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at shopify.com/profits23. The

0:33

Economist So we've

0:35

just arrived at an

0:38

industrial estate outside of

0:41

Manchester. We're

0:43

looking at a sort of low-grey industrial building with

0:47

some huge tanks outside. Looks

0:49

very unassuming from the outside, but

0:52

there's something pretty special going on inside.

0:54

Ainslie Johnson is a very famous economist.

0:57

Pretty special going on inside. Ainslie

0:59

Johnston is a data journalist and science

1:01

correspondent for The Economist. Hello. Hello, Ainslie.

1:03

Hi, good to meet you. Hi, it's

1:05

very nice to meet you. Hi,

1:08

I'm Steve. Welcome to the UK Biobank

1:10

Imaging Centre. She recently

1:12

went to visit a brain imaging lab in

1:14

the north of England. The

1:17

UK Biobank imaging study went up

1:19

with each participant contributing about 9,000

1:21

images. Dawood

1:23

Dassu is the head of imaging

1:25

operations at UK Biobank. These

1:27

are things that tell you about the size,

1:30

volume, the structure of the brain, but

1:32

also tells you about brain function as

1:34

well. So which parts of the brain

1:37

are active during certain tasks. And

1:39

we also have something which gives us a

1:41

measure of flow of blood in

1:43

key parts of the brain as

1:45

well. So each participant contributes just

1:47

from brain around two and a

1:50

half thousand variables to the dataset

1:52

that we upload for researchers to

1:54

use. The UK Biobank

1:56

Maintains a huge database of

1:58

biomedical data. It collects... everything

2:00

from genome sequences to information

2:02

on people's diets. The. Imaging

2:05

study that Darwin is talking about

2:07

here aims to scan everything from

2:09

the hearts to the bones and

2:11

absence of all the participants. Their

2:14

stance will help scientists delve into

2:16

the intricacies of one of the

2:19

most complicated objects in the entire

2:21

universe. The. Human brain. Well,

2:25

participants light inside of them are

2:28

ice gonna take. If it's it's

2:30

it's tough to do to stop

2:32

trying to get three images. three

2:34

cases of to match the top.

2:36

Phase two either the last or

2:39

the was inside and button in

2:41

my layman's terms lights up the

2:43

pause for the brain that were

2:45

involved in decision making. The compare

2:48

that to what was happening earlier

2:50

run when the same Magnus Feals

2:52

were being applied for those noticed.

2:58

We all know that human

3:00

brains a remarkable somehow from

3:02

a tangle of billions of

3:04

brain cells and a super

3:06

chemical reactions emerges a vast

3:08

range of skills: language, memory,

3:10

vision, the ability to process

3:12

information that he be control

3:14

muscles and much much more.

3:17

And the some is much greater

3:19

than the parts, because human brains

3:22

are also the center of what

3:24

we call intelligence. Human

3:26

intelligence has driven the success of

3:29

our species, which perhaps makes it

3:31

odd that we still have so

3:33

much to learn about what human

3:36

intelligence in fact, any intelligence actually

3:38

is. But. Understanding

3:40

human intelligence has to be the

3:43

starting point if you want to

3:45

understand the artificial time to. That's

3:48

our goal and this special four

3:50

part series on the science that

3:53

built the I revolution. I'm

3:57

Anna charm issues babish for the

3:59

Economist. He didn't show. Will

4:01

look at the very earliest ai systems

4:03

and how they took inspiration from the

4:05

human brain. This. Is

4:07

the first a full episodes in

4:10

which will examine the scientific ideas

4:12

and innovations that have led to

4:14

the current moments in? Will.

4:17

Gonna get behind the hype buzzwords and

4:19

jargon and explore eight ideas that we

4:21

think you need to know if you

4:24

want to understand how the generous is

4:26

A I have today came to be.

4:30

That explore what artificial neural networks

4:32

we love. The. Nerve Cell Look

4:34

Up Up Up Up Up Up Up

4:36

Up Up Up Up Us When we

4:39

come to think about new really inspired

4:41

artificial systems, the fact that it's sending

4:43

a pulsar isn't is the critical insight

4:45

the gives us all the information processing

4:47

power that we use Now from the

4:49

earliest attempts to model the human brain

4:52

in silicon, the since we were building

4:54

were sued them and so weak and

4:56

so difficult to train to the technologies

4:58

that enable both models to be scaled

5:00

up image. That was the turning point.

5:03

Of a eyes history Recognizing

5:05

how critical it is to

5:07

use big data. Will

5:09

he was finally around a

5:11

decade ago a I got

5:13

astonishingly good. All of a

5:15

sudden things are working and people pay

5:17

attention to what we do. We have

5:20

a number of examples where a computer

5:22

vision systems can beat human experts at

5:24

that on game. On how

5:26

those systems just kept on

5:28

getting better. The. Change. From

5:30

say Gp T to to Gp to

5:32

three was huge. The change from Tpg

5:34

three to Tpp for was huge. I

5:37

do not think the large language. Models would

5:39

work as well as they are. I just thought

5:41

I kids just throw the whole internet at its

5:43

and be able to get next word predictions that

5:45

have that seem like a human you know on

5:47

our dead wrong. You. Can. If

5:52

you want to understand the origins

5:54

of artificial intelligence, it's best to

5:56

start with the second of those

5:58

words. Though our

6:01

first question in this

6:03

series is this: what

6:05

is intelligence. To

6:15

figure out exactly how the human brain

6:17

works, let's pick up where we left

6:19

off with our correspondent, Ainsley Johnston. The

6:26

Uk Biobank Center just outside of

6:28

Manchester. Scans patients brains seven days

6:30

a week. And they work towards

6:33

their goal of imaging a hundred thousand people.

6:37

Steve. Garrett, the imaging program

6:39

manager explained the process the

6:41

participants undergo. We

6:44

are in the missing clinics we've

6:46

got. participants in here are coming

6:48

for around of souls five hour

6:51

visit. Are they doing tests

6:53

and things on the computers? We

6:55

have a touchscreen question i was

6:57

a gif really comprehensive analysis or

6:59

anything about health and lifestyle for

7:01

they also do the commission. Of.

7:06

Oh sure, I'll see. That

7:08

sewer nice. One of the health research

7:11

assistants at the clinic fans by of

7:13

the say that the going sailors adultery

7:15

with twenty five minute com section on

7:17

some games, puzzles and memory tests as

7:19

well so have a read in the

7:21

yellow and then when you're ready press

7:23

that smile in exports. And. Pop.

7:27

Into the game will have three pairs right.

7:30

Case I can see six cards in front of

7:32

me and that have been turned over and gotta

7:34

find a kid the some of the man in

7:36

the from as a man. In

7:40

the sun. Titan

7:45

isn't. As a game. Where.

7:51

Okay, some being asked to add the

7:54

following numbers together One. C

7:56

Three Four Five.

7:59

okay So that equals 15. If

8:04

Truda's mother's brother is Tim's

8:07

sister's father, what relation

8:09

is Truda to Tim? Truda's

8:13

mother's brother. That's

8:16

Truda's uncle. Tim's

8:18

sister's father. That's Tim's father.

8:22

Truda's uncle is Tim's father. Truda

8:27

must be his aunt, I think. Oh

8:30

god. At this point,

8:32

I'm a god master's master at pen and paper. I

8:35

think I need a pen and paper. I

8:38

feel like we've probably got enough of this and I

8:41

think I'm probably embarrassing myself. Uh

8:44

oh. These

8:51

tests are about a lot more than just making

8:53

fun of journalists though. The

8:55

scores from each of the tests help

8:57

to paint a unique picture of participants'

9:00

cognitive abilities. This is

9:02

powerful data for researchers, particularly

9:04

in combination with the biomedical data that's

9:06

about to be collected. And

9:09

then they'll go and get changed. And

9:11

after they're changed, one of them will go

9:13

for their brain scan. At

9:18

the end of a corridor full of warning

9:20

signs for strong magnetic fields is the brain

9:22

MRI machine. These

9:24

machines look like giant donuts. The

9:27

participant lies down on a bed and

9:29

then their head and shoulders are moved inside the bore

9:31

of the scanner. We

9:34

entered the control room next door. From

9:37

here, a radiographer controls the scanner,

9:40

checks the quality of the brain images that are

9:42

being collected, and makes sure that

9:44

the participant is happy and comfortable. How's

9:47

the party now? Angela

9:50

Emmons, one of the radiographers, took me through

9:52

the process. It's a half

9:54

hour scan of the brain. First

9:56

25 minutes you need to keep nice and still. then

10:00

there's a task coming up. The task

10:02

is just to look at the brain

10:05

when it's actually working. We run

10:08

an earlier sequence when

10:10

they are at rest and then just run two

10:12

minutes of that when they're undertaking a game of

10:14

snap. We show them a series of

10:16

shapes and we show them a

10:18

series of faces. Runs about two and

10:21

a half minutes and then when

10:23

that comes to an end they've got about another

10:25

two minutes left in the scanner. While

10:28

the participant's in the scanner what can you see in

10:30

the control room? Lots of images come

10:32

up, the images come up in real time. We

10:35

check the resolution, make sure

10:37

we've got good images, participants settled

10:40

and then just follow that through the

10:42

sequences. This

10:46

gives you an idea of intelligence. That's

10:49

so interesting. Can we be heard at the start of

10:51

the podcast? You can look at

10:53

how much of that variation amongst

10:55

participants is explained by genome data.

10:57

You can look at our imaging

10:59

data. You might

11:02

even be looking at history as well as

11:04

the lifestyle, job, diet and things like that.

11:06

You could look at all of that as

11:08

well and I'm sure somebody will figure out

11:10

a way of looking at all of that

11:12

together. Using

11:16

the Biobank data, scientists have discovered that

11:18

having a larger brain and

11:20

in particular a larger frontal cortex

11:23

is associated with higher intelligence. There

11:26

are also certain patterns in how different parts

11:29

of the brain communicate with each other that

11:31

can predict people's scores on cognitive tests.

11:35

There's still a lot of variability in intelligence

11:37

that scientists can't explain using these measures of

11:39

the brain though. But

11:42

access to enormous data sets like the

11:44

UK Biobank is allowing scientists

11:46

to pick apart how the

11:48

tangle of neurons inside our heads have

11:51

enabled us to develop vaccines, send

11:53

a man to the moon and even Create

11:56

AI. Lots.

12:07

Of researchers from around the world

12:10

used data from the Uk, Biobank

12:12

and other sources to investigate brain

12:14

intelligence. But intelligence in human brains

12:16

is not something that's easy to

12:18

pinpoint. There isn't one bit of

12:21

the brain that's responsible for it.

12:23

For example, And the more

12:25

you get into it, the harder it

12:27

gets to define what intelligence even is.

12:30

So. Let's take a step back

12:32

and look at how the brain works

12:34

at a more basic level. To

12:37

do that, I spoke to Daniel

12:39

Glazer. He's a neuroscientist at the

12:41

Institute of Philosophy, part of the

12:43

University of London. He works at

12:45

the intersection of neuroscience and Ai.

12:49

We know a lot about how the brain

12:51

structures, and we know a lot about how

12:53

it works in the sense of how the

12:55

molecular level works. I can tell you an

12:57

exquisite detail about the structure of the individual

12:59

neurons and at the level of the whole

13:01

brains. I can tell you what the front

13:04

doesn't, what the bat does. Makes

13:10

the difference at the macroscopic levels. So although

13:12

I know all of these levels of description

13:14

of the brains, I can't give you a

13:17

coherent story that tells you how the overall.

13:19

Behavior The Reiser Melissa exquisite detail but

13:21

I do know about the molecules. Let's

13:23

go into a bit of exquisite detail

13:25

than just describing Natsumi for me and

13:27

how the anatomy functions to brains are

13:30

collections of neurons which a nerve cells

13:32

and while nerve cells exist throughout the

13:34

body, that pain detectors and all sorts

13:36

of things like that in the brain,

13:38

they're all clump together in a big

13:41

watch and the principal property that almost

13:43

all nerve cells house is that they

13:45

use electricity to send signals over a

13:47

distance and this to things. that derive

13:49

from that so one is that these cells

13:51

are often philo gated so most says nobody

13:54

kind of roundy clumpy the have a a

13:56

shape like that nerve cells characteristically have go

13:58

along extended process which tend to call

14:00

an axon and you really can think

14:02

about this extended process like a wire.

14:04

And like a wire, nerve cells send

14:07

information along this long process using electricity.

14:09

So nerve cells are signalling devices that

14:11

get, if you like, information from one

14:13

bit of the cell to the other

14:15

bit of the cell along a long

14:17

bit called the axon and they use

14:19

electricity to do that. Just

14:21

in terms of how that manifests in sensing

14:23

the world, just explain to me how a

14:25

network of these cells smells

14:27

something or learns something.

14:30

I think to understand how this works, you

14:32

can actually go back in evolution around about

14:34

70 million years. You could use chemicals to

14:36

send information, ooh there's something nasty there pulled

14:38

back and you could retract your feelers. But

14:40

that only works at very short distances and

14:42

for animals and cells to get bigger, organisms

14:44

to get bigger, they needed to communicate information

14:46

about smells, about predators, about food over longer

14:48

distances. And so what evolution did, if we

14:50

can say it that way, about 70 million

14:52

years ago, is to use some of these

14:54

proteins that were being used for signalling within

14:56

cells and wire them up

14:58

to an electrical signal. And then at the

15:01

other end they turned them back into chemical

15:03

information which they then used to set off

15:05

other cells in the network. And that insight

15:07

interestingly, which was about signalling, is

15:10

paralleled in the evolution, in human terms,

15:12

of what we would call telegraphy. If

15:14

you want to send reliably a signal

15:16

of long distances you want to be

15:18

using some kind of code, for example

15:20

Morse code. And so the first transatlantic

15:22

cable used pulses, dit-dit-dit, da-da-da, which could

15:24

be reliably read out at the other

15:26

end. And it turns out that 70

15:28

million years ago evolution came up with

15:30

the same insight. So the critical thing

15:32

about nerve cells is that they

15:35

use electricity to signal. But that code is not

15:37

a kind of more less, more

15:39

less, more, you know, it's not

15:41

a continuously modulated signal, it's pulses.

15:44

And this transmission of information by pulses,

15:46

either fires or it doesn't, is a

15:48

critical thing you need to know about

15:50

nerve cells. When we come to think

15:52

about neurally inspired artificial systems, the fact

15:54

that it's thresholding, it's sending a pulse

15:57

or it isn't, it's doing a yes-no

15:59

firing pattern. The critical insight that gives

16:01

us all the information processing power that we use

16:03

now and it's kind of in a very sort

16:05

of crude were kind of a digital signal in

16:07

that respect. Not really tall is a digital signal

16:09

in the sense that the information is a one

16:12

or zero the to sell or the files or

16:14

it doesn't and replaced. Think about nerve cells. Problem

16:16

is not so much to start within the brains

16:18

but to think about flexing a muscle tone to

16:20

send a signal from your spinal cord to your

16:22

muscle in your arm if you want the muscles

16:25

contract more. Allocca get a sound like

16:27

a neuron for a second the nerve cell will go

16:29

Up Up Up Up Up Up Up Up Up Up

16:31

A baths if you want to contract a little bit.

16:33

Org Up Up Up Up Up By Similarly, if you

16:35

have a pain receptors and something's a bit painful you'll

16:38

get done. That.

16:40

That. That if something's really really painful,

16:42

the nerve cell signals that like I

16:44

pop up up up up up up

16:46

up up up Up And so the

16:49

rate coding We would say the rate

16:51

at which things fire in the can

16:53

be more subtle code is a yes

16:55

no signal that contains information over time

16:57

rather than an amplitude modulated smooth signal

17:00

as you might have in the nuances

17:02

of your voice. So it's in your

17:04

brain to neurons are very close together.

17:06

They it's exists in networks which represent

17:08

all sorts of functionality and memory. Etc

17:11

in your brain, Out

17:13

of the brain cells, the new ones

17:15

work together to learn something, whether it's

17:17

a language or what part of the

17:20

looks like or whatever else. When you're

17:22

on, the connected to each other individually

17:24

or networks, There was a strength of

17:26

the connection so you don't get the

17:28

same bang for your buck from each

17:30

of the cells that connects into a

17:32

particular sell. So much. And you've got

17:34

a cell. It's got thousands of other

17:37

cells. Connecting into. it comes thousands of

17:39

other sills of foreign. That

17:41

feats of a pulse is from those cells

17:43

does not give you the same input to

17:45

the so that the targets so we can

17:47

control the amounts of input that you get

17:49

from the sell by the strength of what

17:51

we call a sign ups the why that

17:53

comes in and if you liked to take

17:55

an analogy from humankind you might ask all

17:57

of your mates for a restaurant with me.

18:00

Station. But you're gonna pay more attention to

18:02

one of your friends who's good with food

18:04

or likes that kind of cuisine, knows the

18:06

city than another. So they're all saying pizza

18:08

Burger, we should go to the Indian place,

18:10

we should go to that Asian restaurant. Whatever.

18:12

but listening and you might say well I

18:14

hear all of those inputs but I'm gonna

18:16

up regulate one them down regularly, the other.

18:18

So that's the strength of connections in learning

18:20

is. It turns out that the restaurant you

18:22

chose was a good one. From your experience

18:24

you go to the restaurant was great, your

18:26

site off a restaurant was amazing and then

18:28

you say to was recommended. That restroom Iraq.

18:31

What? You know what? Next time I'm

18:33

looking for recommendation for restaurant I'm gonna

18:35

up. wait. Alex. Signal compared

18:37

to the other guys who didn't when

18:39

women out of them. So since the

18:41

fall together was together when a new

18:43

one fires it's has okay I got

18:45

excited. Now I'm asking what was the

18:47

input that got me to the place

18:49

I am on. I'm going to subtly

18:52

up regulate those input so that in

18:54

future the ones that got me to

18:56

this good place are more likely to

18:58

get me going again. That learning that

19:00

strengthen connection at a chemical never was

19:02

happening. At a chemical level

19:04

there are neurotransmitters, which seems generally

19:07

speaking, the structure of the dendrites.

19:09

so there are things called spines

19:11

the basic least allow. Each.

19:13

Neuron that fires to release more neurotransmitter

19:16

to that sells. So it changes the

19:18

neurochemistry and so small extent the neural

19:20

and estimates. It really just chase this

19:22

microstructure off the neurons so that you

19:25

get more input to protect yourself from

19:27

the cells that fired previously. Let's zoom

19:29

out. People always ask this question about

19:32

intelligence and on human brains. Intelligent? where

19:34

does that come from and all of

19:36

this but like if you look it

19:38

up online or or elsewhere me alone

19:41

as I would with any respected interview.

19:43

an interviewer i looked into from wikipedia before i

19:45

came out this morning and if you look up

19:48

unless the homeless or his intelligence it says it's

19:50

that thing which humans are good at rights that's

19:52

a bit facetious but there is a sort of

19:54

sex so for example when we look for intelligence

19:56

in animals or indeed implants the some nice stuff

19:59

about forests being it intelligence makes it, they can

20:01

just speed up forests, and they kind

20:03

of think things through, and they're generous, and they look

20:05

after each other, and they feel pain when their fellows

20:07

are chopped down. When we say that,

20:09

when we look for intelligence in animals, broadly speaking, we're

20:12

looking for things that they do that are like things

20:14

that we do, right? So I can

20:16

do better than this, but actually as a

20:18

starting point, intelligence is what we think like.

20:20

And so just break that down, what does

20:22

intelligence mean? Even if we can't define it

20:24

exactly, what are the kind of components of

20:26

what we think of as intelligence? So intelligence

20:28

is the ability to think things through, and

20:31

the evidence for that is that you can apply it

20:33

to different domains, you can abstract things to

20:35

look at something and see their structure, to

20:37

apply it to other things, to bring knowledge

20:40

of different domains to bear on certain things,

20:42

that requires kind of memory and breadth of

20:44

reference and understanding. It turns out that language

20:46

is quite a useful tool in helping one

20:48

to be intelligent, so it's difficult maybe to

20:50

imagine a human or a creature that doesn't

20:52

have any kind of symbolic abstract thought like

20:54

language and is still intelligent, it seems to

20:57

be very helpful to do that. Although,

20:59

when we start to look at other organisms

21:01

like octopuses, they exhibit behaviors which you might

21:03

think of intelligent, they solve problems, they learn

21:06

from experience, they think things through, they try

21:08

stuff and try things again differently from that,

21:10

and they probably don't have internal language of

21:12

thought. It's interesting, Alok, if you think of

21:14

any given thing, so for example, the ability

21:16

to project into the future, to think about

21:18

a future, you might think of that planning

21:21

as intelligent thing. The problem is,

21:23

as soon as you write down a single thing

21:25

that's about intelligence, you can usually

21:27

find an animal that does that particular thing, right?

21:29

So if you want planning, go for corvids like crow-like

21:31

creatures. We call crows intelligent, people say all the time.

21:34

Quite so, and that's because they share a thing which

21:36

we think of as intelligent ourselves, which is the

21:38

ability to plan, the ability, so for example, when crows

21:40

hide stuff, if they're observed hiding a thing by another

21:42

crow or in sometimes a different species, they'll kind

21:44

of wander away, and then when they're sure the person

21:46

who saw them like the piece of food is gone,

21:49

they'll go back and move the food hiding place

21:51

to a place somewhere else. Now why would you do

21:53

that? It's because you kind of have fought through that

21:55

when your back is turned, if you don't come

21:57

back soon, the person who saw you hiding it

22:00

is gonna come and move it. So we used

22:02

to think that only humans could do that. The

22:04

problem is once you, as you're encouraging me to

22:06

do, Alok, once you define a single thing, which

22:08

is, yeah, do you know what, intelligence is that,

22:10

I can probably find you an animal that can

22:12

do something like that. What I can't find you

22:15

an animal that can do is all the things

22:17

that we count of as intelligent, but that's a

22:19

bit circular again because we call them intelligent because

22:21

we do them. Yeah, so it is a bit

22:23

reductive and it's not at all comprehensive in the

22:25

way that you can define

22:27

intelligence. But as scientists,

22:30

you want to try and test hypotheses. You wanna

22:32

try and measure specific things

22:35

in this sort of slightly confusing world.

22:38

So in terms of intelligence in humans, what

22:40

are the ways that neuroscientists or others would

22:42

try and measure that or test it? So

22:44

we can certainly look at what's going on

22:46

in people's brains when they do things that

22:48

we would consider intelligence. And we can also

22:50

particularly do that in the bits of brains

22:52

of which we have more than animals that

22:54

are less intelligent than we are. So we

22:56

can learn by looking at the bits of

22:58

the brain which are different in us from

23:00

monkeys, and we can draw out

23:02

the circuits which enable us to do that kind

23:04

of complex thought. I do think

23:07

that intelligence is something that allows us

23:09

to manipulate objects. It's very rare

23:11

for somebody to be just intelligent without using some

23:13

kind of external system, even if they've internalized it.

23:15

So language would be an example of an external

23:17

system which you put in your head. But actually

23:19

smart people use tools well. While

23:21

we're talking a lot, we've got somebody very friendly

23:24

in the room who's operating some complex sound recording

23:26

equipment, and you're using a Mac to structure your

23:28

thinking and look at the questions. That's

23:30

intelligence. We use these prosthetics. And actually again,

23:32

when we come to think about large language

23:35

models and the contemporary developments in AI, one

23:37

of the things that intelligent people like us

23:39

do is to make good use of these

23:41

tools. Now we also fool ourselves that they

23:43

might be intelligent too, but nobody

23:45

thinks that their phone is intelligent really, but

23:48

they use it to enhance their own intelligence

23:50

if their smart orphan can defeat your intelligence

23:52

by too much scrolling. But you can use

23:54

it to extend yourself by judicious use of

23:57

Wikipedia on the fly or storing information in

23:59

a helpful. way. And this ability to

24:01

use tools is something that we observe

24:03

in the history of man, actually, when

24:05

these frontal lobes developed, as something that

24:07

is a marker of a time when

24:09

our intelligence probably really took off. It's

24:12

interesting with the phone example, actually, isn't

24:14

it? A mobile phone that's connected to

24:16

the internet, basically a small computer has

24:18

memory, it has some sorts of

24:20

reasoning capabilities too. These markers, as you say, of

24:22

intelligence, but it doesn't have all of the things.

24:24

It doesn't plan or abstract things in the way

24:26

that humans do. But I guess it's a different

24:28

type of intelligence in that respect, but we

24:30

would never call it intelligent. You're right. In

24:32

general, that's right. I think it's an interesting

24:35

question about ascribing intelligence is worth pondering for

24:37

a second. Fast forwarding to

24:39

LLMs, artificial neural networks like

24:41

large language models and machine learning, I

24:43

think our inevitable ability, we can't

24:45

turn it off to make them

24:47

seem intelligent, allows us to use

24:50

these tools more effectively. It doesn't

24:52

mean they are intelligent, but treating

24:54

them like they're intelligent enables us

24:56

to engage with them in more

24:58

effective ways. When we come to

25:00

ask, as I'm sure you will, Alot,

25:02

whether these machines are smart or not,

25:04

we must always beware of this innate

25:06

capacity of humans to ascribe intelligence to

25:08

others and to machines. That will

25:10

mislead us when we try to make judgments

25:13

about the new machines that we've built. All

25:15

right. Well, we've talked about the difficulty of

25:17

defining human intelligence. We talked about the difficulty

25:19

of actually trying to understand

25:21

it at all the different levels, from

25:24

the whole level to the

25:26

cellular level. Clearly, huge amounts still to

25:29

learn. I guess if we try

25:31

to understand where all of

25:33

this knowledge leads into how to do

25:35

the artificial bit of the artificial intelligence.

25:38

When we're talking about computer scientists who

25:40

were looking for ways of being inspired

25:42

by intelligence to make artificial versions of

25:44

it, was it a good idea to

25:46

try and build artificial intelligences on the

25:48

human brain? I suppose it's the only

25:50

way they had, right? When

25:52

computer scientists tried to make smarter machines, one of

25:54

the observations that they made is that maybe what's

25:57

important about the way that humans think is the

25:59

way that Is the wet stuff?

26:01

Is the neurons? And so we can ask

26:03

what are the properties of neurons that they lit

26:05

upon and how did they Implement

26:07

them and actually they did go right back

26:09

to basics. So to understand a neural network

26:11

in the sense of computers That's the way

26:14

that most machine learning algorithms work. You really

26:16

just start with a neuron It's a device

26:18

which takes inputs from a bunch of other

26:20

neurons Not all the

26:22

neurons affect it to the same extent. Those

26:24

are called weights This is true of a

26:26

tiny little worm each of the neurons that

26:28

comes on to another neuron excites it to a

26:30

different extent and It works out

26:33

on the base of those inputs whether it's past

26:35

a threshold for excitement or not And if it

26:37

does it goes boom and that ping that spike

26:39

goes to the next one taking that architecture

26:42

and layer in the pond it a Learning

26:45

rule which as we said before things

26:47

that fire together wire together So by

26:49

adjusting the weights between the neurons to

26:51

up regulate things that tended to make

26:53

things fire in a good context Those

26:56

two simple insights give you quite a

26:58

powerful Computational learning

27:00

machine now when we

27:02

talk about these neural networks, they're actually

27:04

being implemented in digital architecture So funny

27:06

enough, you've got a good old-fashioned digital

27:08

computer like the kind that works in

27:10

your desktop PC or in your phone

27:13

But it's running a simulation of

27:15

these very simple neurons and again

27:17

if you think about the exquisite

27:19

Microarchitecture of human neurons it would

27:22

take you know years to describe

27:24

even a single human neuron So

27:26

no we abstract it into some

27:28

inputs some weights a firing pattern

27:30

So this very simplified neuron

27:32

is at the basis of all

27:34

of the artificial neural networks that

27:37

underlie machine learning and current

27:39

AI So

27:56

Next We'll continue that thought and move

27:58

from human cells. The silicon Chips,

28:00

a look at the first attempts to

28:03

create artificial versions of the human brain

28:05

and one of the godfathers of modern

28:07

A I will tell us about the

28:09

first time his computer system showed some

28:11

of the skills that deglaze it has

28:13

been telling about. The. So Common

28:15

law. First though, just

28:18

a quick reminder that this is a

28:20

free episode of Babbage. To continue this

28:22

thing to ah special series on a I

28:24

you'll need to sign up to Economists

28:26

podcast plus a now's the perfect time

28:28

to do so. We've got a sale on

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subscribe for less than two dollars fifty

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Sunday the seventeenth of March. And

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as a subscriber, he would only

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have access to all of our

28:41

specialist Meekly podcasts. You'll be able

28:43

to join us to Babbage's first

28:45

ever life events following the conclusion

28:47

of this very series that's going

28:49

to be held on Thursday, April

28:51

The fourth where we're going to

28:53

answer as many of your questions

28:55

is because on the science behind

28:57

artificial intelligence, don't miss out. You

28:59

can submit your questions, check the

29:01

start time in your region, and

29:03

book your place by going to

29:05

economists.com/a I. Isn't. All one word blink

29:08

is in the show. Today

29:27

on Babbage, we've heard about how

29:29

the brain works and we're trying

29:31

to unpack how computer science has

29:33

C Wanted to build intelligent systems.

29:35

were inspired by what neuroscientist said

29:37

already. Fund. But. Rather

29:39

than building an artificial version of

29:42

a physical nerve cell, computer scientists

29:44

wanted to build virtual once. And.

29:47

That leads us to the next step as

29:49

we build our understanding of the science behind

29:51

More Than A I. Question

29:54

to. What? was the

29:56

first artificial neural To

30:15

answer this question, we travelled across

30:17

the Atlantic Ocean to Boston in Massachusetts.

30:21

Main Street, which carries traffic after crossing

30:23

the Charles River from central Boston, is

30:25

awash with offices of some of the

30:27

world's biggest tech firms, Google,

30:29

Facebook and IBM. This

30:32

part of the city has been called the

30:34

most innovative square mile on the planet. Companies

30:38

are lured in because the

30:40

area is dominated by two

30:42

institutions, Harvard University and the

30:44

Massachusetts Institute of Technology, or

30:46

MIT. Few

30:48

places on the planet have played

30:50

a more central role in the

30:52

evolution of modern artificial intelligence. Our

30:55

quest to mathematically think about

30:58

intelligence and model our

31:00

brains goes back to 1943, where

31:06

Warren McCulloch and Walter Pitts introduced

31:09

the concept of neural networks. Daniela

31:12

Ruz is the director of the

31:14

MIT Computer Science and Artificial Intelligence

31:16

Laboratory, also known as CSAIL.

31:19

And they published the first mathematical

31:21

model that at

31:24

that time was believed to capture

31:26

what is happening in our brain.

31:29

If the way that neurons work in the

31:31

brain can be explained by mathematics, then

31:34

the brain's network surely could be

31:36

replicated using computer code. Professors

31:40

McCulloch and Pitts thought that

31:42

machines with brain-like architecture could

31:44

have a lot of computational

31:46

power. The early

31:48

artificial neuron was a very

31:51

simple mathematical model. You

31:53

had a computational unit that

31:56

took as input data from other

31:58

sources, maybe other units. units. The

32:01

input was weighted

32:04

by parameters. And then

32:06

inside the artificial neuron, the computation

32:08

was very simple. It was a

32:10

thresholding computation, essentially, if the

32:13

sum total of what came

32:15

in was larger than given

32:17

threshold, the neuron output

32:19

1, otherwise the neuron output

32:22

0. So

32:24

the computation was discrete and very

32:26

simple, essentially a step function. You're

32:29

either above or below a value.

32:33

Neurons in the human brain also

32:35

operate using discrete functions, which Dan

32:37

Glaser mentioned earlier. They either

32:39

fire or they don't fire. A psychologist

32:43

at Cornell University called Frank Rosenblatt

32:45

went on to develop this model

32:48

to create an artificial neuron, a

32:51

mathematical function that he called a

32:53

perceptron. At first,

32:55

the perceptron seemed promising. After

32:57

learning some examples, perceptrons could do

33:00

some basic things, giving

33:02

a yes or no answer to

33:04

an input that hadn't been previously

33:06

analysed by the machine. Let's say

33:08

you've fed the model some data about

33:10

the strength and speed of athletes in

33:12

a sports team. Learning

33:14

from those two variables, the model could

33:16

answer whether or not a new athlete

33:18

would be likely to be accepted into

33:20

a team. As the

33:22

field matured, however, flaws

33:24

in the perceptron became clearer. Because

33:27

perceptrons only worked like a single

33:29

artificial neuron, they couldn't be

33:32

trained to recognise patterns that were more

33:34

complex. What about, for example, athletes who

33:36

were neither particularly fast nor strong but

33:39

had really good technique? In

33:42

1969, Marvin Minsky and

33:45

Seymour Papert co-authored Perceptrons,

33:48

which is a book that demonstrated

33:50

that mathematically, if all

33:52

you have is a single layer

33:54

neural network, then you could only

33:57

compute linear functions. function

34:00

and you can have a closed form solution,

34:02

there's no need for machine learning. And actually

34:05

this work triggered the

34:07

first AI winter because

34:09

people lost faith in what would be

34:11

possible. It

34:14

became clear that if artificial neural

34:16

networks were to work, they'd have

34:18

to have more layers of perceptrons

34:21

to deal with the complexity of

34:23

the real world. During

34:26

the AI winter that Daniela mentioned,

34:28

funding dissipated and interest in

34:30

a very idea of creating

34:33

artificial neural networks dwindled. There

34:36

was very little progress until the

34:38

1980s. But

34:40

some researchers did persist within that

34:42

time and they found

34:45

other pathways to creating intelligent

34:47

machines. In 1950 Alan

34:49

Turing introduced a

34:52

very important benchmark for machine

34:55

intelligence in his paper

34:57

Computing Machinery and Intelligence. This

34:59

is the paper that introduced the Turing

35:02

test. The idea

35:04

that you know whether

35:06

a machine is intelligent or not

35:08

if you aim to converse with

35:11

it and if you cannot tell whether on

35:13

the other side you're talking to

35:15

a machine or to a person. So

35:17

there have been many efforts to

35:20

build intelligent machines that meet

35:22

the Turing test in particular

35:24

at MIT. One

35:27

of the earliest systems called

35:29

ELISA was introduced and

35:32

ELISA enchanted people for

35:34

a long time. ELISA was one of

35:36

the first so-called chatterbots which took the

35:38

world by storm in 1966. Its creator

35:41

Joseph Weisenbaum

35:44

put the bot in the role

35:46

of a psychotherapist. A

35:48

user would type a message on

35:50

an electric typewriter and the

35:52

machine would reply. Men

35:57

are all alike In what way? They're

36:01

always talking about something or other.

36:03

Can you think of a specific

36:06

example? Well my boyfriend Nathan come

36:08

here. Your boyfriends me to come

36:10

here he says and I'm depressed.

36:13

much. A time I am

36:15

sorry to hear the word

36:17

press is true. I. Am

36:19

unhappy. The

36:22

early Ai systems used what is

36:24

called a lot of rule they

36:26

systems so you'll need to solve

36:28

a problem. You identify some rules

36:30

and then you're right. The program

36:32

that makes decisions according to the

36:34

set of rules to this is

36:36

sometimes called good old. Fashioned A I.

36:42

Realize it didn't use an artificial

36:44

neural network and it didn't learn

36:46

from it's input. Instead, the language

36:49

model recognized key word and reflected

36:51

the back in the form of

36:53

simple phrases. All questions. Supposedly.

36:55

Modeling The kind of conversation is expect

36:58

with a therapist. It was almost like

37:00

a mirror. Allies: I did not

37:02

pass the turing test. Which was in

37:04

fact the point. The researchers

37:06

behind the bought designed Eliza to

37:09

show how superficial the state of

37:11

human to conversation really was, but

37:13

in reality hits have the opposite

37:16

response. People. Became engaged

37:18

in long, deep conversations with

37:20

the computer program. You

37:22

know that's really incredible. It's as if it

37:24

really understood what I was saying, but it

37:26

doesn't. Of course, it's just a bag of

37:29

tricks. Oh, I get it at have the

37:31

faintest idea what I'm talking about. Eliza

37:35

was not an intelligent machines, but

37:37

it made people stop and think

37:39

about what the world might be

37:41

like if artificial intelligence did come

37:43

along. It was perhaps

37:45

also the first time that humans showed

37:47

her winning. We all are to believe

37:49

that computers could be intelligent fish. They

37:51

spoke to us and our own language.

37:54

is another example of what done

37:56

glaser described earlier as the innate

37:58

desire of humans anthropomorphize everything

38:01

in the world around us. Of

38:04

course in the decades since

38:06

Eliza, chatabots became chatbots and

38:09

that's not all. These days our

38:12

conversations with chatbots easily

38:15

pass the Turing test. But

38:19

how did the skills of chatbots that we

38:22

see today emerge from the primitive AI

38:24

of the 1960s? What was

38:27

it that made the theory of

38:29

artificial neural networks actually work in

38:31

practice? At

38:33

its core the answer lies in the insight

38:35

that artificial neurons had to be layered on

38:37

top of each other like neural

38:39

networks are in the human brain. And

38:42

so at the end of the 1960s researchers

38:45

came up with the idea of

38:47

the deep neural network. The

38:50

deep learning revolution that came several

38:52

decades later happened in no

38:54

small part thanks to three scientists who

38:56

would later become known as the godfathers

38:59

of AI. The systems we

39:01

were building were so dumb and

39:03

so weak and so difficult to train.

39:06

That's one of the so-called godfathers, Yoshua

39:08

Bengio. He is a computer scientist at

39:10

the University of Montreal and he was

39:12

a key figure in the development of

39:14

deep learning. What got me

39:16

really excited when I started reading some

39:18

of the early neural net papers from

39:21

the early 80s is

39:23

the idea that our

39:25

own intelligence with our

39:28

brain could be explained by a

39:30

few principles just like think

39:32

of how physics works. Could it be

39:34

possible that we would

39:37

do something similar for understanding

39:39

intelligence and of course take

39:41

advantage of those principles to design intelligent machines?

39:44

And in fact it goes also in the

39:46

other direction because there are experiments we can

39:48

run in computers that we can't run on

39:50

real brains and so the

39:52

work we've been doing in

39:55

AI is informing

39:57

also theories of how the brain

39:59

works. two-way street. So that

40:01

synergy and that

40:03

idea that maybe there is an explanation

40:06

for intelligence that we can communicate as

40:08

a scientific theory is

40:10

really what got me into this field. Talk

40:13

to us about what the challenge was

40:15

in trying to model the human brain

40:17

in silicon. Well, we didn't try

40:19

to model the human brain in silicon because

40:21

that would have seemed to daunting

40:23

a task. Instead,

40:26

we looked at the simplest possible

40:30

models that come from

40:32

neuroscience and see

40:34

how we can tweak them. In

40:36

the early days when I was doing my PhD,

40:39

we were trying to use these

40:42

systems to classify simple patterns

40:44

like shapes of characters

40:47

or phonemes using the

40:49

sound recording of mesing

40:52

R E O. Can

40:54

a neural network, which is this very

40:57

simplified calculation inspired by neurons in

40:59

the brain, can a neural

41:02

network learn to distinguish between

41:04

those different categories of objects in the

41:06

input? I've been working on

41:08

this from the mid 80s to the

41:11

mid 2000s. What

41:13

were some of the first things you tried to do

41:15

with the neural networks to prove that they could be

41:18

useful? In

41:20

the 90s, I

41:22

worked on these pattern recognition tasks,

41:24

both speech and image

41:27

classification.

41:30

Industrial applications emerged. For example,

41:32

I worked on a project

41:34

to use neural nets for

41:37

classifying amounts on checks to

41:39

automate the process of making sure that

41:41

a check you deposited the bank has

41:43

the right amount and

41:46

that was actually deployed in banks

41:48

in the 90s and

41:50

processed a large number of checks. All of

41:52

the approaches that had been tried before didn't

41:55

do very well because there is

41:57

so much variability between people. We

42:00

write in different ways. So

42:03

it was not trivial, and

42:05

it is something that had

42:07

a lot of economic value already

42:10

to address that challenge. Next

42:15

week, we'll look at exactly how

42:17

artificial neural networks allowed machines to

42:19

learn, and we'll also examine

42:21

the clever maths that allowed all of

42:23

this to happen. People

42:25

realize that if you could insert a

42:28

middle layer, which is sometimes called a

42:30

hidden layer, these systems

42:32

could actually compute many more functions.

42:36

That's next time on Babbage. Thanks

42:42

to Daniel Glaser, Daniela Ruz, Yoshua

42:44

Bengier, the economist, Aimee Johnston, and

42:46

all the people she spoke to

42:49

at the UK Biobank. And

42:51

thank you for listening. To follow the next

42:53

stage of our journey to understand modern

42:55

AI, subscribe to Economist, Test Test

42:58

Plus. Find out more by clicking the

43:00

link in the show notes. Babbage

43:02

is produced by Jason Haskin and Kannar

43:04

Patel, with mixing and sound design by

43:06

Nuka Rofast. The executive

43:08

producer is Hannah Mourinho. I'm

43:11

Alok Jha, and in London, this

43:13

is The Economist. Thank

43:22

you.