Speaker 1: What is special about the wrinkly outer layer of the brain,
Speaker 1: the cortex, And what does this have to do with
Speaker 1: the way that you come to explore and understand the world.
Speaker 2: And by the way, why do you.
Speaker 1: See a whole image when you open your eyes even though.
Speaker 2: Each part of your visual.
Speaker 1: Cortex has access to only a tiny bit of the image.
Speaker 1: And for that matter, the brain is divided into different
Speaker 1: areas for sight and sound and touch and so on.
Speaker 1: And so why when you're petting a cat, why does
Speaker 1: the cat seem unified? Why doesn't the site of the
Speaker 1: cat seem separate from the purring and the feel of
Speaker 1: the fur. Can we build a new model of how
Speaker 1: the brain works and in what ways is what the
Speaker 1: brain doing something very different than what's happening in current AI.
Speaker 1: Welcome to Intercouse with me David Eagleman. I'm a neuroscientist
Speaker 1: at Stanford and in these episodes we sail deeply into
Speaker 1: our three pound universe to understand.
Speaker 2: Why and how our lives look the way they do.
Speaker 1: Today's episode is about a new model of the brain
Speaker 1: developed by my friend and colleague, Jeff Hawkins, and we'll
Speaker 1: get into an interview with him shortly but let me
Speaker 1: preface by saying that for centuries people have stared at
Speaker 1: the brain and tried to figure out how this thing works.
Speaker 1: Because when you stare at it, it's just a huge
Speaker 1: lump of cells.
Speaker 2: You can see that.
Speaker 1: There's a wrinkled layer on the outside. And when people
Speaker 1: dissect that, they can see that that part is about
Speaker 1: three millimeters thick, and it looks a little different, looks grayer.
Speaker 1: And so that part is called the gray matter. And
Speaker 1: we call this the cortex, which means bark, like tree bark.
Speaker 1: And the stuff below that thin layer is called white matter.
Speaker 1: And it looks white because the tiny data cables coming
Speaker 1: off the cells, the axons, these are wrapped in a.
Speaker 2: Little sheath called myelin, which makes it look white.
Speaker 1: Okay, Now, what you immediately noticed by looking at brains
Speaker 1: across different mammals is that all the stuff you find
Speaker 1: under the cortex, all the sub cortical stuff, looks essentially
Speaker 1: the same. Horses and elephants and mice. They all have
Speaker 1: the same architecture going on that we do.
Speaker 2: They all have.
Speaker 1: A thalamus and hippocampus and cerebellum and so on. But
Speaker 1: there's one thing that really distinguishes us from our cousins,
Speaker 1: and for that we return to the gray matter, the cortex.
Speaker 1: It's not that our cousins don't have a cortex. What
Speaker 1: distinguishes us is the absolute enormity of our cortex. We
Speaker 1: humans have a ton of this stuff. So take four
Speaker 1: pieces of paper from your printer and place them next
Speaker 1: to each other to make one really large piece.
Speaker 2: That's how much cortex.
Speaker 1: A human has. If you were to spread out the wrinkles. Now,
Speaker 1: our nearest cousins, the great apes only have about one
Speaker 1: piece of paper worth, and most mammals have a lot
Speaker 1: less than that. So something about the story of the
Speaker 1: runaway human success has to do with the fact that
Speaker 1: we have way more cortex for our body size than
Speaker 1: any other creature. And side note, I'm really talking about
Speaker 1: what's called the neocortex or new cortex, because we also
Speaker 1: have a little bit of paleocortex or old cortex. But
Speaker 1: the thing that really makes us outstanding is the amount
Speaker 1: of neo cortex that we have.
Speaker 2: But what is this neocortex doing.
Speaker 1: Well, if you look at any neuroscience textbook, you'll see
Speaker 1: that this part of the brain, the cortex, is often
Speaker 1: drawn with different colored regions like this red region over
Speaker 1: here is devoted to vision, and this green one is
Speaker 1: devoted to hearing, and this yellow one to touch and
Speaker 1: so on. But something I've been obsessed with and write
Speaker 1: about in my latest book, Live Wired, is that this
Speaker 1: is the wrong way to think about it, because the
Speaker 1: neocortex is remarkably flexible.
Speaker 2: It's not a fixed map.
Speaker 1: If you are born blind, the part of your cortex
Speaker 1: that we would have thought of as visual cortex gets
Speaker 1: taken over by hearing and touch and so on. Now
Speaker 1: let me just be really clear what I mean by
Speaker 1: taking over. The neurons there are the same The cortex
Speaker 1: looks exactly the same from the outside, but the function
Speaker 1: of those particular neurons is now not visual. They have
Speaker 1: nothing to do with visual information anymore. Now that same
Speaker 1: neuron instead of firing when it detects a moving object,
Speaker 1: now it responds to a touch on your.
Speaker 2: Toe, or hearing a B flat note or whatever. So
Speaker 2: the little labels that.
Speaker 1: We draw onto the brain, these maps that we impose,
Speaker 1: these are.
Speaker 2: Actually massively flexible.
Speaker 1: And as you may know, I gave a talk at
Speaker 1: TED about this a while ago, where I showed that
Speaker 1: you can feed in new kinds of information, let's say
Speaker 1: through the ears or the skin, and the brain will
Speaker 1: figure out how to deal with that data. It will
Speaker 1: flexibly devote part of its cortical real estate to that.
Speaker 1: And this line of thinking led some scientists, like Vernon
Speaker 1: mount Castle some decades ago to realize that the cells
Speaker 1: of the cortex are.
Speaker 2: A one trick pony.
Speaker 1: No neuron is inherently a visual neuron or a neuron
Speaker 1: devoted to hearing or touch or smell or taste or
Speaker 1: memory or whatever. All parts of the cortex are perfectly
Speaker 1: capable and willing to take on any job. So that
Speaker 1: suggests they're all running some sort of basic algorithm. And
Speaker 1: it doesn't matter what kind of data you feed in.
Speaker 1: Different parts of the cortex will say cool, I'll build
Speaker 1: a representation of that data. I don't care if it
Speaker 1: comes from photons or air compression waves or temperature or whatever.
Speaker 1: I'm on the job here to build an understanding of
Speaker 1: whatever is coming in locally. Now, it's not individual neurons
Speaker 1: that are building models, but instead groups of many tens
Speaker 1: of thousands of neurons arranged in a six layered cylinder.
Speaker 1: So think about this like you're a geologist and you
Speaker 1: drilled out a cylinder of rock and you saw six
Speaker 1: layers in it, six sedimentary layers.
Speaker 2: That's what the neocortex looks like.
Speaker 1: Six layers. And it's built out of these columns which
Speaker 1: have the same types of neurons with the same connection
Speaker 1: patterns in each column. And so think about the cortex
Speaker 1: as being made of lots of these columns, like taking
Speaker 1: hundreds of thousands of grains of rice and standing them
Speaker 1: up on their end and packing them all next to
Speaker 1: each other. Now, people have known about cortical columns for
Speaker 1: many decades since Vernon Mountcastle first discovered these in nineteen
Speaker 1: fifty seven. But recently someone has pulled together several different
Speaker 1: threads to propose how this could underlie what the cortex
Speaker 1: is all about. And that's someone is Jeff Hawkins and
Speaker 1: his team. And so I met with Jeff in my studio. Now,
Speaker 1: Jeff is one of my favorite people because he does
Speaker 1: theoretical neuroscience. He really tries to figure out the big
Speaker 1: picture of what the brain is doing. Now, Jeff has
Speaker 1: a very interesting history, so I'll just mention that in
Speaker 1: the nineteen eighties he was a graduate student at Berkeley,
Speaker 1: where he proposed a PhD thesis on a new theory
Speaker 1: of the cortex, but his proposal was rejected, and so
Speaker 1: he ended up pursuing his vision for mobile computing instead,
Speaker 1: and in nineteen ninety two he launch the company Palm,
Speaker 1: which made the Palm Pilot. If you remember that, this
Speaker 1: was this little handheld device and you could write on
Speaker 1: it with a stylus and it would translate your handwriting
Speaker 1: into text. And you can use this for your address
Speaker 1: book and your calendar and your contacts and note taking.
Speaker 1: This was the first entrant into the world of portable computing,
Speaker 1: and it.
Speaker 2: Really changed the world.
Speaker 1: Anyhow, A decade later, Jeff returned to his original love,
Speaker 1: which was theoretical neuroscience, trying to figure out what's going
Speaker 1: on with the brain, and he wrote a book in
Speaker 1: two thousand and four called on Intelligence, which was very
Speaker 1: influential on me and lots of other thinkers I know.
Speaker 1: So I was very excited when Jeff recently came out
Speaker 1: with his next book that represents his last decade and
Speaker 1: a half of research. It's called One Thousand Brains, a
Speaker 1: New Theory of Intelligence, and it describes his framework for
Speaker 1: thinking about the brain. So, without further ado, let's dive
Speaker 1: into a very cool new model of the brain. Okay, Jeff,
Speaker 1: So you are a theoretician. You think about the brain
Speaker 1: from a high level. We're in this era now of
Speaker 1: AI where AI is doing all kinds of things that
Speaker 1: are amazing and no one expected. But you see the
Speaker 1: brain as being very different from what is going on
Speaker 1: with let's say, large language models. So tell us about that.
Speaker 2: That's absolutely true. You know, the current AI wave is
Speaker 2: really amazing, but those models don't work at all like
Speaker 2: the brain. And I think you could start with one
Speaker 2: really fundamental difference. Brains work through movement. We move our
Speaker 2: bodies through the world. We move our hands over objects
Speaker 2: to touch and learn what they are. We move our
Speaker 2: eyes constantly, so the inputs of the brain are constantly changing,
Speaker 2: but mostly because we're moving through the world. And the
Speaker 2: term for that is a centory motor system. And the
Speaker 2: brain can't understand its inputs unless it knows how it's
Speaker 2: moving through the world. So we learn by exploring, by
Speaker 2: moving different places, picking things up, touching, so on, and
Speaker 2: that's all Animals that move in the world learn this way.
Speaker 2: So this idea that the brain is the central motor
Speaker 2: system has been known back in the late eighteen hundreds,
Speaker 2: but it's pretty much ignored by everybody. But it leads
Speaker 2: to a very fundamental different way of how we acquire
Speaker 2: knowledge and how knowledge is represented in the brain. Whereas
Speaker 2: today's AI is most of it's built on well deep
Speaker 2: learning of transformer technologies, which are essentially we feed data
Speaker 2: to it. We don't it doesn't explore it, and we
Speaker 2: feed to large language models. We just feed a language.
Speaker 2: So there's no inherent knowledge about what these words mean,
Speaker 2: only what these words mean in the context of other words. Right,
Speaker 2: But you and I can pick up a cat and
Speaker 2: touch it and feel it and know this warmth, and
Speaker 2: we understand how its body's moved because no one has
Speaker 2: to tell us that. We just experience it directly. So
Speaker 2: this is a huge gap between brains. Pretty much all
Speaker 2: brains work by century motor learning and almost all oh
Speaker 2: it doesn't. And you can just peel the layers apart
Speaker 2: and see what the differences are, and it makes a
Speaker 2: huge difference. So I'm not a fan. I'm a fan
Speaker 2: of AI today, but I don't think it's the future
Speaker 2: of AI. I don't think it's going to get you
Speaker 2: to what people really want or truly intelligent machines.
Speaker 1: Okay, terrific, And we'll dive into that more in a
Speaker 1: little bit.
Speaker 2: Now.
Speaker 1: When we look at let's say the human brain, there's
Speaker 1: lots of areas that we can point to. There's the
Speaker 1: cortext and wrinkly outer bit, there's all these subcortical areas.
Speaker 1: When you think about intelligence and the stuff that we're
Speaker 1: going to talk about today, what is the part that
Speaker 1: you concentrate on.
Speaker 2: Well, we concentrate first and foremost in the New York cortext,
Speaker 2: which is about seventy five percent of the volume of
Speaker 2: your brain. I mean it's what you see, as you said,
Speaker 2: if you can take a scope, and that's what you
Speaker 2: see in the New York cortext. And so it's a pretty
Speaker 2: dominant part of what we think of intelligence. You can't
Speaker 2: consider it completely on its own. I mean, it's connected
Speaker 2: to all these other things. And so we also study
Speaker 2: those other things and in service to the new or cortex.
Speaker 2: So we study the thalmbists, and we study the cerebellopment,
Speaker 2: We study the basic just because you have to know
Speaker 2: how the cortex workship these other things. But our primarily
Speaker 2: our goal and many neurosign this goal is to understand
Speaker 2: the New York cortex, because that's what mammals have. We've
Speaker 2: got a big one. You know, everything we think, most
Speaker 2: of what we think about being intelligent, about our ability
Speaker 2: to understand the world and generate language and see and
Speaker 2: hear and and so on, is the New York cortext
Speaker 2: not one hundred percent, but most of it. Unfortunately. Also,
Speaker 2: not only is it the biggest structure, but it's a
Speaker 2: very very regular structure. So you can look at this thing,
Speaker 2: the New or Courts is like a sheet of cells.
Speaker 2: It's you know, it's like a size of a large
Speaker 2: dinner napkin and only a few millimeters thick, and it
Speaker 2: gets wrinkly because it's stuck it in your head that
Speaker 2: everywhere you look on it, it looks remarkably complicated and remarkably
Speaker 2: the same. So the areas are doing vision, look like
Speaker 2: the areas are doing languages, look like the areas are
Speaker 2: doing touch. Look the areas that are doing everything. Really
Speaker 2: and so there's been long speculated that there's sort of
Speaker 2: a common algorithmic principle that's applying to everything everything we do,
Speaker 2: all of our sensory inputs, all of our thinking, all
Speaker 2: the language, it's hard to believe, but the evidence is overwhelming,
Speaker 2: and so our research has really been to understand what
Speaker 2: is that that algorithm, corical algorithm, often referred to as
Speaker 2: a cortical column. You know, this repeated structure that seems
Speaker 2: to underline vision and hearing and touch and thought and
Speaker 2: everything we do. And that's that's just just an appealing
Speaker 2: thing to try to understand. And we've cracked it. We've
Speaker 2: actually we actually cracked it. We understand what's going on.
Speaker 2: That's awesome.
Speaker 1: Okay, so a couple of things, right, So the way
Speaker 1: I sometimes phrase this to people is that if I
Speaker 1: had a magical microscope and could show you a part
Speaker 1: of the brain and you can see all the activity
Speaker 1: running around in the cortext there, could you tell me
Speaker 1: is that visual cortex are auditory or somatosensory? And the
Speaker 1: answer is you couldn't tell me, and I couldn't tell you.
Speaker 2: Guys, Right, it all looks the same, and there's a
Speaker 2: and there's as you know, oh, there's these experiments people
Speaker 2: have done where well, first of all, if you have
Speaker 2: trauma to one part of the cortext other parts will
Speaker 2: pick up the same function. You can also people re
Speaker 2: routed sensory and puts the different parts of the cortext
Speaker 2: in animals and they seem the work.
Speaker 1: So, for example, you have visual information instead of going
Speaker 1: to the back of the visual cortex, that gets rerouted
Speaker 1: to the auditory cortex, and that auditory cortex becomes visual cortext.
Speaker 2: Right, It's incredibly powerful and flexible system. And mammals, you know,
Speaker 2: all we have we have a set of sensors, quite
Speaker 2: a few action more than most people think because the
Speaker 2: skin there's a lot of different sensors. But other animals
Speaker 2: have different sensors and they have cortext too, And so
Speaker 2: there's seems to be this universal algorithm that can be applied.
Speaker 2: And now we know it's a century motor algorithm. It
Speaker 2: can be applied, and and we've spent decades trying to
Speaker 2: figure this out and we've cracked it. Oh that's awesome.
Speaker 1: Just before you tell us about that, So tell us
Speaker 1: what a cortical column is.
Speaker 2: Okay, So imagine we talked about. The near cortex is
Speaker 2: a sheet of cells like three milimeters stick. A cortical
Speaker 2: column is a little section of that going through the
Speaker 2: three free milimeters. It's convariating with from a third of
Speaker 2: a millimeters to a millimeters in diameter. It's it's it's
Speaker 2: a it's not something you would see. It's not like
Speaker 2: it's sitting there to be plucked out. But we know
Speaker 2: they exist, and so within that, let's let's say it's
Speaker 2: a three millimeters tall and a half millimeters wide cylinder
Speaker 2: that goes across the cortex that contains all the neural
Speaker 2: machinery that you would see anywhere in the cortex and
Speaker 2: in each cortical column, because they look like a little
Speaker 2: grain of rice in some sense. Why you can imagine
Speaker 2: what's a little brains of ice stacked next to each other.
Speaker 2: Each cornico column gets input from some well in parts
Speaker 2: of the bend they get from some pats of sensory input,
Speaker 2: so from pats to the retina, from pats to the
Speaker 2: cochlea of patrio skin. Other parts get information from parts
Speaker 2: of the near cortex. So the cortex is connected to cortex,
Speaker 2: but each one is looking at a small If you
Speaker 2: think about the primary entry regions of the cortex, which
Speaker 2: are quite large, they're getting input from a small sensory area, right,
Speaker 2: And so people used to think that, well, if this
Speaker 2: bil colm is only getting input from a small part
Speaker 2: of the rent. Now, it can't really doing very much right,
Speaker 2: It can't be very smart. All you could do is
Speaker 2: process a little piece of information there, and therefore maybe
Speaker 2: it's going to detect an edge or something like that,
Speaker 2: and there's a lot of evidence for that. But we
Speaker 2: now know what happens is that the cortical comms they
Speaker 2: get input from over time, from different parts of the world.
Speaker 2: So the eyes are moving like three times a second,
Speaker 2: and so that cortical comms may be looking at three
Speaker 2: different things every second, and it can integrate how the
Speaker 2: sensor is moving, how your eyes are moving, with what
Speaker 2: it's sensing to build models that are much larger than
Speaker 2: it can sense. And the same way that you could
Speaker 2: take your finger in a dark room and say, okay, David,
Speaker 2: I want you to learn this new object. Let's call it.
Speaker 2: You know, a coffee cup. You never touch it, and
Speaker 2: so you could do is you touch the coffee cup
Speaker 2: and you move your finger along and around, and as
Speaker 2: you do, you build a three dimensional model of the cup.
Speaker 2: Even though you're only getting input from one fingertip, the
Speaker 2: eyes are doing the same thing. It's surprising you don't
Speaker 2: realize this so every quarter do Colm, when we understand
Speaker 2: now is doing this sort of processing movement, information and
Speaker 2: sense for information, building what we call structure or three
Speaker 2: D models of things in the world. So it's quite
Speaker 2: different than even those neurosciences think about it, and there's
Speaker 2: a lot of reasons we can talk about how it
Speaker 2: was missed for all these years.
Speaker 1: So in the court, you have a century six layers
Speaker 1: of cells, and a column is all six layers, is
Speaker 1: all six layers. It's going up and down. It's like
Speaker 1: think of it like layers of a cake. And the
Speaker 1: column is you're taking a straw and shoving it through
Speaker 1: the top, and so you've got.
Speaker 2: This, Okay, got a straw cake.
Speaker 1: Okay, great, And so the idea is if you're looking
Speaker 1: at some column in you know, in primary visual cortex. Yeah,
Speaker 1: your point, Jeff was that, you know, it's it's like
Speaker 1: looking at the world through a straw.
Speaker 2: It only sees a little tiny piece of the world.
Speaker 1: But because the eyes are moving arout, because you're exploring
Speaker 1: the world, this is actually getting lots of parts of information.
Speaker 1: It's exploring the world in the same way that your
Speaker 1: finger typically.
Speaker 2: Right, and it has to integrate information over time, that's
Speaker 2: the key, right, And you can literally do this. You
Speaker 2: can look at the world through a straw, right, and
Speaker 2: and you can say, oh, what am I looking at? Well,
Speaker 2: you can't tell them. You start moving the straw and
Speaker 2: then you can start and you can also learn objects
Speaker 2: that way. So literally you can learn by looking through
Speaker 2: a straw, which is what sort of what one column
Speaker 2: is doing? Got it?
Speaker 1: And in your model there are thousands of such columns
Speaker 1: and each one of these is learning a model of
Speaker 1: the world as it's going. So tell us about right, right.
Speaker 2: So I think this idea that there's all these columns
Speaker 2: is not a new idea and that they have this
Speaker 2: fundamental argithm. But what we were I think the first
Speaker 2: people to kind of figure out what it is and
Speaker 2: what it's doing. So the trick of this thing is
Speaker 2: it's trick it here. You know, when you look out
Speaker 2: at the world, you have a sense anybody you have
Speaker 2: a sense where things are. I have a sense where
Speaker 2: you are relative to me. I have a sense where
Speaker 2: this microphone is relative to me. I know where my
Speaker 2: hand is relative to this cop I Now there turns
Speaker 2: out that you have any kind of sense of location
Speaker 2: in space, you have to have neurons representing it. There's
Speaker 2: nothing goes on in the brain if there aren't neurons
Speaker 2: firing doing it. Turns out most of the machinery in
Speaker 2: the New York cortex is keeping track of where things
Speaker 2: are relative to other things. So those six layers, all
Speaker 2: those cells, at least half of that circuitry is tracking
Speaker 2: where the sensory input is coming from in the world.
Speaker 2: So if I move my finger over this coffee cup,
Speaker 2: the part that's getting information from the sensory like I'm
Speaker 2: sensing an edge, for your example, as I move my finger,
Speaker 2: it has to keep track of where my finger is,
Speaker 2: a location of it and its orientation relative this cup.
Speaker 2: Is quite complicated, but that's what it has to do
Speaker 2: to build the models. And now we know how it
Speaker 2: does it. There's all this evidence for it. So the
Speaker 2: brain is just trying to track of where all of
Speaker 2: its inputs are in the world, all relative other things.
Speaker 2: Then it builds up these three dimensional models of the world.
Speaker 2: So tell us about how it does that then, right,
Speaker 2: so you can think about when you're in high school,
Speaker 2: you learned about Cartesian coordinates, x, y, and z coordinates, right,
Speaker 2: and so if I wanted to say where is something?
Speaker 2: Where are your relative to me? I might say, okay,
Speaker 2: your nose the origin, and I could say it's some
Speaker 2: distance from here, and you know X, Y and Z.
Speaker 2: Well you have to have something like that. But brains
Speaker 2: don't do it that way. They do it another way.
Speaker 2: And this was some very clever research in the last
Speaker 2: twenty years that people discovered in the antarilo cortex and hippocampus.
Speaker 2: These cells called grid cells and play cells, which actually
Speaker 2: operate as reference frames. They are a way of neurons
Speaker 2: to represent locations and they work differently than X, Y
Speaker 2: and Z, so there's no origin. It's kind of really
Speaker 2: clever how they work. The nature has discovered a different
Speaker 2: way of doing this, so yeah, make sure you tell
Speaker 2: us a little bit about that. Well, okay, but these
Speaker 2: these are well known thing. It's just like grid cells,
Speaker 2: which entronoic cord six. What they do is they these cells,
Speaker 2: if you take a set of them, individual cells could
Speaker 2: are not unique, and any d real sell me said,
Speaker 2: I fired different locations in space, but if you take
Speaker 2: a set of them, they're unique, and so you can
Speaker 2: encode a unique location in space. And the key thing
Speaker 2: about them is these cells automatically update as you move.
Speaker 2: So the original grid cells are where your body is
Speaker 2: in a room, and as you move, it's called past integration.
Speaker 2: It says, okay, you're moving at this dis direction at
Speaker 2: this speed, so we'll just automatically update these neurons. Is
Speaker 2: if we know where you are right and so it's
Speaker 2: it's what sales used to do dead reckoning. You just say, oh,
Speaker 2: you know, I could I'm heading north for an hour
Speaker 2: or three knots there for all these three miles in
Speaker 2: this direction. So we know that these cells exist. They've
Speaker 2: been well studied, people with Nobel Prize for these things.
Speaker 2: So we speculated that the same neural mechanisms, these grid
Speaker 2: cells and equivalents would be in the cortex at every
Speaker 2: corticle home and sure enough they're finding that now. So
Speaker 2: all kinds of research now they're finding in humans and
Speaker 2: other animals that there are grid cell like structures in
Speaker 2: cortical column And so what does that tell you? It
Speaker 2: tells me that that's the mechanism by which the brain
Speaker 2: uses for reference frames. And so literally, when you build
Speaker 2: a model of something in the world, like a model
Speaker 2: of a cup or a model of anything, it's essentially
Speaker 2: what you're doing. You're just saying, here's the sensation, and
Speaker 2: here it's location. Here's another sensation a different location. Here's
Speaker 2: another sensation at a different location. You add all these together
Speaker 2: and you get a three dimensional model. You can say,
Speaker 2: this thing consists of these features in these locations relative
Speaker 2: to each other. And so literally, in our head we
Speaker 2: build models of the world that are three dimensional analogs
Speaker 2: of the physical things we interact with. And that's why
Speaker 2: you appears three dimensional to me. You know you're not
Speaker 2: an image, You're a three dimensional structure because I have
Speaker 2: a three dimensional model of humans, and I have a
Speaker 2: special model for you, David.
Speaker 1: Okay, great, okay. So you've got these columns in the cortex.
Speaker 1: They're building three dimensional models or keeping track of where
Speaker 1: your fingertips are, where your eyes are. So we've got
Speaker 1: these different windows into the brain. You've got these data
Speaker 1: cables coming in carrying spikes. It's all spikes, but some
Speaker 1: of them carrying visual intraces to monitory is some touch.
Speaker 2: Every brain doesn't know that, by the way, exactly right.
Speaker 2: It's all the spikes exactly right. And so for any
Speaker 2: particular column, it might only be getting a subset of
Speaker 2: those tell us about that, right, right? Well, any well,
Speaker 2: I'm not shore going to be a subset.
Speaker 1: But what I mean is if I if I am
Speaker 1: a cortical column that happens to be sitting in the
Speaker 1: visual cortex, that I happen to be getting visual information,
Speaker 1: but I'm not getting auditorial So.
Speaker 2: There's a real One of the first things we had
Speaker 2: to address with this series is why does the world
Speaker 2: appear unified? Right? I don't feel like you know, I
Speaker 2: I don't feel like, oh, I'm touching something with my
Speaker 2: hands and I'm looking at something else in my eyes.
Speaker 2: It's all one thing. There's this cup, right, and I
Speaker 2: feel the warmth of it, and I know it. I mean,
Speaker 2: it's one thing. It's and yet we have all these
Speaker 2: different models. So it turns out you have models of
Speaker 2: cups and that are taxile models. They're based on how
Speaker 2: how it feels. You have models of how it looks.
Speaker 2: You might even have a model how it sounds like
Speaker 2: this particular ceramic cup. I have an expectation what it
Speaker 2: sound like. I put on this account here my different
Speaker 2: ceramic counter. And yet these models are they're all independent,
Speaker 2: but they're not completely in pandit so there's these long
Speaker 2: range connections in the cortex. They go from all different
Speaker 2: sides to the left side of the brain and the right
Speaker 2: side of the brain and all over the place. There's lots
Speaker 2: of different types. What they're essentially doing is they're voting.
Speaker 2: They're all saying, like one my finger says, I think
Speaker 2: I'm touching something that feels like a cup, and I
Speaker 2: may not be certain. Another thing, I have something too
Speaker 2: that's I'm not really certain, and the nice thing and
Speaker 2: they very quickly are reaching the set. The only thing
Speaker 2: that makes sense for all our input is we're all
Speaker 2: looking at the same object. And so there's like across
Speaker 2: these long range connections, it'll into a percept that's what
Speaker 2: you perceive. You don't actually normally perceive the individual sensations
Speaker 2: from your eye or your fingers. You just say, I'm
Speaker 2: holding this cup in my hand, and it's one percept.
Speaker 2: And so it's these long raine connections and how these
Speaker 2: columns vote all the time. This is why I can
Speaker 2: flash an image in front of your eye and say, okay,
Speaker 2: well each column is looking at part of that image.
Speaker 2: Who decides what the whole image right? And by the way,
Speaker 2: I don't even have time to move my eyes. Once
Speaker 2: I've run into objects, I don't have to move my
Speaker 2: eyes to recognize them. Man, what we call a flash inference.
Speaker 2: The reason is because each part of the court to
Speaker 2: visual cortext has a hypothesis about what it might be seeing,
Speaker 2: and they vote, and the only thing that makes sense
Speaker 2: is the final thing they agree upon. So I have
Speaker 2: to learn by moving my eyes, by tending to different
Speaker 2: things and my fingers. But I don't always have to
Speaker 2: infer or recognize things by movement. I don't always have to.
Speaker 2: I can just flash an image in front of you
Speaker 2: and you say, I know what that is, and you
Speaker 2: don't have time to move rise. This folded a lot
Speaker 2: of vision researchers for many years because they assume that
Speaker 2: the movement was necessary because I can flash an image
Speaker 2: in front of you. But you can't learn that way.
Speaker 2: You have to learn by attending to different things, quite right,
Speaker 2: Just so it's clear to the audience.
Speaker 1: So this issue about voting, it's not that they're all
Speaker 1: submitting their votes to some central agency. It's that they're
Speaker 1: all talking with one another simultaneously, simultaneously. And something about
Speaker 1: the spike patterns holds into shape.
Speaker 2: Right, right, well, we know exactly how this occurs. We
Speaker 2: have models of it, and we've simulated and matches of neuroscience.
Speaker 2: It's a little it takes a little while for people
Speaker 2: to get the sense of it. You're right, there's no
Speaker 2: central voting tally. It's like it's and I don't have
Speaker 2: all the commns, don't have to talk to all the
Speaker 2: other comms. It turns out they only have to stalk
Speaker 2: to a few other commns as long as everyone talks
Speaker 2: to somebody and the whole thing is connected, so they
Speaker 2: don't have to like in Zilian connections. But it's it's
Speaker 2: more like you have a neuro you know, magic neurons
Speaker 2: are spiking, and in I have I have five thousand
Speaker 2: neurons that representing what I'm seeing. That's not that man actually,
Speaker 2: so five thousand neurons and in the brain. We're getting
Speaker 2: a little technical here. Activations are typically sparse, meaning of
Speaker 2: those five thousand cells, maybe only two percent or one
Speaker 2: hundred are active at any point in time. The others
Speaker 2: are silent. So I'm representing something by saying there's one
Speaker 2: hundred neurons active out of five thousand. Now, if I
Speaker 2: wasn't certain, I might say, oh, well, let's do this.
Speaker 2: I'm going to say it could be object day, it
Speaker 2: could be object being, could be object C. And I'm
Speaker 2: gonna activate them all the same time. So now I
Speaker 2: have three hundred neurons out of five hundred they're simultaneously active.
Speaker 2: Now that might seem confusing, but it isn't. No trouble
Speaker 2: is this and everybody's doing the same thing. They're all
Speaker 2: doing multiple hy positives, and it very quickly says you're
Speaker 2: supporting this positive and you're supporting this po. It happens simultaneously.
Speaker 2: No one has to go through so early. There's no
Speaker 2: like counting the vote. So let's try this like positive
Speaker 2: in this it all settles very very quickly.
Speaker 1: It's kind of cool thing if you thought about what
Speaker 1: happens when you settle on a hypothesis and then you switch.
Speaker 1: For example, looking at the Neckar cube, this cube made
Speaker 1: out of it, yes, twelve lines.
Speaker 2: What you know?
Speaker 1: You see it one way, then you see it the
Speaker 1: other way? What is it that allows it to switch? All?
Speaker 2: Right? Now, a Necker cube is a two dimensional image, right,
Speaker 2: it's a two dimensional image of a three dimensional wireframe
Speaker 2: cube or something like that. Right, And so it's not
Speaker 2: three dimensional. It's really two dimensional, but your brain wants
Speaker 2: to make it three dimensional, right because it doesn't know
Speaker 2: two dimensional things that look like that, And so everything
Speaker 2: we try to do fits into our models, right, Right.
Speaker 2: We don't say, oh, that's a two dimensional image. It
Speaker 2: can't be a cube. No, you says, oh, no, that's
Speaker 2: got to be a cube, because I know cubes. I
Speaker 2: don't think it looks like that's not a cube. So
Speaker 2: it wants to settle on a hypothesis. Is like, okay,
Speaker 2: well this corner is in front of that corner, and
Speaker 2: this corner is behind that corner, the corner to the
Speaker 2: left of that corner. It just has to do that
Speaker 2: to fit its models. That's right.
Speaker 1: But why doesn't it land on a hypothesis and stick there.
Speaker 2: Well, I don't really know, but there's other people hypothesis
Speaker 2: about this is that the evidence goes both ways, right,
Speaker 2: there's multiple hypositive and so neurons have a way of
Speaker 2: getting tired about what they're doing. After a while, they say,
Speaker 2: you know, literally, they have a way of they say,
Speaker 2: you know, I'm not going to keep finding on this forever.
Speaker 2: You know, things are changing the world. We don't just
Speaker 2: get stuck. So there's various speculated mechanisms BEHUWD neurons, and
Speaker 2: it's been observed. We'll sort of, you know, say okay,
Speaker 2: I'll be active a little while and then I'm going
Speaker 2: to stop U. Lets someone else try something, right.
Speaker 1: Yeah, it, But what it means is that the other
Speaker 1: hypothesis has to be kept alive somewhere somehow.
Speaker 2: Well it maybe not. Maybe just like I have this
Speaker 2: hypothesis that locked in on it, and now I'm going
Speaker 2: to say that's no longer possible. Just go back to
Speaker 2: square one. What is possible? You know? So it's not
Speaker 2: like I have these two images in my head conceptual
Speaker 2: or perceptually. You don't feel that way, right, You only
Speaker 2: one or the other? Do you lock in the one?
Speaker 2: The other is forgotten? But then if i'd say disabled
Speaker 2: the first hypothesis, We're not gonna allow that be anymore.
Speaker 2: Then it's okay, what's possible. This one's possible, I'll switch
Speaker 2: to that one. It's not like they're both active. One's
Speaker 2: active and then it gets tired and then the other, well,
Speaker 2: I work.
Speaker 1: So coming back to the main thing, one part that
Speaker 1: I want to return to is just this issue that
Speaker 1: a particular column might only be receiving touch information. Another
Speaker 1: call might be receiving only auditory information, and so on.
Speaker 2: Well, they build independent models, right they. I could have
Speaker 2: a tactle model of an object, a visual model of
Speaker 2: an object, right, they're not the same. The visual model
Speaker 2: of the object will have color, perhaps the tact will
Speaker 2: have temperature and texture and things like that. So they're
Speaker 2: different models. But because they can vote, you have a
Speaker 2: single percept of it. Yeah, okay.
Speaker 1: And one of the things that's important here, which of
Speaker 1: course you have, so you and I both emphasize this
Speaker 1: is a lot in our books, is that all we
Speaker 1: are ever seeing is our model of the world, right,
Speaker 1: and so we don't have any direct access to what's
Speaker 1: actually out there.
Speaker 2: And so the fact.
Speaker 1: You mentioned earlier the binding problem, if you mentioned it
Speaker 1: by name. But the binding problem is this issue that
Speaker 1: when the coffee cup is here and it's moving, how
Speaker 1: come the color doesn't bleed off the cup?
Speaker 2: And how come it seems like one thing and so on.
Speaker 2: Buying problem is a poorly defined problem. Exactly. It means
Speaker 2: a lot of different things, a lot of different people.
Speaker 2: So you gotta be really careful a say, oh, I
Speaker 2: let's talk about the binding problem, I might have a
Speaker 2: different perception of what the binding problem is.
Speaker 1: To me.
Speaker 2: The binding problem is the one I've already discussed, which
Speaker 2: is you have these different sensory inputs that but somehow
Speaker 2: they lead to a single perscept and you can switch
Speaker 2: back and forth. It's like, I don't It's like, how
Speaker 2: do I bring these things together? How do I say
Speaker 2: these are all the same thing? And people used to
Speaker 2: think in the binding problems like, oh, if I have
Speaker 2: the auditory cortex and the visual cortext and somautter centric
Speaker 2: cortex touch, then they must all project to someplace where
Speaker 2: they are binding together into a single model. And we
Speaker 2: flip that on its head. They don't bind. They bind
Speaker 2: together through just long range connections. But there's no place
Speaker 2: I have to do that. There's no nobody's sitting on
Speaker 2: top of it and saying, hey, what's your vote? What's
Speaker 2: your vote? Now? It's just like, so there, we don't
Speaker 2: need a model that incorporates all the aspects of objects.
Speaker 2: We have independent models that we can invoke as needed,
Speaker 2: and they all they all vote to reach common consensus.
Speaker 2: So I have no problems navigating, you know, doing things
Speaker 2: in the dark. I have no troubles doing things just
Speaker 2: by vision. I have no I can do things sometimes
Speaker 2: of audition, you know, like I know the same things
Speaker 2: are going on. I have the same model in the world, right.
Speaker 2: You know, if I if I'm walking at night between
Speaker 2: my bed and the bathroom and it's pitch black, I
Speaker 2: still have the same model in the house. I still
Speaker 2: know where the door is going to be and everything else.
Speaker 2: You know, if I can do with touch for the vision. Right,
Speaker 2: So there isn't a central model that says, here's a
Speaker 2: model of my house of touch and vision hearing. It
Speaker 2: says all these independent models. Right.
Speaker 1: And now the reason you called your book a thousand brains,
Speaker 1: we call this ypods.
Speaker 2: It's one thousand brains theary theory, right, is.
Speaker 1: Precisely because you've got all these cortical calls and they're
Speaker 1: each making a little model of the world, and they're
Speaker 1: all talking to one another. Right, So you know, hi,
Speaker 1: this is this feels like coffee cup. This looks like
Speaker 1: coffee cup. It sounds like coffee cup when it plays down.
Speaker 1: This is the temperature of coffee cup, and so and
Speaker 1: so these are all talking with one another.
Speaker 2: So so the reason they call the thousand brains. Is
Speaker 2: that each cortical column is doing what the entire brain
Speaker 2: is doing. Right, each quarter column is a senior model
Speaker 2: learning system. And and when we ask where is a
Speaker 2: model of something? We've been talking about this, where is
Speaker 2: the model of the skull or the microphone or whatever.
Speaker 2: So many things we know, where is that model? It's not,
Speaker 2: it's it's in many different places. So there's a thousand
Speaker 2: models of coffee cops, a thousand models. You don't perceive that,
Speaker 2: but they exist. And and so it's like it's it
Speaker 2: was really trying to capture that original idea that cortical
Speaker 2: columns are common and that and that there's all these
Speaker 2: different models out there that are different and they can
Speaker 2: vote one day, they vote to reach a consensus.
Speaker 1: Yeah, and it's certainly consistent with the idea that you know,
Speaker 1: for example, if someone is born blind and then visual
Speaker 1: cortex gets taken over by hearing and touches on, they
Speaker 1: are better at hearing in touch presuming because they just
Speaker 1: have a lot more real estates.
Speaker 2: Voted right, right, or real estate is a lot more
Speaker 2: practice too, right right. So it's amazing how flexible it is.
Speaker 1: Yes, given your model of the brain. Let's talk about
Speaker 1: AI and what you think is going on currently with
Speaker 1: LLLMS and what that is missing.
Speaker 2: Right, LMS flipp is interesting. Well, let me start with
Speaker 2: the criticism AI in general. Okay, AI has always been
Speaker 2: focused on what they call benchmarks, like how well can
Speaker 2: you solve problems? So, how well came this system recognized images?
Speaker 2: How well can play chess? How well can play go?
Speaker 2: How well I can translate from one language to the other.
Speaker 2: And you have all these benchmarks, and everyone competes against
Speaker 2: these benchmarks that they're kind of diverse all over the place.
Speaker 2: That's the wrong way to think about it. When we
Speaker 2: let's use computer as an analogy. When we say something
Speaker 2: as a computer, we don't based on what it's doing.
Speaker 2: We based on how it works. Alan Turing and John
Speaker 2: Fan Nordmann define what we now call a universal turning machine,
Speaker 2: which is like, okay, if a system has memory and
Speaker 2: a processor, and the memory has data and instructions, and
Speaker 2: you can change the instructions and change the data, it
Speaker 2: can do anything. And that is a computer. So I
Speaker 2: can say my toaster is a computer, even though it's
Speaker 2: a very limited computer, because it has one of those
Speaker 2: things inside. Right, if it was hard coded with springs
Speaker 2: and wires and stuff, it wouldn't be a computer. But
Speaker 2: because it has a little microprocessor has those definitions, it's
Speaker 2: a computer. So that's how we do it in the
Speaker 2: computer world. We say, these are the functions that it
Speaker 2: has to perform, and you can apply it to big problems,
Speaker 2: little problems, different types of problems all over the place.
Speaker 2: And AI we've been focused on this idea that oh
Speaker 2: a benchmarks, you know, and we always want to be
Speaker 2: beat some human Well, like a dog. Almost everyone who
Speaker 2: has the dog says it's intelligent, right, but doesn't have language,
Speaker 2: it doesn't play standswer, it doesn't play go. But why
Speaker 2: do we say it's intelligent because we can tell the
Speaker 2: that dog has an eternal model of the world. It's
Speaker 2: kind of like my internal the model. He knows where
Speaker 2: the door is, it knows how to get go on
Speaker 2: the walk. And so why why focus on this issue
Speaker 2: of like, well, it's not intelligent because it doesn't play
Speaker 2: go better than the best human player. So I think
Speaker 2: part of the problem was that people didn't know how
Speaker 2: brains worked, and so if you don't, what are you
Speaker 2: going to do? Right? We don't know it. We we
Speaker 2: know enough to build this stuff. So I think in
Speaker 2: the future that's what's going to be. We're going to
Speaker 2: say AI systems don't have to be like humans. They
Speaker 2: don't even have to do the same things humans do.
Speaker 2: Some of them are going to be very dedicated. It's
Speaker 2: very focused tasks, and we're going to be very broad
Speaker 2: So might be you know, engineers building space stations, all
Speaker 2: this huge rider, but they're all going to work on
Speaker 2: the same principles that biology has discovered. Today's AI doesn't
Speaker 2: work on those principles, you know, most of it. If
Speaker 2: you talk about the large language models, these are transform
Speaker 2: well models. We feed in a string of tokens basically
Speaker 2: words or wordlike things, and it just learns the structure
Speaker 2: of that string, and it's very good at what it does.
Speaker 2: But there's no inherent knowledge of the actual the world.
Speaker 2: It doesn't have a three dimensional models of the world.
Speaker 2: It doesn't if someone's written about it, it'll tell you
Speaker 2: about it, but it can't experience it itself. So you
Speaker 2: couldn't send in one of these AI systems down the
Speaker 2: space and say, you know, go to Mars explore and
Speaker 2: see what's out there that we can build things with
Speaker 2: and here's some tools and start building a structure. It
Speaker 2: just no why they're gonna do this though it's not
Speaker 2: gonna happen, but to contact. The tools we're working on
Speaker 2: can do that. That's what humans do, and that's the
Speaker 2: promise of AI. It's not just you know, targeting things
Speaker 2: that humans can do, like high level things like you know,
Speaker 2: translating language or writing poems or things like that. It's
Speaker 2: really how do you build the system to understands the
Speaker 2: world and knows how to act in that world. And
Speaker 2: that's the key.
Speaker 1: One of the things you wrote in your book that
Speaker 1: I thought was great was, uh, you address this issue
Speaker 1: of the existential threat of AI that a lot of
Speaker 1: people are banging on about and you don't think it's
Speaker 1: a threat.
Speaker 2: I don't think it's a threat. I mean you have to,
Speaker 2: you have to tease it apart because so many people
Speaker 2: like there's different existential threats, but you know that I
Speaker 2: one is called the alignment problem, Like all these AI
Speaker 2: agents are gonna you know, you're gonna tell what to do,
Speaker 2: but it won't be aligned with our values. And I'm
Speaker 2: just saying they don't have any values and it's just
Speaker 2: so far from reality. I just if once you understand
Speaker 2: how brains work, you said, like I'm doing any of
Speaker 2: that stuff. It's it's hard to it's hard for me
Speaker 2: to give a succinct answer to this. But I don't
Speaker 2: think that today's AI systems have any of these problems.
Speaker 2: They're not gonna run away. They're not They're not gonna
Speaker 2: have their own desires. They're not gonna say, hey, I'm awake,
Speaker 2: I need to survive, you know.
Speaker 1: Because because these current large language models are just statistical
Speaker 1: parrots that are taking much language and spinning language back out.
Speaker 2: Right, and you can apply they'll prime the robotics and
Speaker 2: other things, but they're gonna be still so statistical parents exactly.
Speaker 2: But and and by the way, they lack the human brain.
Speaker 2: We talked earlier, the new or corts is the biggest
Speaker 2: part of the brain, but we have a lot of
Speaker 2: other parts of the brain, and our emotional centers and
Speaker 2: how much of what makes us humans, our drives and
Speaker 2: motivations are mostly not the near cortex. Right. There are
Speaker 2: these other things. And if you provided an AI system
Speaker 2: with those other things, I might start worrying about it.
Speaker 2: But if you're just trying to model stuff, it's it's
Speaker 2: no threat. It's we just assume that some AI system,
Speaker 2: because it can spew back language, is going to think
Speaker 2: like us and be like us and have our same motivations.
Speaker 2: Nothing like it at all.
Speaker 1: So tell us about the thousand brains projects and how
Speaker 1: you're gonna make this happen.
Speaker 2: Right, So, we kind of been working on this theory
Speaker 2: for decades really, and maybe five or six years ago
Speaker 2: we really had some breakthroughs and sort of all came
Speaker 2: together and then we said, well, I always thought that
Speaker 2: this is the way we're going to build tuly intelligent machines.
Speaker 2: And this is at the same time as deep learning
Speaker 2: and transformers are taken off and all this excitement about it.
Speaker 2: But that didn't distract us. We said, Okay, let's see
Speaker 2: if we can start building this stuff. So we for
Speaker 2: a couple of years we had a small team that
Speaker 2: was trying to implement the thousand brains theory, modeling quarter
Speaker 2: u cooms, the voting, all this stuff, multisensories things, all
Speaker 2: this stuff, were modeling it, and we decided earlier this
Speaker 2: year that the best way to go forward was to
Speaker 2: do this in an open source project. We haven't actually
Speaker 2: told people we're doing this before. So we've created a
Speaker 2: thousand Brains project. We're taking all of our our code
Speaker 2: and putting an open source We are taking the patterns.
Speaker 2: We have a lot of patents. We're going to make
Speaker 2: a non a short clause. We've done that. I'm not
Speaker 2: a short close on our patents. We have. We hire
Speaker 2: a team of people to like open source project manager
Speaker 2: for the outside people. We've already got quite a few
Speaker 2: people interested. We already have received some funding from the
Speaker 2: Gates Foundation for this significant funding and help fund the
Speaker 2: project for a couple of years. We have there's a
Speaker 2: guy named John hen at Connegie Mellen University who's building
Speaker 2: silicon to implement quartical columns. So there's the people around
Speaker 2: the world who've been excited about our work and following
Speaker 2: it and want to join in on this day. So
Speaker 2: we figured let's get them all together, let's build a
Speaker 2: framework open source project. And so we built out this team.
Speaker 2: We have just been run by the women Vision Clay,
Speaker 2: who's just brilliant, and technical side is by Dunam Neils
Speaker 2: and and so we're just starting this, you know. So
Speaker 2: we actually haven't officially and we've talked about it, but
Speaker 2: we haven't officially launched yet because not everything is open
Speaker 2: yet and we have to there's a lot of stuff
Speaker 2: you have to do to put in to get those
Speaker 2: whole do work. But we're going full boring this and
Speaker 2: I think my hope is that anyone who's excited about
Speaker 2: the work, and there's quite a few people can help
Speaker 2: join us and work on this and propel it forward
Speaker 2: and really created what I not only just an alternate
Speaker 2: form of AI centory motor AI based on brain principles,
Speaker 2: but I think what's going to be actually the ultimate,
Speaker 2: uh primary source of AI, which is brain modeling a
Speaker 2: thousand brains projects. This is amazing. So how do people
Speaker 2: get involved in this?
Speaker 1: Uh?
Speaker 2: You can just go to our website dementa and dot
Speaker 2: com and you know, there's a lot of information already,
Speaker 2: a tons of information. It's like we have all the
Speaker 2: stuff with accumulated documentation, code, videos, were all that up there,
Speaker 2: plus tutorials and so on. So you just you can
Speaker 2: just you can you go to our nomenta dot com.
Speaker 2: It'll be obvious how to sign up to be informed
Speaker 2: of what's going on or how to get involved.
Speaker 1: Great, So some listener to this podcast is I want
Speaker 1: to get involved and understand more about this thing. Go
Speaker 1: to Nementa dot com and they can.
Speaker 2: They can. First of all, they'll start, they'll sign up
Speaker 2: to getting notified about things are happening. They can get
Speaker 2: educated on the whole project. They can I don't think
Speaker 2: right yet they can contribute code yet, but that will
Speaker 2: happen within a month or so. It'll be obvious how
Speaker 2: to get started. There's there's a lot of information to learn.
Speaker 2: I would think if you haven't. If you haven't, you
Speaker 2: might want to start with just by reading the book
Speaker 2: to the Thousand Brains, because it gives you that not
Speaker 2: only the basics of the theory, but it also gives
Speaker 2: you the vision about how this is going to play
Speaker 2: out over time.
Speaker 1: And so the idea is just so I'm straight on this.
Speaker 1: So the idea is a person can download the code
Speaker 1: and run this model.
Speaker 2: Right the first we're making said that you can do that.
Speaker 2: You can run our current experiments, you can recreate them,
Speaker 2: you can apply them different ways. Great.
Speaker 1: So something that you and I have in common that
Speaker 1: we are obsessed about is this idea that we're living
Speaker 1: inside our own internal models. This is all a construction.
Speaker 1: And you had a line in the book that I loved,
Speaker 1: which is that if you had different sensors for picking
Speaker 1: up different information in the world, we would have a
Speaker 1: different perceptual experience, a completely different experience of the universe.
Speaker 2: Well maybe completely, not completely, Like a blind person is
Speaker 2: we're learning the worth of touch, and a person who
Speaker 2: is deaferent, a person maybe who has sensory problems on
Speaker 2: his hand, they will end up with a similar structure.
Speaker 1: Sorry, but what I mean is not in terms of
Speaker 1: how we pick up on the visible light ratee. But
Speaker 1: I pick up on infrared and you pick up on
Speaker 1: radio waves.
Speaker 2: Okay, right, you might if you if you really did that,
Speaker 2: then you would have a different view of the world,
Speaker 2: like it's often you know, like it take the issue
Speaker 2: of color. It's often said that bees, you know, seeing
Speaker 2: the ultra violet and we don't. So what looks like
Speaker 2: toss is a white flower to them? Is this beautifully
Speaker 2: colorful variegated flower.
Speaker 1: But let's say you saw a totally different part of
Speaker 1: the electromagic spectrum and so you see in the microwave range.
Speaker 1: Question is would would we have color at all?
Speaker 2: I don't know, It's hard to say, right, there's a
Speaker 2: there's an underlying really interesting philosophical problem called qualitia, which
Speaker 2: is like, why does color feel like color? Right? And
Speaker 2: it doesn't feel like sounds or tactile sensations. And it's
Speaker 2: an interesting challenge to understand that. I've written about it
Speaker 2: a bit.
Speaker 1: Yeah, do you have a hypothesis about this, I'll tell
Speaker 1: you what mine is, But it is it's always sort
Speaker 1: of half one, which is I think it's about the
Speaker 1: structure of the data coming in defines the quality.
Speaker 2: I don't know why or how that's true.
Speaker 1: But you know, with the eyes, you've got two two
Speaker 1: dimensional sheets of data coming in, and so vision feels
Speaker 1: like something with hearing, it's a one dimensional signals just
Speaker 1: going up and down and vibringing your ear drum. That
Speaker 1: feels like something. You don't confuse vision with hearing. That's
Speaker 1: like completely different worlds to you. My interest has been
Speaker 1: in what happens when we feed news structures. We've done
Speaker 1: a lot of interesting stuff in this area.
Speaker 2: Exactly would you have a completely new quality? Is it possible? So?
Speaker 2: I mean, certainly you can imagine. First of all, I
Speaker 2: agree with you again, it's all spikes, right, So there's
Speaker 2: nothing there's no color spikes, there's no heat, spikes. It's
Speaker 2: just spikes. And so obviously the different quality it has
Speaker 2: to come about somehow from the structure of the data
Speaker 2: spatially and temporarily, and also sensory motory, you know, it's
Speaker 2: like how things change as you move, and I think
Speaker 2: that's a big part of it. So I agree with
Speaker 2: on a fundamental level that it has to be some
Speaker 2: in the data and it's nothing else. And we can
Speaker 2: then ask ourselves something like, well, imagine you've been blind
Speaker 2: your whole life. You don't have a sense of color.
Speaker 2: You've never experienced color, and so to you would be
Speaker 2: kind of mysterious things. Someone can say, well, can't you
Speaker 2: tell that's that's you know, that's this type of orange
Speaker 2: and that time? What are you talking about? Right? They'd
Speaker 2: have to accept that you have some super sense and
Speaker 2: the world looks different to you because you have vision
Speaker 2: and I don't. And they may be able to touch
Speaker 2: things that you know, sense things that I don't. Sign,
Speaker 2: So I could be able to try to read braille
Speaker 2: if you're not a braille reader, that feels like what
Speaker 2: the stuff? It's a blur? Right? So they oh, no,
Speaker 2: I feel everything there, right, So we can we can
Speaker 2: just ask ourselves a questions like what's the world like
Speaker 2: to different people, and sometimes we'll end up with a
Speaker 2: similar model, like yeah, well you and I would have
Speaker 2: no matter what censors you have, we'd have the model
Speaker 2: of physical structure of a coffee cup. But other times
Speaker 2: it could be quite different, you know, and certain if
Speaker 2: you start like sensing parts of the radio spectrum or
Speaker 2: other things, just be you know. One of the things
Speaker 2: I always wondered, like, what would be like if you
Speaker 2: had if you had smell sensors stand under your fingers, right,
Speaker 2: and then everything you touch. Well, we kind of we have.
Speaker 2: We have temperature sensors, and we have tapping with all
Speaker 2: kinds of But what I could smell like you could
Speaker 2: tell chemicals that were on the surface of objects. This
Speaker 2: is what dogs do. You know. Dogs they don't just smell.
Speaker 2: They stick their nose right on the thing and they smell.
Speaker 2: They moved to the next bot it smell. Dogs build
Speaker 2: this freedom, actual structure of smells. We don't have that
Speaker 2: smell for us. It's kind of like wasting in from
Speaker 2: some direction, right. Dogs have this incredible model of the
Speaker 2: world smell mod and it's hard to imagine what it is.
Speaker 2: But I'm sure they have it, so I think it's
Speaker 2: fun to think about these things. I don't you know,
Speaker 2: in the future will build machines that perceive the world
Speaker 2: different than we do. But that'll be great.
Speaker 1: Yeah, Okay, Jeff, this has been wonderful.
Speaker 2: Thank you for being here too. Thanks David. It's always
Speaker 2: great talking to you and I enjoy it. It's a
Speaker 2: lot of fun we were and I love your podcast.
Speaker 1: So that was Jeff Hawkins, theoretician and author of A
Speaker 1: Thousand Brains.
Speaker 2: Now.
Speaker 1: I love his model because it builds on previous research
Speaker 1: and gives us a possible starting point for how this
Speaker 1: whole system might be working. This is a view of
Speaker 1: the brain in which you don't have just a single
Speaker 1: model of the world being constructed, but hundreds of thousands
Speaker 1: of little models, each viewing the world through their little straw.
Speaker 1: And these models are independent, but they're not completely independent,
Speaker 1: so they communicate with each other and they vote, and
Speaker 1: in this way, the whole system converges on its best
Speaker 1: guess of what's going on out there in the world.
Speaker 1: And by this mechanism we construct a full three dimensional
Speaker 1: representation of the environment around us, with its sites and
Speaker 1: sounds and three dimensional structure. So this gives us a
Speaker 1: clear framework for thinking about the neocortex. Now, we might
Speaker 1: not know, oh for a while, if this answers everything,
Speaker 1: or it needs some tweaking, or if there are far
Speaker 1: better models coming down the pike. But what I absolutely
Speaker 1: love about this is that this is where the endeavor
Speaker 1: of science shines. Taking something that seems insanely complex, eighty
Speaker 1: six billion neurons with two hundred trillion connections, something of
Speaker 1: such vast complexity that it bankrupts our language, and saying, wait,
Speaker 1: what if there's a really simple principle at work here?
Speaker 1: What if there's a way that we could reduce all
Speaker 1: that complexity by just looking at this from a new angle.
Speaker 1: So let me give an analogy here. Just think about
Speaker 1: what it would be like if you had a magical
Speaker 1: microscope with which you could look into a cell and
Speaker 1: into the nucleus in the middle. What you would see
Speaker 1: is mind boggling complexity. There. You'd see millions or billions
Speaker 1: of molecules racing around and interacting and doing god knows what,
Speaker 1: and you'd say, wow, there's no way.
Speaker 2: We're ever going to understand this.
Speaker 1: But then Krick and Watson come along and say, actually,
Speaker 1: the important thing is this DNA molecule and keeping the
Speaker 1: order of these base.
Speaker 2: Pairs, and all the rest is housekeeping.
Speaker 1: And suddenly the fog of confusion lifts. Now something that
Speaker 1: seemed well beyond us can be described in a sentence
Speaker 1: or two, and science leaps forward and things move fast
Speaker 1: from there. I worked with Francis Crik when I was
Speaker 1: in my postdoctoral years, and now I look around me
Speaker 1: at Stanford and Silicon Valley, and there are thousands of
Speaker 1: laboratories and companies doing amazing work with genomes, and their
Speaker 1: existence results entirely from this one simplifying insight about DNA
Speaker 1: in nineteen fifty three, that new model that suddenly clarified
Speaker 1: what what is happening inside the nucleus. By the same token,
Speaker 1: this is what we're trying to do with the brain.
Speaker 1: Brains appear to be ferociously complex, and yet we have
Speaker 1: lots of brains running around the planet.
Speaker 2: We've got eight point two billion of them.
Speaker 1: So something must be straightforward about their architecture, or else
Speaker 1: Mother Nature wouldn't be able to build these over and
Speaker 1: over with such reliability. You couldn't drop this massive quantity
Speaker 1: into the world and have them all functioning well unless
Speaker 1: there was something pretty uncomplicated about building and running a brain.
Speaker 1: So that is the overarching game of science to take
Speaker 1: the overwhelming complexity around us and to find new angles
Speaker 1: to look at things to reveal simplicity. Go to eagleman
Speaker 1: dot com slash podcast for more information and find further reading.
Speaker 1: Send me an email at podcasts at eagleman dot com
Speaker 1: with questions or discussion, and check out and subscribe to
Speaker 1: Inner Cosmos on YouTube for videos of each episode and
Speaker 1: to leave comments until next time. I'm David Eagleman, and
Speaker 1: this is Inner Cosmos.