker 1: You know. One of the things that always has amazed
Speaker 1: me is the existence of literature. The thing I hadn't
Speaker 1: realized until I thought about it was how low bandwidth
Speaker 1: literature is. The author tells you a few sentences about
Speaker 1: this and that, the description and the emotions and all
Speaker 1: the rest depends on the reader. The reader is bringing
Speaker 1: everything to the table. The author can't put what he's
Speaker 1: imagining directly into the mind of the reader because every
Speaker 1: reader is going to imagine something differently predicated totally on
Speaker 1: this issue that you know, it's all about mentalizing and
Speaker 1: language is just a very few bits of information that
Speaker 1: you know, get thrown over the transom to inspire something
Speaker 1: in someone else's mind.
Speaker 2: One hundred percent. I think one thing that just to
Speaker 2: add to that I think is really cool is it
Speaker 2: almost is a neuroscience or AI perspective on why many
Speaker 2: artists talk about how art is an active process. In
Speaker 2: the sort of consumer of art, when we read a book,
Speaker 2: we are participating in that artistic creation because we are
Speaker 2: filling in the gaps. And that's why people can interpret
Speaker 2: art so differently, and in some ways that's why art
Speaker 2: is so beautiful, because it's this like message, but it's
Speaker 2: not fixed. We as consumers get to sort of explore
Speaker 2: it in our own way. I think it's also in
Speaker 2: some ways why reading feels harder than watching a movie
Speaker 2: because you don't realize it, but your mind is doing
Speaker 2: a lot of work when you read, because it's turning
Speaker 2: what you read into a mental movie, and that translation
Speaker 2: takes effort versus watching a movie requires less sort of
Speaker 2: cognitive overlook.
Speaker 1: Now returning to the primates and the humans. So one
Speaker 1: of the things that people have pointed out is that
Speaker 1: humans are the only species that teach. So a prime,
Speaker 1: a young primate will watch his mother, you know, crushing
Speaker 1: rocks and doing something, and the primate will imitate that.
Speaker 1: But the mother never gives feedback. The mother never says, oh,
Speaker 1: you're doing it wrong, do it this way, and grabs
Speaker 1: his hands and does the right way. But humans do
Speaker 1: that all the time. We actually teach, and that's something
Speaker 1: unique to our species. What is the basis of that?
Speaker 2: I would argue in my framework, I would argue the
Speaker 2: basic machinery for teaching exists in mentalizing, but it teaching
Speaker 2: might be such a complex version of mentalizing because it's
Speaker 2: two steps. Not only do I need to render what's
Speaker 2: in your mind, but then I need to be able
Speaker 2: to think about what actions can I take to update
Speaker 2: something in your mind. You know, that's a complex act.
Speaker 2: So I think even if the machinery exists in mentalizing,
Speaker 2: when you scale up the brain, I mean, the human
Speaker 2: brain is about you know, three x bigger than a
Speaker 2: chimpanzee brain, or then the cortex area, you start getting
Speaker 2: some of the machinery that's there in a very lightweight,
Speaker 2: primitive form. So I think in my frame, I would
Speaker 2: argue that some very primitive version of teaching exists in mentalizing,
Speaker 2: but it doesn't really get rendered more effective and so
Speaker 2: it scales up in human brains.
Speaker 1: Okay, So that puts us at today, and what we
Speaker 1: have today is this incredible explosion of AI, which is
Speaker 1: something that you know, my whole career in neuroscience, neuroscientists
Speaker 1: generally looked at AI and said, well, it's you know,
Speaker 1: it's not very good. It's not able to do X,
Speaker 1: y Z. But we've all been surprised in the last
Speaker 1: few years about what it is able to do. The
Speaker 1: interesting thing is still the stuff that it's not able
Speaker 1: to do and why. So let's talk about AI. Tell
Speaker 1: me your take on where it is currently and what
Speaker 1: all of your study about the history of intelligence tells us.
Speaker 2: So one thing that's interesting is AI today, and this
Speaker 2: moment seems to be almost taking the exact opposite path
Speaker 2: as our brains. It's starting from language, at least the
Speaker 2: sort of explosion general AI has at its foundation been
Speaker 2: language models been these things called transformers that are trained
Speaker 2: on huge amounts of language text. And what has been
Speaker 2: surprising is the degree with which language seems to be
Speaker 2: so informationally rich that from going from the top of
Speaker 2: this pyramid of the five breakthroughs, you actually can start
Speaker 2: going down. So if you ask a large language model
Speaker 2: questions that require theory of mind, which just to remind
Speaker 2: the listeners, is being able to reason about other people's
Speaker 2: knowledge or intent, language models do very good at correctly
Speaker 2: predicting what someone might do, given that they're missing certain information,
Speaker 2: and so one might have thought that in the absence
Speaker 2: of having a mind themselves, they would be quite bad
Speaker 2: at that. But what seems to actually be the case
Speaker 2: is by reading all of the texts that exists effectively
Speaker 2: in the world, it has started to infer things about
Speaker 2: other people's minds. Similarly, I would have thought that common
Speaker 2: sense questions so questions about are three redimensional worlds. For example,
Speaker 2: if you threw a baseball one hundred feet above my
Speaker 2: head and I jumped up, could I catch it? It's
Speaker 2: such a simple question for a child to answer. But
Speaker 2: what you're doing in your mind is you're rendering a
Speaker 2: three D simulation of the world, and you're looking at
Speaker 2: the ball one hundred feet above my head, seeing me jump,
Speaker 2: and realizing you'd know way you could solve that. I
Speaker 2: would have thought these types of common sense questions would
Speaker 2: fail in language models, and they did up until you
Speaker 2: get the most recent update, GBT four. It answers these
Speaker 2: common sense questions really well. However, all of that said,
Speaker 2: the way IT solves these problems are completely different than
Speaker 2: the way that human brains solve these problems, and those
Speaker 2: differences do matter. Two key things that I think AI
Speaker 2: is missing that mammal brains can do, even some fish
Speaker 2: brands can do that I think AI can learn from
Speaker 2: neuroscience is the following. The first is something called continual learning,
Speaker 2: So we don't realize it. But all AI systems today
Speaker 2: are largely trained all at once, so chat GBT doesn't
Speaker 2: update its information as it reads new articles. The way
Speaker 2: they update the system is, by and large, they retake
Speaker 2: the entire data set and they rebuild the model from scratch.
Speaker 2: And the reason they do that is because AI systems
Speaker 2: today suffer from what's called the problem of catastrophic forgetting.
Speaker 2: All that means is when you train an AI system
Speaker 2: with new data, it tends to overwrite its memories of
Speaker 2: the old data. And somehow, mammal brands and even fish
Speaker 2: brains don't forget things when they learn new information, at
Speaker 2: least not to the extent that aisystems do. So for example,
Speaker 2: if you learn to ride a bicycle, you don't forget
Speaker 2: how to drive, or vice versa. And yet somehow AI
Speaker 2: systems still suffer from this. So commercial AI systems ignore
Speaker 2: this problem because they say, we're just going to throw
Speaker 2: more money at the problem and just keep retraining systems.
Speaker 2: That's also the approach in robotics, by the way, But
Speaker 2: eventually we're going to want systems that can learn as
Speaker 2: they go, that can get to know us, that can
Speaker 2: change their approach based on how they interact with us
Speaker 2: that can be around our home, and we can show
Speaker 2: them new skills and they figure out the new skills
Speaker 2: as they go, and that's something that's unique to mammals
Speaker 2: that we have not yet figured out NA. So that's
Speaker 2: one of the big problems. The second problem is mammals
Speaker 2: have this internal model of the world, so they have
Speaker 2: this sort of rendered world in their head that adheres
Speaker 2: to the laws of physics. That's how I can imagine
Speaker 2: myself do things, and the consequences of my actions in
Speaker 2: my mind are relatively accurate for what would happen in
Speaker 2: the real world. And this enables me to build hypotheses
Speaker 2: and intervene in the world to test those hypotheses. And
Speaker 2: the reason this is so important is these AI systems today,
Speaker 2: the truthfulness of information is only as good as the
Speaker 2: data you give it. So if you give articles about
Speaker 2: the Earth being flat to the training set of chat SHEGBT,
Speaker 2: it will start thinking the Earth is flat. But the
Speaker 2: AI systems we want to create one day are going
Speaker 2: to be ones that interact with the world, build their
Speaker 2: own hypo aothesies about the world, and reject information that's
Speaker 2: inconsistent with them. Model the world and so that's going
Speaker 2: to be the way that we can get systems that
Speaker 2: can contribute to science. That's the way we're going to
Speaker 2: get systems that get more truthful over time. And that's
Speaker 2: the way we're going to get systems that don't require
Speaker 2: you know, humans to go in and manually curate these
Speaker 2: data sets. So although CHATGBT has learned on its own,
Speaker 2: the manual effort went into creating the data set on
Speaker 2: which it learned and making sure that data sets rich.
Speaker 2: So continual learning and world models that allow you to
Speaker 2: build hypotheses, in my view, are the two big missing
Speaker 2: gaps that mammal brains have. But aisystems today.
Speaker 1: General I agree. You know, last year I wrote a
Speaker 1: paper about how we would know if AI is really
Speaker 1: intelligent as opposed to a statistical parrot. And my suggestion
Speaker 1: is that scientific discovery is really the gold standard for that,
Speaker 1: because yeah, this is what humans do, and what we
Speaker 1: do with scientific discovery is not just piece facts together.
Speaker 1: That's and chat GEPT can do that, but it's the
Speaker 1: simulation of possible futures. It's what if I were writing
Speaker 1: atop a photon, what would the world look like? And
Speaker 1: you valuate that you simulate it out, and you come
Speaker 1: up with a special theory of relativity. That's the kind
Speaker 1: of thing that humans do all the time, not just Einstein,
Speaker 1: but we do that when we mentalize and simulate anything
Speaker 1: and evaluate it and say, okay, that's not going to work.
Speaker 1: But this other strategy over here, maybe that is going
Speaker 1: to yield something when I compare the results to other
Speaker 1: things I know in the world. So that's what our
Speaker 1: systems don't do currently. So this is what's really special
Speaker 1: about human brains is being able to mentalize and having
Speaker 1: and having a model of the world so that we
Speaker 1: can evaluate the outcome compare it to what we know
Speaker 1: in the world. Now you mentioned that as AI is
Speaker 1: getting better. Let's say chatchept four and whatever will come out.
Speaker 1: You know, a few months from now, you're saying that
Speaker 1: it's better and better at answering these sort of of
Speaker 1: mentalizing questions. But do you suppose it is because of
Speaker 1: a lot of feedback from humans and a lot of
Speaker 1: these examples appearing on the corpus of data that it
Speaker 1: reads that it's able to do this as opposed to
Speaker 1: actually mentalizing and having understanding.
Speaker 2: Certainly, I think one of the key challenges with evaluating
Speaker 2: these AI systems is we don't know what the training
Speaker 2: data is, so it can be hard to know if
Speaker 2: the solution to a problem or word problem you give
Speaker 2: it is because it's effectively looking up what was in
Speaker 2: the training data or actually generalizing. I do think though,
Speaker 2: there's been lots of great work where like there was
Speaker 2: a study out of Microsoft recently where they reformat some
Speaker 2: of these mentalizing questions in way that it's very hard
Speaker 2: to believe that it would be in the training data,
Speaker 2: and it still solves the problems well. To me, this
Speaker 2: is a question of how it solved the problems though,
Speaker 2: because the way that chatchebt solves these problems as it
Speaker 2: makes an inference over a whole series let's call it,
Speaker 2: millions of word problems about theory of mind questions, and
Speaker 2: so it probably builds some form of model how agents
Speaker 2: or humans act in the presence of information or lack
Speaker 2: of information. Certainly if it reads enough symbols that suggest
Speaker 2: that maybe it has some of that information in there,
Speaker 2: but that doesn't mean it solves the problem in the
Speaker 2: same way humans do. You know, when we mentalize, we
Speaker 2: compare the way our minds work and how we feel
Speaker 2: about things to how we would infer someone else does
Speaker 2: we put ourselves in someone else's shoes, And so although
Speaker 2: the performance on word problems might look the same, there
Speaker 2: might be very big differences in how we solve these problems,
Speaker 2: which might have very real consequences when we send these
Speaker 2: things out into the real world. For example, if we
Speaker 2: made a robot powered by chatchebt help one of our
Speaker 2: grandparents around the home, and we want them to empathize
Speaker 2: and understand how they feel, I would not be confidence
Speaker 2: based on the performance of word problems of theory of
Speaker 2: mind that chatsheebt is going to care infer about how
Speaker 2: my grandparent feels in this situation, versus I would feel
Speaker 2: confident that a human would because I know how a
Speaker 2: human brain is solving these tasks. So I think algorithmic
Speaker 2: differences matter the more and more we offload these TASKSDAIE systems,
Speaker 2: because otherwise performance in one task might not generalize well
Speaker 2: to these other tests.
Speaker 1: So what's interesting is I've spent a lot of time
Speaker 1: on GPT four seeing if it has theory of mind,
Speaker 1: you know, running tests on this and just for the audience,
Speaker 1: theory of mind tests would be something like Sally walks
Speaker 1: into the room and puts the baseball on the bed.
Speaker 1: Then she leaves and comes into the room, sees the
Speaker 1: baseball on the bed, picks it up, puts in the closet,
Speaker 1: and leaves. When Sally walks back in the room, where
Speaker 1: does she look for the ball? And the answer, of
Speaker 1: course is that she looks on the bed. But this
Speaker 1: requires us to be inside her head. If you ask
Speaker 1: a question like that to any of the big language models,
Speaker 1: it will get it right. But why. In part, it's
Speaker 1: because that particular test, the seal antest, is all over
Speaker 1: the Internet the gajillion places, and there are many many
Speaker 1: questions that have been asked about theory of mind that
Speaker 1: already exist on the Internet. The part that I have
Speaker 1: found so fascinating is that GPT gets this stuff right
Speaker 1: about I don't know, sixty percent of the time. So
Speaker 1: in other words, several times in a row, I'll try
Speaker 1: to make up some question that I think is new,
Speaker 1: and it gets it right, and I'm stunned, and I think, wow,
Speaker 1: I think it really has a sense of what it
Speaker 1: is to be a person. But then it will get
Speaker 1: one wrong, and it's the kind of mistake that a
Speaker 1: person wouldn't make if a person understands theory of mind,
Speaker 1: they wouldn't get this other version wrong. And that's why
Speaker 1: I find myself a little bit confused here in the
Speaker 1: middle of twenty twenty four about whether to conclude that
Speaker 1: AI has theory of mind capabilities or not.
Speaker 2: I think this goes to the semantics of how we
Speaker 2: measure this thing we call theory of mine, and this
Speaker 2: is actually what we're asking these in some ways a
Speaker 2: profound question and an open question in AI, because the
Speaker 2: entire field of machine learning operates on performance benchmarks. The
Speaker 2: entire field is based on this idea of give me
Speaker 2: an evaluation test, and then I'm going to see how
Speaker 2: well I perform on this test. But that's problematic for
Speaker 2: things like theory of mind because if you ask any
Speaker 2: scientist a theory of mind, theory of mind is defined
Speaker 2: in the mechanism, not the performance, but theory of mind
Speaker 2: is is the algorithm by which we imagine ourselves on
Speaker 2: other people's shoes. They don't define theory of mind as
Speaker 2: the ability to solve this word problem, and so we
Speaker 2: see this sort of challenge where just because it solves
Speaker 2: the word problems doesn't mean that it's solving them in
Speaker 2: the way that someone else might classify as theory of mind.
Speaker 2: So I think in some ways this is in the
Speaker 2: semantics of what do we mean when we say does
Speaker 2: the sing have theory of mind? I think it clearly
Speaker 2: is very good at solving theory of mind like word problems.
Speaker 2: I'm quite confident that it's not doing what primates do
Speaker 2: when they engage in theory of mind. And I'm also
Speaker 2: not confident that the solutions to these word problems will
Speaker 2: generalize well to other types of tasks that are not
Speaker 2: word based that require theory of mind, such as a
Speaker 2: robot around the house that has to infer how someone
Speaker 2: might feel in certain situations to proactively help them, proactively
Speaker 2: comfort them. I'm not confident that the theory of mind
Speaker 2: word problem success will translate to these other types of
Speaker 2: theory of mind problems.
Speaker 1: So to get to that robot that is like a
Speaker 1: human and really understands these things, what do you see
Speaker 1: from your framework of these five breakthroughs of intelligence? What
Speaker 1: needs to happen besides this language piece.
Speaker 2: So the big missing pieces are breakthrough three and four.
Speaker 2: We need these systems to have some form of internal
Speaker 2: world model that they're continuously updating based on interacting with
Speaker 2: the actual world. And I do think this grounding in
Speaker 2: reality is important for many of the features that we
Speaker 2: want these AI systems to have, but that will not
Speaker 2: be enough. That will maybe solve some very utilitarian functionals
Speaker 2: around the home, but I think we will quickly realize
Speaker 2: that understanding how to interact with humans and the social
Speaker 2: lives of humans will emerge as this other really important
Speaker 2: missing piece, which will require some form of mentalizing. In
Speaker 2: other words, understanding what's going on in human heads a
Speaker 2: fascinating open question that I don't have the answer to,
Speaker 2: but something we'll need to think about. One way in
Speaker 2: which humans build common ground is that our minds algorithmically
Speaker 2: are quite similar. So when I put myself in someone
Speaker 2: else's shoes, certainly there's lots of mistakes we make when
Speaker 2: trying to guess how other people feel in situations, but
Speaker 2: there is this basic grounding that we are all very similar.
Speaker 2: Our brains works relatively similarly in the scope of all
Speaker 2: possible preferences of life form could have. Humans are remarkably
Speaker 2: more similar than they are different. And yet when we
Speaker 2: build this AI system, it's not at all clear that
Speaker 2: the way it would feel about the world is going
Speaker 2: to be the way we feel about the world. And
Speaker 2: so the basic trick that it seems primate brains use,
Speaker 2: which is I reason about your mind by building a
Speaker 2: model of my own mind and projecting myself into your situation,
Speaker 2: won't work for an aisystem because it won't be the
Speaker 2: same as us. It won't necessarily have the same preferences.
Speaker 2: And so I do think that begets an interesting sort
Speaker 2: of safety challenge for us, which is, how do we
Speaker 2: make sure that they actually understand human preferences, how we
Speaker 2: feel about things, how we would feel about things, while
Speaker 2: not being grounded and having those same feelings themselves.
Speaker 1: That was Max Bennett diving into the six hundred million
Speaker 1: year history of how the human brain got here. As
Speaker 1: you can see, Max looks at evolution the way that
Speaker 1: you might look at technological innovation in the business world.
Speaker 1: When a new technology comes onto the scene, like the
Speaker 1: personal computer, it enables all kinds of new products, and
Speaker 1: it's the same when a new brain capability hits the scene,
Speaker 1: that opens the door to new sorts of skills. For example,
Speaker 1: once a brain can run internal simulations, then it can
Speaker 1: do things like remember the past, and envision possible futures.
Speaker 1: So I just wanted to summarize Max's framework here so
Speaker 1: that you can remember it. The first breakthrough happened in
Speaker 1: animals that have left right symmetry, like a human or
Speaker 1: a bird or a lizard as opposed to a starfish
Speaker 1: or a jellyfish. The first step was that these left
Speaker 1: right animals learned how to steer themselves through their environment.
Speaker 1: Break Through number two happened in vertebrates, those animals that
Speaker 1: have a spinal column. They figured out how to learn
Speaker 1: from trial and error. Break Through three happened in mammals.
Speaker 1: They learned to simulate internally, that's thinking about the past
Speaker 1: and running versions of the future. Break Through number four
Speaker 1: happened in primates in particular, and that was meant, in
Speaker 1: other words, imagining what it is like to be inside
Speaker 1: someone else's head to infer the intent of the other,
Speaker 1: and for that matter, thinking about your own thinking. And finally,
Speaker 1: break through number five happened in humans, and that was speech,
Speaker 1: which allows us to pass information rapidly from one to
Speaker 1: another and for that matter, from generation to generation. From
Speaker 1: the Library of Alexandria to the Inner Cosmos podcast, all
Speaker 1: of this is made possible by figuring out how to
Speaker 1: communicate at this high bandwidth. As a result of this,
Speaker 1: humans don't have to start from scratch every generation the
Speaker 1: way a cat or a horse does, but instead humans
Speaker 1: are able to springboard off the top of everything that
Speaker 1: has been discovered by previous humans. Collectively, these breakthroughs, which
Speaker 1: happened over hundreds of millions of years, gave us the
Speaker 1: kind of brains that we have us to do the
Speaker 1: kind of things that we do. A lot of questions remain.
Speaker 1: One of them is whether there are different paths to intelligence,
Speaker 1: as we suspect when we look at the octopus brain,
Speaker 1: which is a mollusc brain that somehow evolved along a
Speaker 1: very different sort of pathway and yet ended up at
Speaker 1: a similar spot. And once we find other sorts of
Speaker 1: intelligences in the universe, we may look back and realize
Speaker 1: there are many ways to get to intelligence from single
Speaker 1: celled organisms floating around. For all we know, intelligence is
Speaker 1: a path that is nudged into being by the pressures
Speaker 1: of evolution because of the advantages that it grants, so
Speaker 1: that things generally move in that direction. And if that's
Speaker 1: the case, if the pressures of evolution guide animals inexorably
Speaker 1: toward intelligence so they can outcompete their neighbors. Then what
Speaker 1: a pleasure it would be to visit the Earth six
Speaker 1: hundred million years from now, when lots of other species
Speaker 1: have reached new elevations in that long road. They've reached
Speaker 1: those heights that give them the kind of view that
Speaker 1: has allowed us to invent and create and discover and
Speaker 1: intellectually explore. Go to Eagleman dot com slash podcast for
Speaker 1: more information and to find further reading. Send me an
Speaker 1: email at podcasts at eagleman dot com with questions or discussion,
Speaker 1: and check out and subscribe to Inner Cosmos on YouTube
Speaker 1: for videos of each episode and to leave comments. Until
Speaker 1: next time. I'm David Eagleman, and this is Inner Cosmos