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is special about the wrinkly outer layer

3:09

of the brain, the cortex? And what

3:11

does this have to do with the

3:13

way that you come to explore and

3:16

understand the world? And by the way,

3:18

why do you see a whole image

3:20

when you open your eyes even though

3:22

each part of your visual cortex has

3:25

access to only a tiny bit of

3:27

the image? And for that

3:29

matter, the brain is divided into different areas

3:31

for sight and sound and touch and so

3:33

on. And so why when you

3:36

are petting a cat, why does the

3:38

cat seem unified? Why doesn't

3:40

the sight of the cat seem

3:42

separate from the purring and the

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feel of the fur? Can

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we build a new model of how the

3:49

brain works? And in what

3:51

ways is what the brain doing

3:53

something very different than what's happening

3:55

in current AI? of

6:01

our cortex. We humans have a ton

6:03

of this stuff. So take

6:06

four pieces of paper from your printer

6:09

and place them next to each other

6:11

to make one really large piece. That's

6:13

how much cortex a human has if

6:15

you were to spread out the wrinkles.

6:18

Now our nearest cousins, the great apes,

6:20

only have about one piece of paper

6:22

worth and most mammals have a lot

6:24

less than that. So something about

6:26

the story of the runaway human

6:28

success has to do with the

6:31

fact that we have way more

6:33

cortex for our body size than

6:35

any other creature. And

6:37

side note, I'm really talking about

6:40

what's called the neocortex or new

6:42

cortex because we also have a

6:44

little bit of paleocortex or old

6:46

cortex, but the thing that really

6:48

makes us outstanding is the amount

6:50

of neocortex that we have. But

6:53

what is this neocortex doing?

6:56

Well, if you look at any neuroscience

6:58

textbook, you'll see that this part of

7:01

the brain, the cortex is often drawn

7:03

with different colored regions like this red

7:05

region over here is devoted to vision

7:07

and this green one is devoted to

7:10

hearing and this yellow one to touch

7:12

and so on. But something I've been

7:14

obsessed with and write about in my

7:17

latest book, LiveWired, is that this is

7:19

the wrong way to think about it

7:21

because the neocortex is remarkably flexible. It's

7:23

not a fixed map. If

7:25

you are born blind, the part of

7:27

your cortex that we would have thought

7:30

of as visual cortex gets taken over

7:32

by hearing and touch and so on.

7:35

Now let me just be really clear what

7:37

I mean by taken over. The neurons there

7:39

are the same. The cortex

7:41

looks exactly the same from the outside,

7:43

but the function of those particular neurons

7:45

is now not visual. They

7:48

have nothing to do with visual information

7:50

anymore. Now that same neuron,

7:52

instead of firing when

7:54

it detects a moving object,

7:56

now it responds to a

7:58

touch your toe or

8:01

hearing a B flat note or

8:03

whatever. So the little

8:05

labels that we draw onto the brain,

8:08

these maps that we impose, these

8:10

are actually massively flexible. And as you

8:13

may know, I gave a talk at

8:15

Ted about this a while ago, where

8:17

I showed that you can feed in

8:19

new kinds of information, let's say through

8:21

the ears or the skin, and the

8:24

brain will figure out how to deal

8:26

with that data. It will flexibly devote

8:28

part of its cortical real estate to

8:30

that. And

8:32

this line of thinking led some scientists

8:35

like Vernon Mount Castle some decades ago

8:37

to realize that the cells of the

8:40

cortex are a one trick

8:42

pony. No neuron is

8:44

inherently a visual neuron

8:47

or a neuron devoted to hearing

8:49

or touch or smell or taste

8:51

or memory or whatever. All

8:53

parts of the cortex are perfectly

8:55

capable and willing to take on

8:57

any job. So that

9:00

suggests they're all running some sort of

9:02

basic algorithm. And it doesn't matter what

9:04

kind of data you feed in different

9:06

parts of the cortex. We'll say, cool,

9:09

I'll build a representation of that data.

9:11

I don't care if it comes from

9:13

photons or air compression waves or temperature

9:15

or whatever. I'm

9:17

on the job here to build

9:20

an understanding of whatever is coming

9:22

in locally. Now,

9:24

it's not individual neurons that are

9:26

building models, but instead groups

9:29

of many tens of thousands of

9:31

neurons arranged in a

9:33

six layered cylinder. So think about

9:35

this like you're a geologist

9:37

and you drilled out a cylinder of

9:40

rock and you saw

9:42

six layers in it, six sedimentary

9:44

layers. That's what the neocortex looks

9:46

like six layers. And it's built

9:49

out of these columns, which have

9:51

the same types of neurons with

9:53

the same connection patterns in each

9:55

column. And so think

9:57

about the cortex as being made. of

10:00

lots of these columns, like taking hundreds

10:02

of thousands of grains of rice and standing them

10:04

up on their end and packing them all next

10:07

to each other. People

10:09

have known about cortical columns for many

10:11

decades since Vernon Mount Castle first discovered

10:13

these in 1957. But

10:16

recently, someone has pulled together several

10:19

different threads to propose

10:21

how this could underlie what

10:23

the cortex is all about.

10:26

And that someone is Jeff Hawkins and

10:28

his team. And so I

10:30

met with Jeff in my studio. Now,

10:32

Jeff is one of my favorite people

10:34

because he does theoretical neuroscience. He really

10:36

tries to figure out the big picture

10:38

of what the brain is

10:41

doing. Now, Jeff has a very interesting

10:43

history. So I'll just mention that in

10:45

the 1980s, he was a graduate student

10:47

at Berkeley where he proposed a PhD

10:49

thesis on a new theory of the

10:52

cortex. But his proposal was rejected.

10:54

And so he ended up pursuing

10:57

his vision for mobile computing instead.

10:59

And in 1992, he launched the

11:01

company Palm, which made the Palm

11:04

pilot. If you remember that, this

11:06

was this little handheld device and

11:08

you could write on it with

11:10

a stylus and it would translate

11:12

your handwriting into text. And

11:14

you can use this for your address book and your

11:17

calendar and your contacts and note taking. This

11:19

was the first entrant into the world

11:21

of portable computing. It really changed the

11:23

world. Anyhow, a decade

11:26

later, Jeff returned to his original

11:28

love, which was theoretical neuroscience, trying

11:30

to figure out what's going

11:32

on with the brain. And

11:35

he wrote a book in 2004 called

11:37

on intelligence, which was very influential

11:39

on me and lots of other

11:41

thinkers I know. So I was

11:43

very excited when Jeff recently came

11:45

out with his next book that

11:47

represents his last decade and a

11:49

half of research. It's called a

11:51

thousand brains, a new theory of

11:53

intelligence, and it describes his framework

11:55

for thinking about the brain. So

11:58

without further ado, let's dive into.

12:00

a very cool new model of

12:02

the brain. Okay,

12:07

Jeff, so you are a theoretician. You think about

12:10

the brain from a high level. We're in

12:12

this era now of AI, where

12:14

AI is doing all kinds of things that

12:16

are amazing and no one expected, but you

12:18

see the brain as being very different from

12:20

what is going on with, let's say, large

12:22

language models. So tell us about that. That's

12:24

absolutely true. You know,

12:27

the current AI wave is really amazing, but

12:29

those models don't work at all like the

12:31

brain. And I think you

12:33

could start with one really fundamental difference.

12:36

Brains work through movement. We

12:39

move our bodies through the world. We move our

12:41

hands over objects to touch and learn what they

12:43

are. We move our eyes constantly. So

12:45

the inputs of the brain are constantly changing, mostly

12:47

because we're moving through the world. And

12:50

the term for that is a sensorimotor system. And

12:53

the brain can't understand its inputs unless it

12:55

knows how it's moving through the world. So

12:57

we learned by exploring, by moving

12:59

different places, picking things up, touching them,

13:01

and so on. And that's

13:03

all animals that move in the

13:06

world learn this way. So this idea that the

13:08

brain is a sensorimotor system has been known back

13:10

in the late 1800s, but

13:12

it's pretty much ignored by everybody. But

13:15

it leads to a very fundamental different

13:17

way of how we acquire knowledge and

13:19

how knowledge is represented in the brain.

13:22

Whereas today's AI most

13:25

of it's built on deep learning

13:27

or transformer technologies, which essentially we feed

13:29

it to it. It

13:31

doesn't explore it. And we feed to

13:34

large language models, we just feed it language.

13:36

So there's no inherent knowledge about what these

13:38

words mean, only what these words mean in

13:40

the context of other words. But

13:42

you and I can pick up a

13:44

cat and touch it and feel it and know

13:46

that it's warmth and we understand how its body

13:48

moves because no one has to tell us that.

13:51

We just experience it directly. So

13:54

this is a huge gap between

13:57

brains, pretty much all brains work by

13:59

sensorimotor learning. long

20:00

and around. And as you do, you build a

20:02

three-dimensional model of the cup, even though you're only

20:04

getting input from one fingertip. The

20:06

eyes are doing the same thing. It's

20:08

surprising. You don't realize this. So every

20:10

cortical column we understand now is doing

20:13

this sort of processing movement information and

20:15

sensor information, building what we call structure

20:17

or 3D models of things in the

20:19

world. So it's quite different than even

20:21

neuroscientists think about it. And

20:24

there's a lot of reasons we can talk about

20:26

how it was missed for all these years. So

20:28

in the cortex, you have essentially six layers of

20:30

cells. And a column is...

20:33

All six layers. Is all six layers. It's going

20:35

up and down. It's

20:38

like, think of it like layers of a cake. Right. Column

20:40

is you're taking a straw and

20:42

shoving it through the top. And so you've got this Good

20:44

analysis. This is the call. Okay. You

20:46

got a straw of cake. Okay,

20:48

great. And so the idea is

20:50

if you're looking at some column

20:52

in primary visual cortex,

20:55

yeah, your point, Jeff, was that it's

20:58

like looking at the world

21:00

through a straw. It only sees a little

21:02

tiny piece of the world, but because the

21:05

eyes are moving out, because you're exploring the

21:07

world, this is actually getting lots

21:09

of parts of information. It's exploring the world in

21:11

the same way that your fingertip explores the world.

21:13

Right. And it has to integrate information over time.

21:15

That's the key, right? And you can literally do

21:17

this. You can look at the world through a

21:19

straw. And you

21:21

can say, oh, what am I looking at? Well, you can't

21:23

tell until you start moving the straw. And then

21:26

you can start and you can also learn objects

21:28

that way. So literally, you can learn by looking

21:30

through a straw, which is what sort of one

21:32

column is doing. And

21:34

in your model, there are

21:37

thousands of such columns. And

21:39

each one of these is

21:41

learning a

21:43

model of the world as it's going. So tell

21:45

us about it. Right, right. So figure out what

21:47

it is and what it's doing. So

21:49

the trick of this thing is, it's a little tricky

21:51

here. You know, when you look out at the world,

21:53

you have a sense, anybody, you have

21:55

a sense where things are. I have a sense where you

21:57

are relative to me. I have a sense for this microphone.

22:00

to me, I know where my hand is, relative to this

22:02

cop. Now, it turns out

22:04

that you have any kind of sense of

22:06

location and space, you have to have neurons

22:08

representing it. There's nothing goes on

22:10

in the brain if there aren't neurons firing doing

22:12

it. It turns out most of

22:14

the machinery in the neocortex is keeping track

22:16

of where things are relative to other things.

22:19

So those six layers, all those cells, at

22:21

least half of that circuitry is

22:24

tracking where the sensory input is

22:26

coming from in the world. So if I move

22:28

my finger over this coffee cup, the

22:30

part that's getting information from my sensory, like

22:33

I'm sensing an edge, for example, as

22:35

I move my finger, it has to keep

22:37

track of where my finger is, a location

22:39

of it and its orientation relative to this

22:41

cup. It's quite complicated. But

22:44

that's what it has to do to build those models. And

22:46

now we know how it does it. There's all this evidence

22:48

for it. So the brain

22:50

is just trying to keep track of where all of

22:53

its inputs are in the world, all relative to other

22:55

things. Then it builds up these three dimensional models of

22:57

the world. So tell us about how it does that

22:59

then. Right. So you can think

23:01

about when you're in high school, you learned about Cartesian

23:03

coordinates, x, y, z coordinates, right? And

23:05

so if I wanted to say, where is something, where are

23:08

you relative to me? I might say, okay, your nose is

23:10

the origin. I could say it's some distance from here. And

23:12

so, you know, x, y, and z. Well,

23:14

you have to have something like that. But

23:17

brains don't do it that way. They do it another way. And

23:20

this was some very clever research in

23:22

the last 20 years that people discovered in

23:25

the antirhinal cortex and hippocampus, these cells

23:27

called grid cells and play cells, which

23:29

actually operate as reference frames. They are

23:32

a way of neurons to

23:34

represent locations. And

23:36

they work differently than x, y, and z. So

23:38

there's no origin. It's kind of really clever how

23:41

they work. Nature

23:43

has discovered a different way of doing this. So make

23:45

sure you tell us a little bit about that. Well,

23:47

okay. But these are well-known things.

23:49

Like grid cells, which are antirhinal cortex. What

23:51

they do is they, these cells, if you

23:53

take a set of them, individual cells are

23:56

not unique. And these real cells,

23:58

I fire at different locations in space. But if

24:00

you take a set of them, they're unique. And

24:02

so you can encode a unique location in space.

24:05

And the key thing about them is

24:07

these cells automatically update as

24:10

you move. So the original grid cells are where your

24:12

body is in a room. And

24:14

as you move, it's called path integration. It says,

24:17

OK, you're moving at this direction,

24:19

at this speed. So we'll just automatically update these

24:21

neurons as if we know where you are, right?

24:24

And so it's what sales used to do,

24:26

dead reckoning. You just say, oh, I'm heading

24:29

north for an hour or three knots there

24:31

for all these three miles in

24:33

this direction. So we know that these

24:35

cells exist. They've been well studied. People

24:37

wouldn't know about prize for these things. So

24:40

we speculated that the same

24:42

neural mechanisms, these grid cells

24:45

and equivalents, would be in the cortex in every cortical

24:47

column. And sure enough, they're

24:49

finding that now. So there's all

24:51

kinds of research now. They're finding in humans and

24:53

other animals that there are grid cell-like structures in

24:55

cortical columns. And so what does that tell you?

24:58

It tells me that that's the mechanism by which

25:00

the brain uses for reference frames. And so literally,

25:02

when you build a model of something in the

25:04

world, like a model of a cup or a

25:06

model of anything, essentially what

25:08

you're doing is you're saying, here's the sensation,

25:10

and here it's location. Here's another sensation at

25:12

a different location. Here's another sensation at a

25:14

different location. You add all these together, and

25:16

you get a three-dimensional model. You

25:18

can say, this thing consists of these features

25:20

in these locations relative to each other. And

25:24

so literally in our head, we build

25:26

models of the world that are three-dimensional

25:28

analogues of the physical things we

25:31

interact with. And that's

25:33

why you appear three-dimensional to me. You're

25:35

not an image. You're a three-dimensional structure, because I have

25:37

a three-dimensional model of humans and I have a special

25:39

model for you, David. OK,

25:43

great. India.

25:53

Let's post in the outfit of the day. We

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look so good. Yeah, we do, but is that

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