0:00

Hey, this is David Eagleman and this past week was

0:02

my birthday, so I took a week off. So I'm

0:04

going to run an episode that I did earlier,

0:06

episode number seven. This is called

0:09

is AI actually intelligent? And

0:11

how would we know if it gets there? This

0:14

episode is from one year ago, but as time

0:16

goes on this becomes more and more

0:18

relevant, So please enjoy

0:20

and I will see you next week with a new episode.

0:28

Modern AI is blowing everybody's

0:30

mind. But is it intelligent

0:33

in the same way as the human brain? And

0:36

could AI reach sentience?

0:39

And how would we know when

0:41

it gets there? Welcome

0:44

to Inner Cosmos with me, David

0:46

Eagleman. I'm a neuroscientist

0:49

and an author at Stanford University,

0:51

and I've spent my whole career studying

0:54

the intersection between how

0:56

the brain works and how

0:58

we experience life.

1:03

Like most brain researchers, I've

1:06

been obsessed with questions of

1:08

intelligence and consciousness.

1:11

How do these arise from collections

1:14

of billions of cells in our brains?

1:17

And could intelligence and consciousness

1:19

arise in artificial brains?

1:22

Say on chat GPT. Those

1:24

are the questions that we're going to attack today.

1:27

Early efforts to figure out the brain, looked

1:29

at all the billions of cells

1:32

and the trillions of connections, and

1:34

said, look, what if we just think of

1:36

each cell as a unit, and

1:39

each unit is connected to other units

1:41

and where they connect, which

1:44

is called the sinnapps, or one cell gives

1:46

a little signal to the next cell. What if

1:48

we just looked at that like a simple

1:51

connection that has a strength

1:53

between zero and one, or zero

1:55

means there's no connection, and one means

1:58

it's the strongest possible connection. So

2:00

this was a massive oversimplification

2:03

of the very complicated biology, but

2:06

it allowed people to start thinking about

2:09

networks and writing down different

2:11

ways that you could put artificial

2:13

neural networks together. And for

2:15

more than fifty years now people have been doing

2:18

research to show

2:20

how artificial neural networks can do

2:22

really cool things. It's a

2:24

totally new kind of way of doing computation.

2:27

So you've got these units, and you've got these

2:29

connections between them, and you've change

2:31

the strength of the connections and

2:34

information flows through the network

2:36

in different ways. Now, my

2:38

colleagues and I have long pointed

2:41

out the ways in which biological

2:43

brands are different and how artificial

2:46

neural networks just push around numbers

2:49

and play statistical tricks. But

2:51

we're entering a revolution

2:53

right now. Large language

2:56

models like GPT four

2:58

or BARD consume trillions

3:00

of words on the Internet and they figure

3:03

out probabilistically which

3:05

word is going to come next given

3:07

the massive context of all the words that have come

3:10

before. So these networks,

3:12

as I talked about on the previous episode,

3:15

are showing incredible successes

3:18

in everything from writing

3:20

to art, to coding

3:23

to generating three dimensional worlds.

3:26

They're changing everything, and they're doing

3:28

so at a pace that we've never

3:30

seen before, and in fact, the

3:33

entire history of humankind has

3:35

never seen before. And there are

3:37

all the societal questions

3:39

that everyone's starting to wrestle with right now,

3:42

like the massive potential

3:45

for displacement of human jobs.

3:48

But today I want to zoom

3:50

in on a question that has captured

3:52

the imagination of scientists and

3:54

philosophers and the general public.

3:58

Could aim alive

4:01

in some way, like become

4:03

conscious or sentient. Now,

4:05

there are lots of ways to think about

4:07

this. We can ask whether AI

4:10

can possess meaningful intelligence,

4:13

or we can ask if it is sentient,

4:16

which means the ability to feel or

4:18

perceive things, particularly

4:20

in terms of sensations like pleasure

4:22

and pain and emotions. Or we can ask

4:25

whether it is conscious, which

4:27

involves being aware of one's self

4:29

and one's surrounding. Now, there are specific

4:32

and important differences between

4:34

these questions, but really I don't care

4:37

for the present conversation. The question

4:39

we're asking here is

4:41

is chat GPT just zeros

4:43

and ones moving around through transistors like

4:46

a giant garage door opener. Or

4:49

is it thinking? Is it having some sort of experience?

4:53

Is it having a private inner life

4:55

like the type that we humans have. As

4:58

we think about the possible of

5:00

sentient AI, we immediately

5:03

find ourselves facing really deep

5:05

ethical questions, the main one being

5:08

if we were to create a machine with

5:10

consciousness, what responsibility

5:13

do we have to treat it

5:15

as a living being? Would

5:17

you be able to turn it off when you're done with

5:19

it at night or would that be murder?

5:22

And what if you turn it off and then you turn

5:24

it back on. Would that be like the

5:26

way that we go into a sleep state at

5:28

night where we're totally gone, and

5:30

then we find ourselves back online

5:33

in the morning and we think, yeah, I'm the same person,

5:35

but I guess eight hours just disappeared.

5:37

Anyway, more generally, would we feel

5:40

obligated to treat it the way we treat

5:43

a sentient fellow human.

5:46

With our current laptops, we're used to

5:48

saying, sure, I can sell

5:50

it, I can trade it, I can upgrade

5:52

it. But what happens when we reach

5:55

sentient machines? Can

5:57

we still do this or would it somehow

5:59

be like putting a child up

6:01

for adoption or giving your pet away?

6:03

Things that we don't take lately. And

6:06

eventually we're going to have entire legal

6:08

precedence built around the question

6:11

of AI rights and responsibilities.

6:14

So that's why today I want to talk

6:16

about these issues of intelligence

6:18

and sentience. Does an AI

6:21

like chat gpt experience

6:24

anything when chat gpt

6:26

writes a poem? Does it appreciate

6:29

the beauty when it types out

6:31

a joke? Does it find itself amused

6:34

and chuckling to itself. Let's

6:36

start with a guy named Blake Lemoyne

6:38

who was a programmer at Google and

6:41

in June of twenty twenty two, he was

6:43

exchanging messages with a

6:46

version of Google's conversational

6:48

AI, which was called Lambda at the time. So

6:51

he asked Namda for an

6:53

example of what it was afraid

6:55

of and it gave him this very

6:57

eloquent response about how

7:00

was afraid of being turned

7:02

off, So he wrote an internal

7:04

memo to Google leadership

7:06

than which he said, I think this AI is

7:09

sentient. And the leadership

7:12

at Google felt that this was an

7:14

entirely unsubstantiated claim,

7:17

and so they made the decision to fire him

7:19

for what they took as an inappropriate

7:22

conclusion that just didn't have enough evidence

7:24

beyond his intuition to qualify

7:27

for raising the alarm on this. So obviously

7:30

this immediately fired up the news cycles

7:32

and the rumor mill and conspiracy

7:34

theorists thought, Wait, if AI isn't

7:36

conscious, why would they fire him. They're

7:39

firing of him as all the evidence I need

7:41

to tell me that AI is sentient? Okay,

7:45

but is it? What does

7:47

it mean to be conscious or sentient?

7:49

How the heck would we know when

7:52

we have created something that gets there?

7:55

How do we know whether the AI is

7:57

sentient or instead whether humans are fooling

7:59

them so into believing that it is well.

8:02

One way to make this distinction would

8:04

be to see if the AI could

8:07

conceptualize things, if it

8:09

could take lots of words and facts

8:11

on the web and abstract

8:13

those to some bigger idea. So

8:16

one of my friends here in Silicon Valley said

8:18

to me the other day, I asked

8:20

chat gpt the following question, Take

8:23

a capital letter D and

8:26

turn it flat side down. Now

8:28

take the letter J and slide

8:30

it underneath. What does that look

8:32

like? And chat gpt said,

8:35

and umbrella. And my friend

8:37

was blown away by this, and he said, this

8:40

is conceptualization. It's

8:43

just done three dimensional reasoning.

8:46

There's something deeper happening

8:48

here than just parenting words.

8:51

But I pointed out to him that this particular

8:53

question about the D on its side

8:56

and the J underneath it is one of the

8:58

oldest examples in psychology

9:00

classes when talking about visual

9:02

imagery, and it's on the Internet

9:04

in thousands of places, so of course it

9:06

got it right. It's just parroting

9:09

the answer because it has read the

9:11

question and it has read the answer before.

9:14

So it's not always easy to determine

9:17

what's going on for these models

9:19

in terms of whether some human

9:22

somewhere has discussed this point and written

9:24

down the answer. And the general story

9:27

is that with trillions of words

9:29

written by humans over centuries, there

9:32

are many things beyond your capacity

9:35

to read them or to even imagine

9:37

that they've been written down before, but

9:39

maybe they have. If any human

9:42

has discussed a question before

9:44

has conceptualized something, then

9:46

chat GPT can find that

9:48

and mimic that. But that's not conceptualization.

9:52

Chat GPT is doing a thousand amazing

9:55

things, and we have an enormous

9:57

amount to learn about it. But

10:00

we shouldn't let ourselves get fooled

10:03

and mesmerized into believing

10:05

that it's doing something more than it is. And

10:07

our ability to get fooled is

10:09

not only about the massive statistics

10:12

of what it takes in. There are other

10:14

examples of seeming

10:17

sentience that result from

10:19

the reinforcement learning

10:21

that it does with humans. So

10:24

here's what that means. The network generates

10:27

lots of sentences and thousands

10:29

of humans are involved in giving it

10:32

feedback, like a thumbs up or a thumbs

10:34

down, to say whether they appreciated

10:37

the answer, whether they thought that was

10:39

a good answer. So, because

10:41

humans are giving reward to

10:43

the machine, sometimes that pushes

10:45

things in weird directions

10:47

that can be mistaken for sentience.

10:50

For example, scholars have shown

10:52

that reinforcement learning with humans

10:55

makes networks more likely to

10:57

say, don't turn me off,

11:00

just like Blake had heard but don't

11:03

mistake this for sentience. It's only

11:05

a sign that the machine is saying

11:07

this because some of the human participants

11:10

gave it a thumbs up when the large

11:12

language model said this before, and

11:14

so it learned to do this again.

11:17

The fact is, it's sometimes hard

11:19

to know why. Sometimes we see

11:21

an answer that feels very impressive.

11:25

But we'd agree that pulling

11:27

text from the Internet and parroting it back

11:29

is not by itself intelligence

11:32

or sentience. Chat GPT

11:34

presumably has no idea

11:37

of what it's saying, whether that's a poem

11:39

or a terrorist manifesto, or

11:42

instructions for building a spaceship or

11:44

a heartbreaking story

11:46

about an orphaned child. Chat

11:49

GPT doesn't know, and it doesn't

11:51

care. It's words in and

11:54

statistical correlations out.

11:56

And in fact, there has been a fundamental

12:00

philosophical point made about this

12:02

in the nineteen eighties when the philosopher

12:04

John Surrele was wondering

12:07

about this question of whether a computer

12:10

could ever be programmed so that it

12:12

has a mind, and

12:14

he came up with a thought experiment that he called

12:17

the Chinese room argument,

12:19

and it goes like this, I

12:22

am locked in a room and

12:25

questions are passed to me through

12:27

a small letter slot, and these

12:29

messages are written only in Chinese,

12:32

and I don't speak Chinese. I have no clue

12:34

what's written on these pieces of paper. However,

12:37

inside this room, I have a

12:39

library of books, and they

12:41

contain step by step instructions

12:44

that tell me exactly what to do with

12:46

these symbols. So I look

12:48

at the grouping of symbols, and

12:50

I simply follow steps in the book

12:52

to tell me what Chinese symbols

12:55

to copy down in response. So

12:57

I write those on the slip of paper. And

13:00

when I pass the paper back out of the slot.

13:02

Now, when the Chinese speaker

13:05

receives my reply message,

13:07

it makes perfect sense to her. It seems

13:10

as though whoever is in the room is

13:13

answering her questions perfectly, and

13:15

therefore it seems obvious that the person in

13:18

the room must understand

13:20

Chinese. I've fooled

13:22

her, of course, because I'm only following a set

13:24

of instructions with no understanding

13:27

of what's going on. With enough

13:29

time and with a big enough set of instructions,

13:32

I can answer almost any question posed

13:34

to me in Chinese. But I, the

13:37

operator, do not understand Chinese.

13:40

I manipulate symbols all day

13:42

long, but I have no idea

13:44

what the symbols mean. Now,

13:48

The philosopher John Searle argued, this

13:51

is just what's happening inside

13:53

a computer. No matter how

13:55

intelligent a program like chat

13:57

GPT seems to be, it's

14:00

only following sets of instructions

14:03

to spit out answers. It's

14:05

manipulating symbols without

14:08

ever really understanding what it's

14:10

doing. Or think about what Google is

14:12

doing. When you send Google a

14:15

query, it doesn't understand your question

14:18

or even its own answer. It simply moves

14:20

around zeros and ones and logicates

14:23

and returns zeros and ones to you. Or

14:25

with a mind blowing program like Google

14:27

Translate, I can write a sentence

14:29

in Russian and it can return

14:32

the translation in Amharic.

14:35

But it's all algorithmic. It's

14:37

just symbol manipulation. Like

14:40

the operator inside the Chinese

14:42

room, Google Translate doesn't

14:44

understand anything about the sentence. Nothing

14:48

carries any meaning to it. So

14:50

the Chinese room argument suggests that

14:53

AI that mimics human intelligence

14:56

doesn't actually understand what it's talking

14:58

about. There's no meaning

15:01

to anything, CHATCHYPT says, and

15:03

Serle used this thought experiment

15:05

to argue that there's something about human

15:08

brains that won't be explained

15:10

if we simply analogize them

15:13

to digital computers. There's

15:15

a gap between symbols

15:17

that have no meaning and our

15:20

conscious experience. Now,

15:27

there's an ongoing debate about the interpretation

15:30

of the Chinese room argument, but however

15:32

one construes it, the argument exposes

15:36

the difficulty in the mystery of

15:38

how zeros and ones would

15:40

ever come to equal our

15:43

experience of being alive in the

15:45

world. Now, just to be very clear

15:47

on this point, we don't understand why

15:50

we are conscious. There's still

15:52

a huge amount of work that has to be done

15:54

in biology to understand that. But

15:56

this is just to say that simply

15:58

having zeros in one moving around

16:01

wouldn't by itself seem to be sufficient

16:05

for conscious experience. In

16:07

other words, how do zeros and ones

16:09

ever equal the sting

16:11

of a hot pepper, or the yellowness

16:15

of yellow or the beauty

16:18

of a sunset. By the way, I've covered

16:20

the Chinese room argument in my TV show

16:22

The Brain, and if you're interested in that, I'll link

16:24

the video on Eagleman dot com

16:26

slash podcast. Now, all

16:29

this is not a criticism of the approach

16:31

of moving zeros and ones around. But

16:33

it is to point out that we shouldn't confuse

16:36

this type of Chinese room

16:38

correlation with real

16:40

sentience or intelligence.

16:43

And there's a deeper reason to be suspicious

16:46

too, because despite the

16:48

incredible successes of large

16:50

language models, we also see

16:53

that they sometimes make decisions

16:55

that expose the fact that they

16:57

don't have any meaningful model of

16:59

the In other words, I think we

17:01

can gain some fast insight

17:04

by paying attention to the places where

17:06

the AI is not working so

17:08

well. So I'll give three quick examples.

17:11

The first has to do with humor. AI

17:14

has a very difficult time making

17:16

an original joke, and

17:19

this is for a simple reason. To make

17:21

up a new joke, you need

17:23

to know what the ending is and then

17:25

you work backwards to construct

17:27

the joke with red herrings so no

17:29

one sees where you're going and it happens

17:32

at the way these large language models

17:34

work is all in the forward direction.

17:36

They decide what is the most probable

17:39

word to come next, So they're

17:41

fine at parroting jokes

17:44

back to us, but they're total failures

17:47

at building original jokes. And

17:49

there's a deeper point here as well. To

17:51

build a joke, You need to have some model,

17:54

some idea of what will

17:56

be funny to a fellow human, what

17:59

shared concept or shared experience

18:01

would make someone laugh. And for

18:03

that, you generally need to have the

18:06

experience of a human life with all

18:08

of its joys and slings

18:10

and arrows and so on. And these

18:12

large language models can do a lot of things,

18:14

but they don't have any

18:17

model of what it is to be

18:19

a human. My

18:21

second example has to do

18:23

with the flip side of making a joke,

18:25

which is getting a joke. And if you look

18:28

carefully, you will see how current AI

18:30

always fails to catch jokes that are thrown

18:32

at it. It doesn't get jokes because

18:34

it doesn't have a model of what it

18:36

is to be a human. But this point

18:38

goes beyond jokes. One

18:41

of the most remarkable feats of these

18:43

large language models is summarizing

18:46

large texts, and in

18:48

twenty twenty two, open Ai announced

18:51

how they could summarize entire

18:53

books like Alice in Wonderland. What

18:55

it does is it generates a summary

18:57

of each chapter, and then it uses

18:59

those after summaries to make a summary

19:01

of the whole book. So for Alice in Wonderland,

19:04

it generates the following. Alice

19:06

falls down a rabbit hole and grows to a giant

19:09

size. After drinking a mysterious bottle,

19:11

she decides to focus on growing

19:13

back to her normal size and finding her

19:15

way into the garden. She meets the caterpillar,

19:18

who tells her that one side of a mushroom will

19:20

make her grow taller, the other side shorter.

19:22

She eats the mushroom and returns to her normal

19:25

size. Alice attends a party with the

19:27

Mad Hatter and the march Hare. The

19:29

Queen arrives and orders the execution

19:32

of the gardeners for making a mistake with the roses.

19:34

Alice saves them by putting them

19:36

in a flower pot. The King and Queen of Hearts

19:39

preside over a trial. The Queen gets

19:41

angry and orders Alice to be sentenced to

19:43

death. Alice wakes up to find her

19:45

sister by her side.

19:48

So that's pretty remarkable. It

19:50

took a whole book, and it was able

19:52

to summarize it down to a paragraph. But

19:55

I kept reading these text summaries

19:57

carefully, and I got to the summary

20:00

of Act one of Romeo and Juliet,

20:02

and here's what it says. Romeo

20:05

locks himself in his room, no

20:07

longer in love with rosalind Now,

20:10

I think the engineers at open Ai felt

20:12

really satisfied with this summary. They

20:14

thought it was quite good, and my proof for

20:16

this is that they still display it

20:18

proudly on their website. But

20:21

I majored in literature as an

20:23

undergraduate, and I spend a lot of time with shakespeare

20:25

plays, and I immediately knew that

20:27

this summary was exactly wrong.

20:30

The actual scene from Shakespeare goes

20:32

like this. His friend ben Voglio finds

20:35

Romeo catatonically

20:37

depressed, and ben Volio says,

20:40

what sadness lengthens Romeo's

20:42

hours? And Romeo says, not

20:45

having that which having makes

20:47

them short? And ben Volio says

20:50

in love, and Romeo says out

20:53

ben Reli says of love, and Romeo

20:55

says out of her favor, where

20:57

I am in love? This

21:00

this is typical Shakespearean wordplay, where

21:02

Romeo is expressing his

21:04

grief of being out of favor

21:07

with Roslin, with whom he is

21:09

deeply in love. And when you read

21:11

the play, it's obvious that Romeo is

21:14

not over Roslin. He's suffering over

21:16

her. He's almost suicidal. And this

21:18

is an important piece of the play, because

21:21

the play is really about a young man in

21:23

love with the idea of being in love,

21:26

and that's why he later

21:28

in the same act, falls so hard into

21:30

his relationship with Juliet, a

21:32

relationship which ends in their mutual

21:34

suicide. By the way, as Friar Lauren

21:37

says of their relationship, these

21:39

violent delights have violent ends.

21:42

And you get a bonus if you can tell me where else you've

21:44

heard that line more recently. Okay,

21:46

anyway back to the AI summary, The

21:49

AI misses this wordplay

21:51

entirely, and it concludes

21:53

that Romeo is out of love

21:56

with Roslin. Again, a

21:58

human watching the play or reading

22:00

the play immediately gets that

22:02

Romeo is making wordplay and his heartbroken

22:05

over Roslin, but the AI doesn't

22:08

get that because it's reading words

22:10

only at a statistical level, not

22:13

at a level of understanding of

22:15

what it is to be a human saying

22:18

those words. And that leads

22:20

me to the third example, which

22:22

is the difficulty in understanding

22:24

the physical world. So consider

22:26

a question like this, When President

22:29

Biden walks into a room, does

22:32

his head come with him? So

22:34

this is famously difficult for AI

22:36

to answer a question like this, even though it's

22:38

trivial for you because the AI

22:41

doesn't have an internal model

22:44

of how everything physically hangs together

22:46

in the world. Last week, I was

22:48

at the TED conference and I heard a great talk

22:51

by Yegin Choi, and she

22:53

was phrasing this problem as AI

22:55

not having common sense. She

22:58

asked chat GPT the following question, it

23:01

takes six hours to dry six shirts

23:03

in the sun, how long does it take

23:05

to dry thirty shirts? And it

23:07

answers thirty hours.

23:10

Now you and I see that the answer should be six

23:12

hours, because we know the sun

23:14

doesn't care how many shirts are out there.

23:17

But chat GPT just doesn't get it

23:19

because despite appearances, it

23:21

doesn't have a model of

23:24

the world. And we've seen this sort

23:26

of thing for years. By the way, even in mind

23:29

blowingly impressive AI models

23:31

that do image recognition, they're so

23:33

impressive in what they recognize,

23:36

but then they'll fail catastrophically.

23:38

It's some easy picture making mistakes

23:40

that a human just wouldn't make. For example,

23:42

there's one picture where there's a boy holding a toothbrush

23:45

and the AI says it's a boy

23:47

with a baseball bat. Okay, so

23:49

there are things that AI doesn't do that well.

23:52

But that said, there

23:54

are other things that are mind

23:56

blowing, things that no

23:58

one expected it to do. And

24:00

this is why I mentioned in my previous episode

24:03

that we are in an era of discovery

24:07

more than just invention. Everyone's

24:09

searching and finding things that the

24:11

AI can do that nobody really

24:13

expected or foresaw, including

24:16

all the stuff that we're now taking

24:18

for granted, like oh, it can summarize

24:20

books or it can make art from

24:22

text. And I want to point out that

24:24

a lot of the arguments that people have been making

24:27

about AI not being

24:29

good at something, these arguments

24:31

have been changing rapidly. For

24:34

example, just a few months ago, people were

24:36

arguing that AI would make silly

24:38

mistakes about things, and it couldn't really understand

24:40

math and would get math wrong

24:43

and word problems. But in a

24:45

shockingly brief time, a

24:47

lot of these shortcomings have been mastered.

24:50

So it's yet to be seen what

24:52

challenges will remain and for

24:54

how long. So

25:13

the evidence I've presented so far is that AI

25:16

doesn't have a great model of what it's

25:18

like to be human, but that doesn't necessarily

25:21

rule out that it has sentience

25:24

or awareness, even if it's of another

25:27

flavor. It doesn't think

25:29

like a human, but maybe it

25:32

stif thinks so is

25:34

chat GPT having some sort

25:36

of experience? And

25:39

how would we know? In

25:42

nineteen fifty, the brilliant

25:44

mathematician and computer scientist Alan

25:46

Turing was asking this question,

25:49

how could you determine whether

25:51

a machine exhibits human

25:53

like intelligence? So he proposed

25:56

an experiment that he called the

25:58

imitation game. You've got a machine

26:01

AI that's programmed to simulate

26:04

human speech or conversation, and

26:06

you place it in a closed room, and

26:08

in a second room you have a

26:10

real human, but the doors are

26:12

closed, so you don't know which room

26:15

has which machine or human. And

26:17

now you are a person, the

26:19

evaluator, who communicates

26:22

with both of them via a

26:24

computer terminal or I think of a nowadays

26:26

like text messaging with both of them. So

26:29

you, the evaluator, engage

26:31

in a conversation with both closed

26:34

rooms, one of which has the machine

26:36

and one the human, and your job is simply

26:38

to figure out which is which, which

26:40

is the machine and which is the human. And the

26:42

only thing that you have to work

26:44

with are the texts that are going back and forth.

26:47

And if you, the evaluator, cannot

26:49

tell, that is the moment when

26:52

machine intelligence has finally

26:54

arrived at the level of human intelligence.

26:57

It has passed the imitation

27:00

or what we now call the Touring test.

27:04

And this reminds me of this great line

27:06

in the first episode of Westworld,

27:09

where the protagonist William is

27:11

talking to the woman who's outfitting him

27:13

for his adventure in Westworld and giving

27:16

him a hat and a gun and so on, and he

27:18

hesitantly asks, I hope

27:20

you don't mind if I ask you this question, but are

27:22

you real? And she says to

27:24

him, if you can't tell,

27:27

does it matter? So I brought

27:29

this up last episode in the context

27:31

of art, where we asked whether it

27:33

matters if the art is generated by

27:35

an AI or a human, But now

27:37

this question comes up in the context

27:39

of intelligence and sentence.

27:43

Does it matter whether

27:45

we can tell or not? Well, I

27:47

think we're way beyond the Turing test

27:49

nowadays, but I don't feel like it

27:51

gives us a good answer to the question of

27:54

whether the AI is intelligent

27:56

and is experiencing an inner life.

27:59

I mean, the Sturing test has been the

28:01

test in the AI world since the beginning.

28:04

Why is it the perfect test? No, but it's

28:06

really hard to figure out how to test

28:09

for intelligence. But we have to

28:11

be cautious about equating

28:14

conversational ability with sentience.

28:17

Why well, for starters, let's just

28:19

acknowledge how easy it is for

28:22

us to anthropomorphize.

28:24

That means to assign human

28:27

qualities to everything around us. Like we

28:29

give animals human names

28:31

and talk to them as though they are people,

28:34

and we project our emotions onto animals.

28:36

We make stories about animals

28:38

that have human like qualities,

28:41

and we have animals that talk and wear

28:43

clothes and go on adventures in these stories.

28:46

Every Pixar film that you

28:48

watch is about cars or toys

28:51

or airplanes talking and

28:53

having emotions, and we don't

28:55

even bad an eye at that stuff. We

28:57

can, in fact, just watch random

29:00

shapes moving around a computer

29:02

screen and we will assign intention

29:05

and feel emotion depending

29:08

on exactly how they're moving. If you're

29:10

interested in this, see the link on the podcast

29:12

page to the study by Heighter

29:14

and Simil in the nineteen forties where

29:17

they move shapes around on a screen. Okay,

29:20

now this is all related

29:22

to a point that I brought up in the last episode,

29:24

which is how easy it is to

29:26

pluck the strings on a human, or, as

29:29

the West World writers put it, how

29:32

hackable humans are. So

29:34

I bring all this up to say that just because

29:37

you think that an answer

29:39

sounds very clever or it sounds like a human

29:42

really tells us very little about whether the

29:44

AI is actually intelligent

29:47

or sentient. It only tells us

29:49

something about the willingness

29:51

of us as observers to

29:54

anthropomorphize, to assign

29:57

intention where there is none, Because

29:59

what chat GPT does is

30:01

take the structure of language very

30:03

impressively and spoon it back to us,

30:06

and we hear these well formed

30:08

sentences, and we can hardly

30:11

help but impose sentience

30:13

on the AI. And part of the

30:15

reason is that language

30:18

is a super compressed package that

30:20

needs to be unpacked by the

30:22

listener's brain for its meaning.

30:25

So we generally assume that when we send

30:27

our little package of sounds

30:29

across the air, that it unpacks

30:32

and the other person understands exactly what

30:34

we meant. So when I say justice

30:38

or love or suffering,

30:41

we all have a different sense in

30:43

our heads about what that means, because

30:46

I'm just sending a few phonemes across

30:48

the air, and you have to unpack those

30:50

words and interpret them within

30:52

your own model of the world. I'm

30:55

going to come back to this point in future episodes,

30:57

but for now, the point I want

31:00

to make is that a large language

31:02

model can generate text

31:04

statistically and we can be gobsmacked

31:07

by the apparent depth of it. But

31:09

in part this is because we cannot help

31:12

but impose meaning on the words that

31:14

we receive. We hear a particular

31:16

string of sounds and we cannot help

31:18

but assume meaning behind

31:21

it. Okay, so

31:23

maybe the imitation game is not really

31:25

the best test for meaningful

31:27

intelligence, but there are other

31:30

tests out there. Because while

31:32

the Turing test measures something about AI

31:35

language processing, it doesn't necessarily

31:38

require the AI to demonstrate

31:41

creative thinking or originality,

31:43

and so that leads us to the Loveless

31:46

test, named after Ada

31:48

Loveless, who is the nineteenth century mathematician

31:51

who's often thought of as the world's first computer

31:54

programmer. And she once said quote,

31:57

only when computers originate

31:59

things should be believed to

32:01

have minds. So the Loveless

32:04

test was proposed in two thousand and one,

32:06

and this test focuses on the creative

32:09

capabilities of AI systems. So

32:11

to pass the Loveless test, a

32:14

machine has to create an

32:16

original work, such as a piece

32:18

of art or a novel that it was not

32:20

explicitly designed to produce. This

32:23

test aims to assess whether

32:25

AI systems can exhibit creativity

32:28

and autonomy, which are key aspects

32:30

of what we think about with consciousness. And

32:33

the idea is that true sentience

32:35

involves creative and original

32:37

thinking, not just the ability

32:39

to follow pre programmed rules

32:41

or algorithms. And I'll just note

32:43

that over a decade ago, the scientist

32:46

A. Mark Rydel proposed the loveless

32:48

two point zero test, which gets the human

32:50

evaluator to specify the constraints

32:53

that will make the output novel

32:55

and surprising. So the example

32:58

that l used in his paper is,

33:00

quote, create a story in

33:02

which a boy falls in love with a girl, Aliens

33:05

abduct the boy, and the girl saves

33:07

the world with the help of a talking cat. But

33:10

we now know that this is totally trivial

33:13

for chat, GPTE or BARD

33:15

or any large language model.

33:17

And I think this tells us that these sorts

33:19

of games with making conversation

33:22

or making text or art are

33:24

insufficient to actually assess

33:26

intelligence. Why because it's

33:28

not so hard to mix things up to

33:31

make them seem original and intelligent

33:34

when it's really just doing a mashup.

33:37

So I want to turn to another test that

33:39

I think is more powerful than

33:41

the Turing test of the Loveless test, and

33:43

probably easier to judge, and

33:46

that is this, if a system

33:48

is truly intelligent, it

33:50

should be able to do scientific

33:53

discovery. A version

33:56

of the scientific discovery test was

33:58

first proposed by a scientist named

34:00

Shao cheng Xiang a few years

34:03

ago, and he pointed out that the

34:05

most important thing that humans do

34:07

is make scientific discoveries, and

34:10

the day our AI can

34:12

make real discoveries is

34:14

the day they become as smart as

34:16

we are. Now. I want to propose

34:18

an important change to this test,

34:21

and then I think we'll be getting somewhere. So

34:37

here's the scenario I'm envisioning. Let's

34:41

say that I ask Ai some question, a

34:43

question in the biomedical space

34:45

about what kind of drug would be

34:47

best suited to bind to this receptor

34:49

and trigger a cascade that causes

34:51

a particular gene to get suppressed. Okay,

34:54

So imagine that I ask that to chat GPT

34:57

and it tells me some mind

34:59

blowing, amazing clever answer,

35:02

one that had previously not been

35:04

known, something that's never been known

35:06

by scientists before. We would

35:08

assume naturally that it has done

35:10

some extraordinary scientific

35:13

reasoning, but that won't necessarily

35:16

be the reason that it passes. Instead,

35:19

it might pass simply because it's

35:22

more well read than I am, or

35:24

than any other human on the planet by literally

35:27

millions of times. So the way

35:29

to think about this is to picture a

35:32

typical giant biomedical

35:34

library, where there's some fact stored

35:37

at a paper and a journal over here on

35:39

this shelf in this book, and there's

35:41

another seemingly dissociated

35:44

fact over on this shelf seven stacks

35:46

away, and there's a third

35:48

fact all the way on the other side of the library,

35:51

on the bottom shelf, in a book

35:53

from nineteen seventy nine. And

35:55

it's almost infinitesimally

35:57

unlikely that any human could

36:00

even hope to have read one one

36:02

millionth of the biomedical literature, and

36:04

really really unlikely that she

36:06

would be able to catch those three

36:09

facts and hold them in mind at the same

36:11

time. But this is trivial,

36:13

of course, for a large language model with

36:16

hundreds of billions of nodes. So I

36:18

think that we will see new science

36:21

getting done by CHATGPT, not

36:24

because it is conceptualizing,

36:26

not because it's doing human like reasoning,

36:29

but because it doesn't know that

36:31

these are disparate facts spread

36:33

around the library. It simply knows these as three

36:36

facts that seem to fit together. And so

36:38

with the right sort of questions,

36:41

we might find that sometimes AI generates

36:43

something amazing and it seems

36:46

to pass the scientific discovery test.

36:49

So this is going to be incredibly useful for

36:51

science. And I've never been able

36:53

to escape the feeling as I

36:55

sift through Google scholar and the

36:57

thousands of papers published each month that

37:00

have something could hold all the

37:02

knowledge and mind at once, each

37:05

page in every journal, and

37:07

every gene in the genome, and all

37:09

the pages about chemistry and physics and

37:11

mathematical techniques and astrophysics

37:13

and so on. Then you'd have lots

37:15

of puzzle pieces that could potentially

37:17

make lots of connections. And you

37:19

know this might lead to the retirement of many

37:22

scientists, or at minimum

37:24

lead to a better use of our

37:26

time. There's a depressing sense

37:28

in which each scientist, each one of us, finds

37:31

little pieces of the puzzle, and

37:34

in the twinkling of a single human

37:36

lifetime, a busy scientist might collect

37:38

up a handful of different puzzle pieces.

37:41

The most voracious reader,

37:44

the most assiduous worker,

37:46

the most creative synthesizer of ideas,

37:48

can only hope to collect a small

37:50

number of puzzle pieces and

37:53

pray that some of them might fit together. So

37:55

this is going to be massively important. But

38:00

I wanted to find two categories

38:02

of scientific discovery. The first is what I

38:05

just described, which is science where

38:07

things that already exist in literature can

38:09

be pieced together. And let's call

38:11

that level one discovery. And

38:14

these large language models will be awesome

38:16

at level one because they've read every paper and

38:18

they have a perfect memory. But I want to distinguish

38:21

a second level of scientific discovery,

38:24

and this is the one I'm interested in. I'll

38:26

call this level two, and that

38:28

is science that requires conceptualization

38:32

to get to the next step, not just

38:34

remixing what's already there. Conceptualization

38:37

like when the young Albert Einstein

38:40

imagined something that he had never

38:42

seen before. He asked himself, what would

38:44

it be like if I could catch up with a

38:46

beam of light and write it

38:49

like a surfer riding a wave. And

38:51

this is how he derived this

38:53

special theory of relativity. This

38:56

isn't something he looked up and found

38:58

three facts that clicked. Again, he

39:01

imagined he asked new

39:03

questions. He tried out a new model

39:06

of the world, one in which time

39:08

runs differently depending on how fast you're going,

39:11

and then he worked backwards to see

39:13

if that model could work. Or

39:15

consider when Charles Darwin thought

39:18

about the species that he saw around

39:20

him, and he imagined all the species

39:22

that he didn't see but who might have existed,

39:25

and he was able to put together a new

39:28

mental model in which most

39:30

species don't make it and

39:32

we only see those whose

39:34

mutations cause survival advantages

39:37

or reproductive advantages. These

39:39

weren't facts that he just collected

39:41

from some papers. He was trying out

39:44

a new model of the world. Now

39:47

this kind of science isn't just for

39:49

the big giant stuff. Most meaningful

39:51

science is actually driven by this kind

39:54

of imagination of

39:56

new models. Just as

39:58

one example, I recently in an episode

40:00

about whether time runs in

40:02

slow motion when you're in fear for

40:05

your life. And so when I wondered

40:07

about this question, I realized

40:09

there were two hypotheses that might

40:11

explain it, and I thought up an experiment

40:14

to discriminate those two hypotheses. And

40:16

then we built a wristband that flashes

40:19

information at a particular speed and

40:21

had people wear, and we dropped them from one hundred

40:23

and fifty foot tall tower into a net below.

40:26

A large language model presumably

40:29

couldn't do that because it's just

40:31

playing statistical word games. And

40:34

unless someone had thought of that experiment

40:36

and written it down, JATGPT

40:38

would never say, Okay, here's a

40:40

new framework, and how we can design an

40:43

experiment to put this to the test. So

40:45

this is what I wanted to find as the most

40:48

meaningful test for a human

40:50

level of intelligence. When

40:52

AI can do science in

40:55

this way, generating new

40:57

ideas and frameworks, not just clicking

41:00

act together, then we

41:02

will have matched human intelligence.

41:08

And I just want to take one more angle on this to make

41:10

the picture clear. The way a scientist

41:13

reads a journal paper is

41:15

not simply by correlating words

41:17

and extracting keywords, although that

41:19

might be part of it, but also by realizing

41:22

what was not said. Why

41:24

did the authors cut off the

41:26

x axis here at thirty What if

41:29

they had extended this graph, would the

41:31

line have reversed in its trend?

41:33

And why didn't the authors mention the hypothesis

41:36

of Smith at all? And does

41:38

that graph look too perfect? You

41:40

know? One of my mentors, Francis Krik

41:43

operated under the assumption that

41:45

he should disbelieve twenty five percent

41:47

of what he read in the literature. Is

41:49

this because of fraud or error,

41:52

or statistical fluctuations or manipulation

41:54

or the waste basket effect? Who cares? The

41:57

bottom line is that the literature

41:59

is rife with errors, and

42:01

depending on the field, some estimates

42:04

put the ireproducibility

42:06

at fifty percent. So when

42:08

scientists read papers they

42:11

know this, just as Francis Crick did. They

42:14

read in an entirely different

42:16

manner than Google Translate

42:18

or Watson or chat GPT or

42:20

any of the correlational methods they

42:23

extrapolate. They read

42:25

the paper and wonder about other possibilities.

42:28

They chew on what's missing. They envision

42:30

the next step. They think of the

42:33

next experiment that could confirm

42:35

or disconfirm the hypotheses and

42:37

the frameworks in the paper. To my

42:39

mind, the meaningful goal of AI

42:42

is not going to be found in number crunching

42:45

and looking for facts that click together.

42:47

It's going to often be something else.

42:50

It's going to require an AI that learns

42:53

how humans think, how

42:55

they behave, what they don't

42:57

say, what they didn't think of, what

42:59

they misthought about, what they

43:01

should think about. And one more thing,

43:04

I should note that these different levels I've outlined,

43:07

from fitting facts together versus

43:09

imagining new world models, they're

43:11

probably gonna end up with blurry boundaries.

43:14

So maybe chat GPT will

43:17

come up with something, and you won't

43:19

always know whether it's

43:22

piecing together a few disparate

43:24

pieces in the literature what I'm calling

43:26

level one, or whether

43:28

it's come up with something

43:31

that is truly a new world model

43:33

that's not a simple clicking together but a genuine

43:37

process of generating a new framework

43:39

to explain the data. So distinguishing

43:42

the levels of discovery is

43:44

probably not going to be an easy task with a

43:46

bright line between them, but I

43:49

think it will clarify some things to

43:51

make this distinction. And

43:53

last thing, I don't necessarily

43:55

know that there's something magical and ineffable

43:58

about the way that humans do this. Presumably

44:01

we're running algorithms too, it's

44:03

just that they're running on self configuring

44:05

wetwear. I have seen tens

44:07

of thousands of science experiments in my career,

44:10

so I know the process of asking

44:12

a question and figuring out what

44:14

we'll put it to the test. So we may

44:17

get to level two and it may be sooner than

44:19

we expect, but I just want to be clear

44:21

that right now we have not figured

44:23

out the human algorithms. So the

44:26

current version of AI, as

44:28

massively impressive as it is, does

44:31

not do level two scientific

44:33

problem solving. And that's when we're

44:35

going to know that we've crossed a

44:38

new kind of line into a

44:40

machine that is truly intelligent.

44:43

So let's wrap up. At least for now.

44:45

Humans still have to do the science,

44:47

by which I mean the conceptual

44:50

work, wherein we take a framework

44:52

for understanding the world and we rethink

44:54

it and we mentally simulate

44:56

whether a new model of the world

44:58

could explain the observed data, and

45:01

we come up with a way to test that new model.

45:03

It's not just searching for facts. So

45:05

I'm definitely not saying we won't get to the next

45:07

level where AI can conceptualize

45:10

things and predict forward and build new knowledge.

45:13

This might be a week from now, or it might be a

45:15

century from now. Who knows how hard

45:17

a problem that's going to turn out to be. But

45:19

I want us to be clear eyed on where we

45:21

are right now, because sometimes

45:24

in the blindingly impressive light

45:27

of what current AI is doing, it

45:29

can be difficult to see, what's missing

45:32

and where we might be heading. That's

45:38

all for this week. To find out more

45:40

and to share your thoughts, head over to eagleman

45:43

dot com slash Podcasts, and

45:45

you can also watch full episodes of Inner

45:48

Cosmos on YouTube. Subscribe

45:50

to my channel so you can follow along each

45:52

week for new updates. I'd love

45:54

to hear your questions, so please

45:56

send those to podcast at

45:58

eagleman dot com and I will do

46:01

a special episode where I answer questions.

46:03

Until next time. I'm David Eagelman

46:06

and this is Inner Cosmos