0:00

Hi listeners. This is the eighty thousand hours podcast

0:02

where we have unusually in-depth conversations about the

0:04

word's most pressing problems, what you can do to solve

0:06

them, and what to do if your robot dog tells

0:08

you they're conscious. I'm Rob Woodland,

0:10

Head of Research in eighty thousand hours. Do

0:13

you like the show, but wish there were more of it?

0:15

If so, I have some good news for you. The

0:17

Wizard GPT has recently joined the podcasting

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team at eighty thousand hours as a second host,

0:21

which means we should be able to talk with

0:23

more people about world's most pressing

0:26

questions and problems than we've ever

0:28

been able to manage before. You might

0:30

remember Louisa from episode one hundred and sixteen.

0:32

When she just recently started working here

0:34

as a research analyst and came on as a guest

0:36

to talk about why some global catastrophes

0:39

seemed unlikely to cause human extinction.

0:41

When Kew and I decided we wanted to grow the

0:43

team, Louisa was the person we were most excited

0:46

to work with. So if you're a fan of the eighty thousand

0:48

hours podcast, who joining us should definitely

0:50

be cause for celebration. Today's interview

0:52

is Louise' first time in hosting chair

0:54

interviewing the philosopher Rob Long on

0:56

the question of machine consciousness. Is

0:59

there something that it's like to be a large language

1:01

model like chest GPT? How could

1:03

we ever tell if there was? To what extent

1:05

does the wage chat TBD processes information

1:08

resemble what we humans do? Why

1:10

might future machine consciousnesses have a much

1:12

wider range of emotional experiences than humans

1:14

are capable of? And is the bigger risk

1:16

that we end up thinking AI is conscious when

1:18

it's not? Or that we think it isn't when actually

1:21

it is? Those are the sorts of questions

1:23

that Louisa puts her up. For the

1:25

first time in a while, I got to enjoy listening

1:27

to this episode more like typical subscriber

1:29

who hadn't just done a whole lot background research

1:31

on just that topic. And as a result,

1:33

I felt like I was actually learning a ton about

1:35

this really important issue that I hadn't yet had

1:37

any reason to think much about. If

1:40

Louisa can do interviews this good right off the bat,

1:42

both you and I have much to look forward to.

1:44

After they finish talking about AI, Louisa

1:46

and Rob kept going and recorded a conversation

1:49

for our other show, eighty k after hours.

1:51

This time about how to make independent research

1:53

work more fun and

1:54

motivating. Challenge that both of them have

1:56

had to deal with themselves over the years. You

1:58

can find that forty minute conversation by subscribing

2:00

to ADK after hours in any podcasting up

2:03

or clicking the

2:03

link in the share notes. Alright. With that

2:05

further ado, I present the Weezer GPT and

2:07

blah blah.

2:20

Today, I'm still you, miss Robert Long. Rob

2:23

is a philosophy fellow with the Center for

2:25

AI Safety, where he's working on philosophical

2:27

issues of aligning AI systems with

2:29

human interests. Until recently, he

2:32

was a researcher at the Future of Humanity

2:34

Institute, where he led the Mines

2:37

GPT group, which works on AI conscious ness and

2:39

other issues related to artificial minds.

2:41

Rob studied social studies at Harvard and

2:44

has a master's in philosophy from Brandeis

2:46

University and PHD from NYU

2:49

During his PHD, he wrote about philosophical

2:51

issues in machine learning under the supervision

2:54

of David Chalmers, who listeners might remember

2:56

hearing on our show before. On

2:59

top of that, I'm very privileged to call

3:01

Rob one of my closest friends. But

3:03

somehow, in spite of being very

3:05

good friends, Rob and I have actually

3:07

never talked much about his

3:09

research, so I'm really excited

3:11

to do this today. Thanks for coming on the podcast,

3:13

Rob.

3:14

Thanks so much, Lisa. I'm really excited

3:16

to talk with you. Well, I'm excited

3:18

to talk about how likely AI systems

3:20

are to become sentient and

3:22

what that might look like and kind of

3:24

what it would mean

3:25

morally. But first, what are you working

3:27

on at the moment and why do you think it's important?

3:30

Yeah. This is a great question for the

3:32

beginning of the year. I've been working on

3:34

a variety of stuff related to consciousness and

3:36

AI. So one I'm especially excited about

3:39

right now is me and a colleague

3:41

at Future Command Institute, Patrick Butler,

3:44

have been working on this big multi

3:46

author report where we're getting a bunch of

3:49

neuroscientists and AI

3:51

researchers and philosophers together to

3:53

produce a big report about what

3:55

the current scientific evidence is about

3:57

sentience and current and near term AI systems.

4:00

I've also been helping Jeff Siebel with the

4:02

research agenda for a very exciting new center

4:05

at NYU called the Mind Ethics -- Cool.

4:07

-- and Policy Center. Diane, cool. And,

4:10

yeah, just to keep myself really busy.

4:12

I'm also really excited

4:14

to do kind of a technical sprint

4:16

on leveling up my skills in

4:19

machine learning and AI safety.

4:21

That's something that's, like, predominantly on my

4:23

to do list. And I've always been kind of

4:25

technical AI safety

4:26

curious. So that's kind of a big

4:28

change for me recently. It's also shifting more

4:30

into that. Oh, wow. Cool. Okay. So,

4:33

yeah, I'll probably ask you more about that later, but

4:35

it sounds like on top of AI

4:37

sentient and AI consciousness, you

4:40

you're like, let's add AI safety to the mix

4:42

too. How can I solve

4:43

that? Yeah. To be clear, I do see them as

4:45

as related. You're gonna think about a lot of the

4:47

same issues and need a lot of the same technical

4:49

skills think clearly about both of

4:51

them. Okay. Well, we'll come back to that.

4:53

Yeah. To start, I wanted to ask,

4:56

yeah, a kind of basic question. I

4:59

basically don't feel like I have a great sense

5:01

of what artificial sentience would even

5:03

look

5:03

like. Can you help me get a picture of what

5:05

we're talking about here? Yeah. I mean,

5:07

I think it's absolutely fine

5:09

and correct to not know what it would look like.

5:12

In terms of what we're talking about, I think the

5:14

short answer or like a short hook

5:16

into it is just think about the problem of animal

5:18

science. I think that's structurally very

5:21

similar. So we share

5:23

the world with a lot of non human animals and

5:26

they look a lot different than we do. They

5:28

act a lot differently than we do. They're

5:30

somewhat similar to us. We're made

5:33

of the same stuff. They have brains. But

5:35

we often face this question

5:37

of as we're looking at a B

5:39

going through the field. Like, we can tell

5:41

that it's doing intelligent behavior, but we also wonder

5:44

Is there something it's like to be that

5:46

be? Like and if so, what are his experiences

5:48

like? And what would that entail for how we should,

5:50

like, treat bees or try to share the world

5:52

with bees? I think the general

5:55

problem of AI sense is that

5:57

question and also harder.

5:59

So I'm thinking of it in terms of

6:01

there's this kind of new class of

6:04

intelligent or intelligent seeming complex

6:06

system. And in addition to

6:08

wondering what they're able to do and how

6:10

they do it. We can also, I think, wonder

6:13

if there is or will ever be something that

6:15

it's like to be them. And if they'll have experiences,

6:18

if they'll have something like pain or pleasure, It's

6:20

a natural question to occur to people. And

6:22

it's it's occurred to

6:23

me. And I've been trying to work on it in

6:25

the past couple of years. Yeah.

6:27

I guess I have almost even

6:29

more basic question, which is like

6:32

yeah. When we talk about ASENTIENTS, both

6:35

kind of in the short term and in the long term,

6:38

are we talking about like a

6:40

thing that looks like my laptop that

6:42

has like a code on it that like

6:44

has been coded to have

6:47

some kind of feelings or experience?

6:50

Yeah. Sure. I think I use the term

6:52

artificial sentence. So, like, very

6:54

generally, it's just like things that

6:56

are made out of different stuff than us,

6:58

and in particular, silicone and,

7:00

like, the computational hardware

7:03

that we run these things on. Could

7:05

things built out of that and running

7:07

computations on that have experiences?

7:09

So, like, the most straightforward case to imagine

7:11

would probably be a robot because there,

7:14

you can kind of clearly think about

7:16

what the the physical system is that

7:18

you're trying to ask if it's sentient. Things

7:20

are lot kind of more complicated with

7:23

the more disembodied AI systems of

7:25

today, like, chat, UPT. Because

7:27

there, it's like a it's like

7:29

a virtual agent in a certain sense.

7:32

And brain emulations would also be

7:34

like virtual agents. But I think

7:36

for all of those, you can ask at some

7:38

level of description or some way of carving up the

7:40

system

7:41

Like, is there any kind of subjective experience

7:43

here? Is there consciousness here? Is there sentient

7:45

here? Yeah. Yeah. Cool.

7:47

Jumping in quickly to distinguish

7:50

between what we're calling phenomenal

7:52

consciousness or consciousness in

7:54

this episode, which is basically the

7:56

experience of having subjective experience

7:59

as opposed to something like,

8:01

I don't know, blood pumping through

8:03

my body, like, that's happening,

8:05

but I'm not subjectively conscious

8:08

of it. In contrast with, I

8:10

don't know, like, the feeling

8:13

of the sun on my

8:15

face or something, which I can't

8:17

have a subjective experience of. And

8:19

then we're also using the

8:21

term sentence. And when we say

8:23

sentence, we mean having

8:26

either positive or negative experiences. So

8:29

it's a type of conscious experience

8:31

that's in particular either

8:33

positive or GPT, like, pain or pleasure?

8:37

Yeah. I guess the reason I'm asking is

8:39

because yeah. I think I just have, like, for a long

8:41

time, had this sense that, like, when

8:44

people use the term digital minds or artificial

8:47

sentience. I have, like, some

8:49

vague images that kinda come from

8:51

sci fi. But I mostly feel

8:53

like I don't even know what we're talking about.

8:56

But it sounds like it could just look like

8:58

a bunch of different things and the

9:00

like core of it is something

9:02

that is sentient in maybe a way

9:05

similar, maybe a way that's pretty different to humans,

9:07

but that exists not

9:09

in biological

9:11

form, but in made up in

9:13

some in some grouping that's made up

9:15

of silicone. So

9:16

that should be right. And I should say, I guess,

9:18

like, silicon is not, like, that deep

9:21

here.

9:22

Sure. Sure. Yeah. Something having to do

9:24

with, like, running on computers, running on GPUs.

9:27

I'm sure I could slice and dice it and we

9:29

you could get in all sorts of philosophical, like,

9:32

classification terms for things. But,

9:34

yeah, that's the general thing I'm pointing at.

9:36

And I, in particular, have been working on

9:38

the question of AI systems. So

9:41

the questions about, like, whole brain emulation, I

9:43

think, would be different because we would

9:45

have something that at some level description

9:47

is extremely similar to the human brain

9:50

by definition. And then you could wonder about

9:52

whether it matters that it's an emulated

9:54

brain, and people have wondered

9:56

about that. In the case of AI is,

9:58

you know, even harder because not only are

10:00

they made on different stuff and

10:03

maybe somewhat virtual. They

10:05

also are kind of strange and

10:07

not necessarily working along

10:09

the same principles as the human brain.

10:12

Right. Right. Okay. That makes sense. I've

10:14

heard the case that if there are AI

10:16

systems that become sentient, there's

10:18

a risk of creating kind of astronomical

10:21

amounts of suffering. I still

10:23

have a really hard time understanding what

10:25

that might concretely look like.

10:28

Can you give, yeah, a kind of concrete example

10:30

scenario where where that's the case?

10:32

Yeah. So before getting to the, like, astronomical cases,

10:35

I'll start with more concrete case,

10:37

maybe of just one system. So you can

10:39

imagine that a robot has been created by

10:41

a company or by some researchers And

10:45

as it happens, it registers damage

10:47

to its body and processes it

10:49

in the way that as it turns out, is

10:51

relevant to, like, having an experience of

10:54

unpleasant pain. And maybe we don't realize

10:56

that because we don't have good theories of what's going

10:58

on in robot or what it takes to kill pain.

11:00

In that case, you can imagine that

11:02

thing having bad a bad

11:04

time because we don't realize it.

11:07

Right. You could also imagine this

11:09

thing being, like, rolled out, and now

11:11

we're economically dependent on systems

11:13

like this. And now we have an incentive

11:16

not to care and not to think

11:18

too hard about whether it might be having a bad

11:20

time.

11:20

Yeah. Yeah. So,

11:21

I mean, that seems like something that could happen.

11:24

Yeah. And that could happen because

11:27

I mean, there's some reason why it's helpful to

11:29

have the robot GPT that it's sustained

11:31

damage. It can, like, be,

11:33

like, help I've broken, I need someone

11:35

to fix my part. So that's, like, something

11:37

that you can like, might get programmed

11:40

in, and then, like, It is

11:42

just kinda wild to me that, like, we don't understand

11:45

what the robot might be experiencing

11:48

well enough to know, like, that thing

11:50

is pain. But, like, in dairy,

11:52

that's possible. Just like they're that

11:54

it's kind of that black boxy to

11:55

us. Yeah. So it might be little bit

11:57

less likely with a robot. But now

12:00

you can imagine more abstract

12:02

or alien ways of feeling bad. So

12:05

I focus on pain because it's like very

12:07

straightforward way of feeling bad. Yeah. A

12:09

disembodied system like a GPT

12:11

three, which we'll talk about. Obviously, can't

12:13

feel ankle pain or almost

12:16

almost certainly. Like, that'd be really weird. Doesn't have an

12:18

ankle. Right. Why would they have computations

12:20

that, like, representatives' ankle is feeling

12:23

bad? Mhmm. But you can imagine maybe

12:25

some strange form of balanced

12:27

experience that develops inside some system

12:29

like this that registers some kind of displeasure

12:32

or

12:32

pleasure, something like that.

12:33

Right. Right. Something like

12:35

And that could give you the wrong

12:38

set of words to come next,

12:40

and that was bad. And the

12:42

user isn't happy with

12:43

the, like, string of words he came up and then

12:45

that feels something like pain. Exactly.

12:47

Yeah. And I will note that

12:50

I don't think that getting

12:52

negative feedback is going to be enough

12:53

for, like, that bad feeling. Fortunately.

12:56

Yeah. But maybe some combination

12:58

of that and some way it's ended up representing

13:00

it inside itself ends up like

13:02

that. And then, yeah, then we have

13:05

something where it's hard for us

13:07

to map its internals

13:09

to what we care about.

13:12

We maybe have various incentives not

13:15

to look too hard at that question.

13:17

We have incentives not to let it speak

13:20

freely about if it thinks

13:22

it's conscious -- Mhmm. -- because, like, that

13:24

would be a big headache. Mhmm. And because

13:26

we're also worried about systems lying

13:29

about being conscious and giving misleading statements

13:31

about whether they're conscious, which they did they

13:33

definitely do. Yeah. So

13:35

we've built this new kind of alien minds

13:38

we don't really have a good theory of pain even

13:40

for ourselves. We don't have a good theory of what's

13:42

going on inside it. And so that's like a

13:44

that's sort of like a stumbling into this. Sort

13:47

of scenario. Yeah. That's not yet astronomical.

13:50

Yeah. So one reason I I started with the the concrete

13:53

case is I think people who

13:55

are worried about risks

13:57

of large scale and

13:59

long term suffering what

14:02

are sometimes called S risks or suffering

14:04

risks? I think they have

14:06

scenarios that involve,

14:08

like, very powerful agents making

14:10

lots of simulations for various

14:13

reasons and the simulations containing suffering.

14:15

I'll just refer people to that work because

14:18

I that's actually not my,

14:20

like, my bag. haven't thought that

14:22

much about those scenarios. Just

14:24

for my interest, what's the basic

14:27

argument for why anyone would wanna

14:29

create simulations with a bunch of suffering in

14:30

them? Yeah. So this is my

14:33

take, and it might not represent They're cheap positions.

14:35

I think one reason you could

14:37

create simulations because you wanna

14:39

learn stuff. So Imagine

14:42

that we were curious how evolution

14:44

would go if something had gone slightly differently.

14:46

Right. Okay. And imagine we had,

14:48

like, planet sized computers. So we could,

14:51

like, just literally rerun, like,

14:53

all of evolution down to the detail so that

14:55

there are, like, virtual creatures --

14:57

Yeah. Yeah. Yeah. -- and reproducing and stuff.

14:59

And also, I suppose that a simulated creature

15:02

is

15:02

sentient, which, you know, is is plausible.

15:05

Yeah. Yeah. Then all your all you really

15:07

are looking for is, like, at the end did the simulation

15:09

output, like -- Right. -- so hominids or

15:11

something. Yeah. Yeah. GPT. You've

15:14

also have, like, billions of years of animals, like,

15:16

eating each other. Totally. Stuff like

15:18

that. Yeah. Okay. Right. But it sounds like

15:20

there's also just, like, we, like, make

15:22

things for, like, economic reasons,

15:24

like robots or like chatbots.

15:27

And we don't realize those things are suffering.

15:30

And then we, like, mass produce them because

15:32

they're valuable. And then that mass production

15:35

isn't astronomical. In

15:37

scale, but it's, like, big and,

15:40

like, those things are suffering. We didn't know it, and

15:42

they're, like, all over. And we don't really wanna change

15:44

anything about those systems because

15:47

we use them. Yeah. I mean, for just

15:49

another dark, dark scenario, you can

15:51

imagine a system where we get pigs

15:53

to be farmed much more efficiently.

15:56

And we're just like, well, this is made

15:58

a meat cheaper. Let's not too much

16:00

about

16:01

that. Totally. Got it.

16:03

Yeah. Yeah. Yeah. Okay. Yeah.

16:05

Are there any other examples you think are plausible

16:07

here, or are those kind of the main

16:08

ones? I guess 146 thing I should note

16:10

is I've been focusing on this, like,

16:13

case where we've hit on it accidentally.

16:15

There are a lot of people who are interested

16:18

in building artificial consciousness.

16:20

Mhmm. On purpose Not understandably so.

16:22

You know, it's, like, just from a purely

16:24

intellectual or philosophical standpoint.

16:26

Fascinating project, and it can help

16:28

us understand the nature of consciousness. So

16:31

for a very long time, probably

16:33

about as old as AI, people are like, wow,

16:35

I wonder if we could make this thing conscious.

16:38

Right. So there was a recent recent New York

16:40

Times article -- Yeah. -- about roboticists

16:43

who Yeah. want to build more

16:45

self awareness into robots, both for

16:47

the intrinsic scientific interests and also

16:49

because it might make for better robots. And

16:52

some of them think, oh, well, like, we're not actually

16:54

that close to doing that and maybe, like,

16:56

yeah, it's too soon to worry about it. Another

16:59

person quoted in that article is, like,

17:01

yeah, it's something to worry about, but, like, we'll

17:03

deal with it. And, yeah,

17:06

I'm I am quoted in that piece as

17:08

just kind of being, like, be

17:11

careful, like, slow

17:13

down.

17:14

Like, we're not really ready to to

17:16

deal with?

17:17

To quote unquote deal with that. Yeah. Yeah.

17:19

Yeah. Yeah. Exactly.

17:20

Okay. So so maybe it happens

17:23

because it's, like, useful for learning. Maybe

17:25

it happens because there are, like, some

17:28

reasons that someone might want to do

17:30

this intentionally to create suffering.

17:33

That's very dark. But then it could also just

17:35

happen accidentally, which yeah.

17:37

All of which kind of terrifies me. And

17:40

I wanna come back to that. But first,

17:42

I wanted to ask about the kind of yeah, flip

17:44

side of this, which is not only

17:46

my AI systems be able to suffer,

17:48

but they might also be able to experience pleasure.

17:51

And I'm curious how their

17:54

pleasure might compare to

17:56

the pleasure that we feel as

17:57

humans. Bauchner: Yeah, the

17:59

short answer is, I think pleasure

18:02

or pain or whatever analogs of

18:04

that that AI systems could experience

18:07

could have a drastically different range than

18:09

ours. They could have a drastically different

18:11

sort of middle

18:12

point. Is there any reason to think the

18:14

default is that artificial

18:17

sentience feels pleasure and pain like

18:19

humans? Or or do you think the default is

18:21

something else? Yeah. I basically am

18:23

agnostic about what the default is. Okay.

18:25

And one reason is that

18:27

Well, let's first think about why the default is what

18:29

it is for humans.

18:30

Yeah. Great. It's a very vexing

18:32

and interesting question. So let's start with,

18:34

I think, one of the saddest facts about life, which

18:36

is that it's much easier to

18:38

make someone feel pain than

18:40

to make them feel really good. Here's

18:42

a dark thought experiment that I actually thought

18:44

about as preparation for this. Suppose

18:47

I'm gonna give you like a billion dollars

18:49

and a team of people who are experts

18:52

in all sorts of

18:52

things. And you have

18:54

the goal of making someone feel as good as possible

18:57

for a week.

18:57

Yep. Or imagine a different scenario

19:00

where I give you the goal of making someone feel

19:02

as bad as possible for a week.

19:03

Yeah. It seems much easier to do the

19:05

second goal. Totally. Right.

19:07

Yeah. That is really exciting. It seems

19:09

like in some ways, it might not really be

19:12

Like, you could still mess up the one week thing.

19:14

It's just like really hard to make people feel durably

19:16

good. Totally. Yeah. And

19:19

the bad is just like waterboard them

19:21

for a week.

19:22

Yeah. You took it there. But yeah.

19:24

Yeah. That's what Jeez. Yeah.

19:26

And, like, why is that the case?

19:28

Like, why are we creatures where it's so

19:30

much easier to make things go

19:32

really badly for us. 146,

19:35

like, line of thinking about this is

19:38

Well, like, why do we have pain and pleasure?

19:40

It has something to do with, like, promoting the

19:43

right kind of behavior to increase

19:45

our genetic fitness. Mhmm.

19:48

That's not to say that that's explicitly

19:50

what we're doing or and we in

19:52

fact don't really have that goal as

19:54

humans.

19:55

Like, it's not what I'm up to. It's not what

19:57

you're up to. Not entirely. Yeah.

20:00

But they should, like, kind of correspond to

20:02

it. And there's kind

20:04

of this asymmetry where it's really easy

20:07

to lose all of your expected offspring

20:09

in one go. If, like,

20:11

something eats your leg, then

20:13

you're, like, really in danger of,

20:16

like, having no descendants. Yeah. Yeah. And I could

20:18

be happening very fast. Uh-huh. In

20:20

contrast, there are, like, very few things that

20:22

all of the sudden drastically increase

20:25

your number of expected offspring. I

20:27

mean, even having sex, which I think

20:29

it's obviously on a coincidence that that's one of the

20:31

most, like, pleasurable experiences for

20:34

many people. Yep. Yeah. Even

20:36

that, like, you know, doesn't hugely

20:39

in any given go increase

20:41

the number of

20:42

descendants. And and did it for, like, eating a

20:44

good meal. Right.

20:47

Right. So if there was something

20:49

that were like, I don't know, some

20:52

some tree that made it possible to,

20:54

like, have twenty

20:56

kids in one pregnancy instead

20:59

of 146. Maybe we'd find eating

21:01

the fruit from that tree, like especially pleasurable.

21:04

But there just aren't that many things like that.

21:06

And so those things don't give us very

21:08

big rewards relative to the things to the many

21:11

things, I

21:11

guess, that could, like, really mess

21:13

up our survival or reproduction?

21:16

Is that basically the --

21:17

Yeah. -- closed? Yeah.

21:19

I actually have never I've just never thought

21:21

about that. It makes perfect sense. Yeah. It's like

21:23

very schematic, but I do think it is like a good

21:25

clue to thinking about these questions. So,

21:27

yeah, like, what what evolution wants for creatures

21:30

is pain and pleasure to, like, roughly

21:32

track those things. I mean, evolution also

21:34

doesn't yeah. It doesn't want you to experience agony

21:37

every time you, like, don't talk

21:39

to a potential mate. Like, it doesn't allow you to be wracked

21:41

with pain. Right. Because, like, that's distracting

21:43

and it takes cognitive resources and

21:45

stuff like that. So, like, that's another piece of it. It

21:47

needs to, like, kind of balance the energy

21:50

requirements and cognitive requirements of that.

21:52

Mhmm. I definitely recommend that readers

21:54

check out work by rethink priorities.

21:57

On trying to think about what the, like, range

21:59

of balanced experiences for different

22:01

animals are based on

22:02

this. Can you give me the rough overview

22:05

of what they try to do? Like, what their

22:07

approach is?

22:08

Yeah. So they're looking at

22:10

considerations based on the

22:13

sort of evolutionary niche that different

22:15

animals are in. Wow. As

22:17

as one thing, like -- Mhmm. -- there are reasons

22:19

to expect differences between animals

22:22

that have different kind of like offspring strategies.

22:25

Right. And then also just

22:27

more direct arguments about,

22:29

like, what are the attentional resources

22:32

of this animal? Like, does it have memory

22:34

in a way that might affect its experiences? Mhmm.

22:37

Here's an interesting 146. do social

22:39

animals have different experiences of

22:40

pain? Because social animals

22:43

it's very helpful for them to cry

22:45

out. Right. They'll get

22:46

helped by her. Yeah.

22:48

Pray animals have an incentive

22:50

not to show pain. Because that

22:52

will sound nice. Fascinating.

22:55

And, like, that might

22:57

just really lead to big differences in

23:00

how much pain or pleasure these animals

23:02

feel?

23:02

I think that's the thought. Yeah.

23:04

That's really cool. It's really fascinating. Yeah.

23:06

I'm sure everyone's seen a kid that

23:08

has fallen over and

23:11

it doesn't freak out until it

23:13

knows that someone's What do you

23:15

do? Oh, got it. Yeah. Yeah. Yeah. Yeah. Yes. Yes.

23:17

True. That's not to say that the pain is different in

23:19

each case. Like, I I don't know. I don't think anyone

23:21

knows, but that's an illustration

23:23

of the social animal kind of

23:26

programming.

23:26

Totally. Totally. Okay. So,

23:29

I guess, by extension

23:31

yeah. You could think that, like, the

23:34

kind of selection pressures that

23:37

an AI system has or doesn't

23:39

have or something about its

23:40

environment. Might affect

23:43

kind of its emotional range? Is that is that

23:45

basically the Yeah. It's something

23:47

like we seem to have some

23:49

sort of partially innate or

23:51

baked in, like, default points

23:54

that we then deviate from

23:57

on either end. It's very

23:59

tough to know what that would mean for an

24:01

AI system. Obviously, AI

24:03

systems have objectives that they're seeking

24:05

to optimize. But it's

24:07

less clear what it is to say it's kind

24:09

of default expectation of how well it's

24:11

gonna be doing such that if it

24:14

does better, it will feel GPT. If it does worse,

24:16

feel bad. I think

24:18

the key point is just to notice that

24:20

maybe, and this could be a very

24:22

good thought. This kind

24:25

of asymmetry between pleasure and pain

24:27

is not like universal law of consciousness

24:29

or something. Got it. Right. Okay? So

24:31

the so the fact that humans have

24:33

this kind of like limited pleasure side

24:35

of things, there's no like inherent

24:38

reason. That an AI system

24:40

would have to have that

24:41

cap. It could have There might be no

24:43

inherent reason we have to have that cap forever,

24:45

which is another wonderful thought. Right.

24:48

This is GPT post by Paul Cristiano, pointing

24:51

out that we're kind of fighting this battle against

24:53

evolution. Evolution doesn't want

24:55

us to find pleasure hacks because

24:58

it doesn't. It doesn't want us to to wire

25:00

head. So, like, that's

25:02

one reason you know, at a high

25:04

level, like, why we maybe habituate to

25:07

GPT. Sorry, wire tooling

25:09

is, like, some pack

25:11

to find pleasure that doesn't actually improve our fitness

25:14

or

25:14

something? Yeah. It means a lot of different things. was

25:16

using it yeah. I was using it loosely to mean that. Okay.

25:19

Yeah, that's maybe why we're always dissatisfied. Right?

25:21

Like, you've got a new job, you've

25:24

got cool friends, like, you know, you've got

25:26

social status, and eventually

25:28

your brain's like more, you know,

25:30

don't get complacent. And,

25:32

you know, we've tried various things to try

25:35

to try to work around that and find

25:37

sustainable ways to boost our well-being

25:39

permanently different cognitive techniques.

25:42

But, like, we're kind of this GPT,

25:45

we're kind of fighting, like, an adversarial game.

25:48

That's really interesting. Yeah.

25:50

And then I guess so I guess it's

25:52

both kind of we don't know where the default point is.

25:55

We also don't know what the upper

25:57

bound and lower bound might be on pleasure

25:59

and pain. It might be similar

26:01

to ours, but many

26:03

of the pressures that might push ours to be what

26:05

they are may or may not exist for an AI

26:07

system, and so they could just be really different.

26:10

Exactly. Cool. Yeah. That's what fun. That's

26:12

wild. Yeah. Are there any other kind

26:14

differences between humans and AI systems

26:16

that might be in AI systems feel kind

26:18

of more or different kinds of pleasure

26:21

than

26:21

humans? Well, yeah. I mean, one thing I'll note

26:23

is that I'm often using

26:25

bodily pain or

26:27

the pleasures of status or something

26:29

as my GPT. Mhmm. But it

26:31

it kind of goes without saying but I'm

26:33

saying it that Yeah. I mean,

26:36

AIs might not have anything, you know, corresponding

26:38

to that. You know? It would be really weird if they

26:40

feel, like, sexual satisfaction at this point.

26:42

Right. Right. Right. You know? Yeah.

26:43

Yeah. Yeah. Yeah. Makes sense. But then it's yeah.

26:46

It's and and you can wonder that we're venturing

26:48

into territory. We don't really know what we're

26:50

talking about. But, like, I think you can

26:52

in the abstract. Imagine valence.

26:54

Yeah. Valence just being a shorthand for, like, this

26:56

quality of pleasure, just pleasure. When I can imagine

26:58

valence, right, least I think I can. That's about

27:01

other kinds of

27:02

things. Yeah. Yeah. Yeah. Like,

27:04

to the extent that there are things

27:06

like goals and rewards

27:09

and other things going

27:11

on that motivate an AI

27:13

system. Maybe those things come with valence.

27:16

And, like -- Yeah. -- maybe they won't. But,

27:18

like, it might make sense for them too. Exactly.

27:20

I

27:21

guess argument I've heard for why

27:23

there might be a difference in kind of the amount

27:25

of pleasure and pain AI systems

27:27

could feel versus humans can feel is just

27:29

something like humans

27:32

require lots of resources right now.

27:34

Like, the the cost of living and the cost

27:36

of thriving and flourishing might just

27:38

be really high. And I

27:40

can imagine it just becoming super

27:43

super cheap for an

27:45

AI system or some kind of

27:47

digital mind feeling just

27:50

like huge amounts of pleasure, but

27:52

not requiring like a bunch of friends

27:54

and like housing. And

27:57

I don't know, romantic relationships. Like,

27:59

maybe it's just, like, relatively small

28:01

computer chips and they

28:03

just, like, get to feel enormous pleasure

28:06

really cheaply by, like, pushing,

28:08

like, the zero key or something. And

28:11

and so you might think that they could just experience

28:14

actually loads more pleasure than

28:16

could at least feel like GPT the same inputs?

28:18

Yeah. 146 thing I'll also note is

28:21

they could also experience the

28:23

higher pleasures cheaply too. Like, suppose

28:25

they do require friends and knowledge

28:27

and community and stuff. Maybe it's just a lot

28:29

cheaper to give that to them

28:30

too.

28:31

Yeah. Yeah. Yeah. Yeah. Right. And then there's

28:33

also cases like you said where maybe they have some

28:35

sort of alien pleasure and we're just

28:37

like turning the dial on that. I mentioned

28:39

the other case because, like, I think a lot of people

28:41

would be wary of finding it valuable

28:44

that you're, like, just, like, cranking

28:46

the dial on maybe some, quote unquote,

28:48

loader or pleasure or,

28:49

like, uninteresting pleasure. But even

28:52

more interesting pleasures could be a lot cheaper.

28:55

Right. Right.

28:55

It's it's it's cheaper for them to achieve

28:58

great things and Yep. contemplate

29:00

the internal truths of existence and have

29:03

friends and stuff like

29:04

that. And that could just be some basic

29:06

thing. Like, it's easier to make

29:08

more silicone things

29:11

than it is to build houses,

29:14

farm food, build cities,

29:16

etcetera. Like, you could just have computer

29:18

farms that, like, allow AI systems

29:21

have all the same experiences and maybe better

29:23

146. But, like, it might just cost

29:25

less. Yeah. That scenario

29:27

is possible. And I will go ahead

29:29

and disclaimer. Like, I don't think

29:31

that much about those scenarios right

29:33

now. Mhmm. And I'm also not, like,

29:35

build the servers, GPT. You

29:37

know? Okay. Okay.

29:39

Yeah. Given how life fraud and in the dark

29:41

we are about these questions, both morally

29:43

and and empirically. Totally. But, yes, I

29:46

I think it is possible. Here's another,

29:48

you know, another Black Mirror episode,

29:50

which I think is maybe my

29:51

favorite, is Santa Clara. Yeah.

29:54

Have you have you seen that one?

29:55

I have. Yeah. Do you wanna recap

29:58

it? Sure. Yeah.

30:00

This one said in the, like, somewhat near future

30:03

And this civilization seems

30:05

to have cracked making realistic

30:08

simulations. And it's possible for

30:10

people to go in those simulations while they're alive.

30:12

It's also possible for them

30:14

to be, like, transferred to them when they

30:17

die. And it's one of the rare black mirror

30:19

utopias. Spoil alert

30:22

before you continue listening. Yeah.

30:25

The, like, protagonist of the episode

30:27

ends up in a very great situation

30:30

at the end of the show. She ends up being

30:32

able to live with this this woman she loves

30:34

and in this cool like

30:36

beach town. And what

30:39

I love about the episode is it ends with this

30:41

happy ending. Of, like,

30:43

digital utopia. And then, like, the last

30:45

shot is this robot arm

30:48

putting

30:49

her little simulation in this huge

30:51

server bank and you see that it's just like this

30:53

entire warehouse of

30:55

-- Right. -- of simulations. Yeah. Yeah.

30:57

Yeah. And and

30:59

why is Did you like that episode? IIIII

31:01

think it's stunning, really moving. Yeah.

31:03

I think my think it's my favorite because

31:06

there's this, like, parody of Black Mirror, which

31:09

is, like, What if phones, but

31:11

bad? Yeah. Yeah. Yeah. Totally.

31:14

And sometimes it does veer into this kind of

31:16

like cheap dystopia, which is

31:18

not to say I'm not worried about dystopias, but --

31:21

Yeah. -- yeah. It's just like, what if

31:23

Facebook that plug directly into

31:25

your brain. Yeah. And

31:28

Holden Karnovsky has a great post about why

31:30

it's hard to depict utopias and hard to

31:32

imagine them in compelling way for

31:34

viewers. Mhmm. And this seems to

31:36

have, at least for me, like, solve

31:38

that problem. I'm, like, that is a it's not

31:41

the best possible

31:41

future, but it's a That's a good one.

31:44

Cool. Yeah. Yeah. Any any other

31:46

differences that you think are, yeah,

31:48

I guess, relevant to

31:50

the kinds of pleasure or the amount of

31:52

pleasure that AI systems might feel

31:54

relative to humans? Yeah, now might

31:56

be a good time to talk about sort of a grab

31:58

bag of complexing issues about artificial

32:00

minds. Right. So there's all

32:02

these philosophical thought experiments about, like,

32:04

what if people were able to split into?

32:07

And you make two copies of them. Which one

32:09

is really them? Or what have two people

32:11

merged? Like, what do we say about that case? And

32:13

I think those are cool thought experiments. Yeah.

32:15

AIs are, like, a lot easier to copy and, like,

32:18

I'm a lot easier to merge. Totally. So

32:21

it could be that we could

32:23

have real life GPT. Of these kind of philosophical

32:25

edge cases and things that have sort of distributed

32:28

selfhood or distributed agency. And

32:30

that, of course, would affect kind of

32:33

how to think about their well-being and stuff. In

32:35

ways that I find very hard to say anything meaningful

32:37

about, but

32:37

it's worth -- Right. -- think

32:38

it's worth flagging and worth people

32:40

thinking about.

32:41

Totally. Right? So with with

32:43

copies, it's something like does each copy

32:45

of an identical, I guess,

32:48

digital mind GPT, like,

32:51

equal moral weight? Like,

32:53

are they different people? And

32:55

do they get if they're, like, both happy?

32:57

Is

32:58

that, like, twice as much happiness in the world?

33:00

Yeah. I mean, I'm inclined to think I'm

33:02

inclined to think too. Yeah.

33:04

Like, there's a paper by Showman

33:07

and Bostrom college sharing the world with digital minds.

33:09

Mhmm. And, yeah, that thinks

33:11

about a lot of the sort of, like, political

33:14

and social implications of cases like this,

33:17

who which yeah. I I haven't

33:19

thought that much about myself, but there

33:21

would be, like, you know, interesting questions about,

33:23

like, the political representation of

33:25

copies. Like,

33:27

before there's some vote in

33:30

San Francisco, we wouldn't want me to be

33:32

able to just make twenty of me and then we'll also

33:34

vote. Right?

33:34

Totally. Yeah. Yeah. Yeah. I

33:37

mean, I don't know if there are twenty

33:39

of you. And do you You also

33:41

don't wanna disenfranchise someone back. Well, you're

33:43

just a copy. So, like, you know, your vote

33:45

now counts for one twentieth

33:48

as much. Yeah. Yeah. Yeah. I mean, do you

33:50

have a view on this? I I think I do have

33:52

the intuition that, like, have

33:54

the intuition that it's bad, but I think

33:56

when I look at it, I'm like, well,

33:58

no. There are just twelve robs who are gonna

34:01

get twelve Rob's worth of joy from,

34:03

like, a certain electoral

34:05

outcome. And, like, that's bad if, like,

34:07

there are only twelve Rob's because you're really rich

34:09

But I don't, like, hate the idea that there might

34:11

be more robs and that you might get twelve

34:13

more robs worth of votes.

34:15

And, yeah, I mean, I don't have strong views

34:17

about this, like, hypothetical of copying

34:19

and political representation. Yeah. But it does

34:22

seem like you would probably

34:24

want rules about when you're allowed copy. Because

34:26

in the run up to an election, you don't want an

34:28

arms race where the population

34:30

of the digital population of San Francisco

34:32

skyrockets because everyone 146

34:35

they're preferred candidate to

34:36

win. Yeah. Yeah. Yeah. I guess

34:38

also if you have to, like, provide

34:41

for your copies, if you have to, like,

34:43

split resources between your copies, you

34:46

might even kill your copies afterward.

34:48

Like, you might delete them because you're, like,

34:51

can't afford all these copies of myself. Yeah.

34:53

Thanks for the thanks for the vote. Thanks for the vote.

34:55

But, of course, if if I feel that way then but

34:58

necessarily all the copies do as

34:59

well. So they feel like

35:01

they also don't wanna share resources and are

35:03

happy to let one of you live.

35:06

You

35:06

mean? Well,

35:07

they're certainly not gonna be deferring

35:09

to the quote unquote original me because they

35:11

all feel like the original

35:13

me. Right. Right. Right. Right. Right. And so --

35:15

Yeah. --

35:15

so the eleven that let's

35:17

say the original u does keep

35:20

power

35:20

somehow. It like somehow has the power

35:22

to delete the other copies. And

35:25

Yeah. They'll all feel like the original

35:27

me. That's the that's another thing. Right? Well, they

35:29

would feel like it, but -- Yeah.

35:31

-- they might not actually be able to click

35:33

the button. To delete the

35:34

copies, but, like, maybe they originally you can.

35:36

Right. Yeah. Yeah.

35:37

And then that's you're murdering

35:40

eleven people. I

35:41

I mean, not me, you know. I I wouldn't do this. But

35:43

You might do that. You might do that. You would be

35:45

nurturing. I'm planning right now. I'm scheming.

35:48

I'm like, oh, sounds like a

35:50

great way to get the electoral the election

35:52

outcomes I want. Yeah.

35:55

How much does emerging data experiment apply

35:58

or like how relevant is

35:59

it? I guess I mostly mentioned the merging

36:01

case because it's like part

36:03

of the canonical battery

36:06

of thought experiments that are

36:08

supposed to make personal identities seem

36:10

a little less deep or kind of

36:13

perplexing if you really insist on there always

36:15

being some fact of the matter about which

36:17

person's exists and not -- Yeah. -- and

36:19

just like splitting, it's like something

36:22

that seems like it could

36:23

happen.

36:23

Yeah. Yeah. Yeah. Okay. So maybe

36:25

you after this election

36:28

try to merge your eleven copies back

36:30

with

36:30

yourself. And then what does that what

36:32

does that mean?

36:33

Yeah. Like, does that thing now still deserve

36:36

twelve votes or something? Right. Right. Right. Yeah.

36:38

Yeah. Okay. Interesting. Yeah.

36:40

I've I've I've never thought about that before.

36:42

So I guess, I feel like there are some

36:45

reasons to think that AI

36:47

systems or or I guess digital minds

36:49

more broadly. They might have

36:52

more capacity for suffering, but they might

36:54

also have more capacity for pleasure.

36:57

They might be able to kind

36:59

of experience that pleasure more cheaply than

37:01

humans. They might have,

37:03

like, a higher kind of pleasure

37:06

set point. So, like, on average,

37:08

they might be better off. Yeah. I GPT,

37:11

you might think that, like, it's

37:13

more cost effective. You can,

37:15

like, create happiness and well-being more

37:18

cost effectively to have a bunch

37:20

of digital minds than to have a bunch

37:22

of humans. How how do

37:24

we even begin to think about kind of what the moral

37:26

implications of that are?

37:28

Yeah. So I guess I will say but not

37:30

endorse the, like, one flat footed

37:32

answer. Okay. And this can go in, like, you

37:34

know, red letters around this. Like Yeah.

37:36

Sure. Yeah. You you could think.

37:39

Like, let's make the world as good as possible and

37:41

contain as much pleasure and as little pain

37:43

as possible. And like,

37:46

we're not the best systems for realizing

37:48

a lot of that. So our

37:51

job is to, like, kind of usher in a,

37:53

like, successor my GPT. Can experience

37:55

these these GPT? I think there are many

37:57

many reasons for not, like being overly

37:59

hasty about such a position. And,

38:01

like, people who've talked about this have have noticed

38:04

this. I mean, one is the in practice,

38:06

like, we're likely to face a lot of uncertainty

38:08

about whether we are actually creating something valuable

38:11

that, like, on reflection we would endorse. Yeah.

38:13

Yeah. Another one is that, you

38:15

know, maybe we have

38:17

the prerogative of just caring about

38:20

the kind of goods that exist

38:22

in, like, our current way

38:24

of existing. So, like, one thing

38:26

that that sharing the world with digital

38:28

minds mentions is that there

38:30

are, like, reasons to maybe look for some sort

38:32

of, like, compromise. Yeah.

38:33

Can you explain what that would look like? Yeah.

38:35

One extreme position is, like, the hundred

38:38

percent just replace some handover position.

38:40

And then The other extreme would be That's, like,

38:43

all of humans just like

38:45

decide voluntarily to give up

38:47

their stake in the resources

38:49

in the world. And they're just like digital minds

38:51

will be happier per tree

38:53

out there. And so let's

38:55

give them all the trees and all the all the

38:57

things. And -- Yeah. --

38:59

and we're just like Our time is done.

39:01

Yeah. Like,

39:02

cool. take it from here. Yeah.

39:04

And then there would be the

39:06

other extreme would be, like, no

39:08

humans forever. No trees

39:11

for the digital minds. Mhmm. And maybe

39:13

and so, like, maybe for that reason, don't build

39:14

them. Like, let's just stick stick

39:17

with what we know. Mhmm. Don't build artificial

39:19

sentence or or don't build, like,

39:21

a utopia of kind of digital

39:23

minds.

39:24

Yeah. A utopia that's, like, too different from --

39:26

Yeah. I have an experience. Then one thing you

39:28

might think is that you could get a lot

39:31

of what each position wants

39:33

with some kind of split.

39:35

So if the, like, pure replacement

39:38

scenario is motivated by this kind

39:40

of flat footed total

39:41

utilitarianism, which is like, let's just make

39:43

the number as high as possible. Yep.

39:45

You could imagine a scenario where you

39:47

give ninety nine percent of resources

39:50

to the digital minds. You leave one percent

39:53

for the humans. But then the

39:55

here's the thing is if you GPT I

39:58

don't know. This is like a very sketchy scenario. But

40:00

if you give one percent of resources

40:02

to humans is actually a lot of resources.

40:05

If giving lot of resources to

40:07

the digital minds creates tons of, like, wealth and

40:09

more resources.

40:10

Right. So is it something like

40:12

digital minds, in addition to

40:15

feeling lots of pleasure, are also

40:18

really smart and they figure out how to colonize

40:21

not only the solar system,

40:23

but like maybe the galaxy, maybe other

40:25

GPT, And then there's just like

40:27

tons of resources. And

40:31

so even just one percent of all those resources

40:33

still makes for a bunch of

40:34

humans. Yeah. I think that's the idea.

40:36

And bunch of human beings being. And so

40:40

on this, like, compromised position,

40:42

you're getting ninety nine percent of

40:44

what the total utilitarian replacement

40:47

wanted. And you're also getting a large

40:49

share of what the the humans

40:51

forever people wanted. And

40:53

you might want this compromise because of

40:55

moral uncertainty. You don't wanna just put

40:57

all of your

40:58

chips.

40:58

Right. Go all in. Yeah. And also

41:00

maybe to prevent some kind of conflict.

41:03

Yeah. And also for, like, you know,

41:05

Democratic cooperative reasons, like, I

41:08

I would be surprised if most

41:10

people, like, are down for

41:12

replacements. Mhmm. And

41:14

I think that like, should be definitely

41:16

respected. And it also may be right. So

41:19

that's the case for this, like, compromise for you.

41:21

Yeah. Yeah. Yeah. I guess I mean,

41:23

it sounds really great. And it sounds

41:26

I mean, it's yeah. It just sounds almost like too

41:28

good to be true to me. And

41:30

some part of me is like, surely,

41:32

it's not that easy. It just feels

41:35

very convenient that, like, we can

41:37

have it all here. I mean, it's not having

41:39

it all for both, but it's like having the majority

41:41

of it all for both humans and

41:44

digital minds. Well, I I feel

41:46

like cooperation does enable

41:48

lots of scenarios like that.

41:49

Ones like that. Yeah. We really can. Get

41:51

most of what they want. I mean, I should say,

41:53

I'm basically recapping an argument

41:56

from sharing world with digital minds. This

41:58

is not something I have liked. Thought

42:00

that much about. Yeah. I think it's really important

42:02

to think about these big questions about the future

42:04

of artificial sentence. But

42:06

my focus has been on issues

42:09

that are, like, more concrete than come

42:11

up today.

42:12

So, yeah, exploring this

42:15

a bit more deeply. Why

42:17

does anyone think that artificial

42:19

sentence is even possible?

42:21

Yeah, this is a great question.

42:25

I think the very broadest

42:27

case for it or, like, the very broadest

42:29

intuition that people have is

42:31

something like We know that some physical

42:33

systems can be conscious or sentient

42:35

like 146 made out of neurons can't

42:38

be. These ones, the ones on either

42:40

end of this recording. And also

42:42

listening in. And you could

42:44

have a view where something has to be made

42:46

out of neurons, it has to be made out of biological material

42:48

in order to be conscious. 146

42:51

reason that people think artificial minds could also

42:53

be conscious is this kind of broad

42:55

position in philosophy and

42:57

cognitive science called functionalism. Which

43:00

is this hypothesis that the very

43:02

lowest level details or like substrate

43:04

that you're building things out of, ultimately

43:07

won't matter. And the sort of things

43:09

that are required for consciousness or

43:11

sentience could also be made out

43:13

of other stuff. So one way of putting

43:15

this or one version of this is thinking that

43:18

it's the computations that

43:19

matter. It's the computations that our brains are

43:21

doing that matter for what we experience and

43:23

what we think about. Sorry.

43:24

What do you mean by computation? That's

43:26

a that's a great question that can go into

43:28

the into the philosophical weeds.

43:31

But for is like a maybe

43:33

like a rough approximation like,

43:35

patterns of information processing is

43:38

a way you could think about it. So you can

43:40

describe what your brain's doing. And

43:42

also think that your brain is in fact doing

43:44

like certain patterns of information

43:46

processing. So there

43:48

are theories by which what certain

43:50

parts of your brain are doing are

43:53

computing a function, taking

43:55

that input and processing it in a certain

43:57

way. So as to get a certain output.

44:00

So you can think of your visual system as

44:03

taking in a bunch of pixels

44:05

or something like

44:06

that. And from that computing where

44:08

the edges are. Right. Okay. So

44:11

really simplistically and maybe

44:13

just not true at all, but it's

44:15

something like when you

44:17

smell a food that smells good,

44:20

maybe you get kind of hungry. And

44:22

the computation is like, get the input

44:25

of, like, a nice yummy smelling food

44:27

and, like, maybe feel some hunger

44:29

is the is the output is the computation. Or

44:32

maybe it's like, feel this thing called

44:34

GPT, and then like search for food

44:36

in the fridge.

44:37

Yeah. It would definitely be more complicated

44:40

than that. But it is something like that. It's

44:42

like you're taking in inputs

44:44

and doing stuff with them. 146 thing

44:46

I might add at this point -- Mhmm. -- although

44:48

maybe this is two in the weeds. I think

44:50

when people say something like

44:52

you need the right computations for

44:55

consciousness, They're not just talking

44:57

about the right mapping between inputs and

44:59

outputs. They're also talking about

45:02

the internal processing that's getting

45:04

you from inputs to output. So here's an

45:06

example. Mhmm. There's this famous case

45:08

by Nedblock, also one of my advisers,

45:11

who put it out that you could have something

45:13

that has this big look look up table

45:15

where the input is a given sentence.

45:17

And then for every given sentence, it has

45:19

a certain output of what it should say. And

45:22

it doesn't do anything else with

45:24

the

45:24

sentences. It just goes to the right

45:27

column of its lookup table.

45:28

Totally. Yeah. Yeah. Yeah. Yeah. Of course, such a thing would, like, not

45:30

be feasible at all.

45:32

A lot people have the intuition that that way of

45:34

getting from input to output is not the

45:36

right sort of thing that you would want for

45:39

consciousness or

45:39

sentient. Right. Right. So,

45:42

like, if the lookup table had,

45:44

like, an input when you receive

45:46

input GPT and the, like,

45:48

looked up value was eat an apple,

45:51

that would not be the same thing as when

45:53

you receive the input, GPT. Think

45:56

about the or like maybe subconsciously, think

45:58

about the nutrients you might need. And

46:01

then go find a thing that will, like, meet that

46:03

need. Sorry. This may be a terrible

46:04

GPT. But something like I think

46:06

it's a good example.

46:07

Allowed to okay. Nice. It's just it's

46:09

pointing at the fact that, like, what your the

46:12

path you're taking internally matters.

46:14

And, yeah, I mean, I I will, like, add

46:17

or or point out, as I think you realize

46:19

that it wouldn't be decribable in

46:21

such a way and that the computations

46:23

would be extremely, like, fine ranging

46:25

complex and you couldn't, like, write them down on a

46:27

piece of paper. Yeah. But but the general

46:29

gesture that is is correct. Yeah.

46:32

Is there, like, a principled reason

46:34

why you couldn't write them down in paper?

46:36

I guess there's not a principled reason.

46:38

It's kind of I I think of that as more

46:40

of an empirical observation that -- Yeah.

46:42

--

46:42

in fact, what our brains are doing

46:44

is pretty complex. But that's

46:46

that's also an open question. I I think in the

46:49

early days of AI, people were

46:51

kind of optimistic that and this goes

46:53

for things with intelligence as well as consciousness.

46:56

That there would be these really simple principles

46:58

that you could write down and distill. That

47:00

doesn't seem to be what we've learned about the brand

47:02

so far or the way that AI has gone.

47:05

Yeah. So and we'll get to this later. I

47:07

do suspect that our theory of consciousness

47:09

might

47:10

involve, like, quite a bit of complexity. Yep.

47:13

Cool. Okay. So I I took you away off

47:15

track. So you're saying that there's

47:17

this idea called functionalism where

47:20

basically it's like the functions

47:22

that matter where all you need is certain

47:25

computations to be happening or,

47:27

like, possible in order to get

47:29

something like

47:30

sentience. Is that that basically right?

47:33

Yeah. That's basically right. Computationalism is

47:35

a more specific thesis about what

47:37

the right level of organization or what

47:39

the right functional organization is

47:41

is the function of performing certain

47:43

computations. Right. That makes sense. So

47:46

I think so. Yeah. Maybe I'll no. Maybe

47:48

I'll make sure I get it. So the

47:51

argument is that there's nothing

47:53

special about the biological material

47:56

in our brain that allows us to be conscious

47:58

or sentient. It's like

48:00

a particular function that our brain serves

48:03

and that, like, specific function is

48:05

doing computations. And

48:07

those computations are the

48:10

kind of underlying required

48:12

ability in order to be sending or conscious.

48:15

And theoretically a computer

48:17

or

48:17

something, silicone based could do that too.

48:20

Yeah.

48:20

I think that's basically right.

48:21

So that's the basic argument. What

48:24

evidence do we have for that argument? Yeah.

48:26

I'll say that's like the basic position, and then

48:28

why would anyone hold that position? I

48:30

think one thing you can do is look at the

48:32

way that computational neuroscience works.

48:34

So the success of computational

48:36

neuroscience, which is kind of the

48:39

endeavor of describing the brain

48:41

in computational terms, is like

48:43

some evidence that it's the computational level

48:45

that matters. And then there are also philosophical

48:48

arguments for this. So a very famous

48:50

argument or class of arguments are

48:53

what are called replacement GPT. Which

48:55

were flushed out by David Chalmers.

48:57

And listeners can also find

49:00

when Holden Karnovsky writes about

49:02

digital people and wonders if they could

49:04

be conscious or sentient. These

49:07

are actually the the arguments that he feels to.

49:09

And those ask us to imagine

49:12

replacing neurons

49:14

of the brain bit by bit

49:16

with artificial silicon

49:19

things that can take in the same input

49:21

and yield the same output. And

49:23

so by definition of the thought experiment,

49:26

as you add each one of these in,

49:29

the the functions remain

49:31

the same and the input output behavior remains

49:33

the same. So charters asked

49:35

us to imagine this happening say

49:38

to us while this podcast is

49:40

happening. Mhmm. So, yeah,

49:42

by stipulation, our behavior won't

49:44

change, and the way we're talking about things

49:46

won't change. And what we're able to

49:48

access in memory won't change.

49:51

And so at the end of the process, you have

49:53

something made entirely out of silicon,

49:55

which has the same behavioral

49:57

and cognitive capacities as

50:00

the biological thing. And

50:02

then you could wonder, well, did that thing

50:04

lose consciousness? By being replaced

50:06

with silicon. And what Thomas points

50:08

out is it would be really weird to have something

50:10

that talks exactly the same way

50:14

about being conscious. Because by definition,

50:16

that's like a behavior that remains the

50:18

same. Yep.

50:19

And has the same memory

50:21

access and internal GPT,

50:24

but, like, their consciousness left

50:26

without leaving any trace of

50:28

leaving. He he thinks this would be like a

50:30

really weird dissociation between cognition

50:33

and consciousness. And a lot

50:35

of people one reason this argument

50:37

kind of has force is a lot of people are pretty

50:40

comfortable with the idea that at least GPT

50:42

and verbal behavior and memory

50:45

and things like that can be functionally multiply

50:49

realized. And this is an argument that if you

50:51

think that it would be kind of weird if consciousness is

50:53

this one exception where the substrate

50:56

matters.

50:57

So I think the idea is something

50:59

like if you if you

51:01

had a human brain and you

51:04

replaced a single neuron with I

51:07

GPT, a silicone neuron that

51:09

did the exact like, performed to the exact

51:11

same function. And is the reason we think

51:13

that's like, a plausible thing

51:16

to think about because neurons transmit

51:18

electricity and they're kind of, like,

51:21

on off switch y and maybe the

51:23

same way that we think or the same way

51:25

that computers

51:26

are? Is that what you're saying? This is this

51:28

is an excellent point. 146 weakness

51:30

of the GPT, in my opinion, and

51:32

people have complained about this, is it

51:35

kind of depends on this replacement being

51:37

plausible or sorry. It

51:39

seems that way to people. In the paper, there's

51:41

actually a note on well, you might think that this is

51:43

actually in practice not something you could do.

51:45

And obviously, we could not do it now. Mhmm.

51:48

And for reasons I don't entirely understand that's

51:50

not really supposed to undermine the argument.

51:53

Okay. Alright. Well, maybe coming back to

51:55

that. Yeah. Is it

51:57

basically right though that we think of

51:59

a neuron and like a computer chip as

52:01

like analogous enough that that's why it's plausible?

52:04

Yeah, we think of them as being

52:07

able to preserve the same functions.

52:09

And I mean, there is some think there is some

52:11

evidence for this from the fact that,

52:14

like, artificial eyes and cochlear

52:16

implants -- Mhmm. -- like,

52:18

we do find that computational things can interface

52:21

with the brain and the brain can make

52:23

sense of them.

52:23

Interesting.

52:25

That's not like the size of argument. People who are kind

52:27

of not on board with this kind of computational

52:29

way of thinking of things would would

52:31

I'd probably

52:32

not. But we'll get off. We'll

52:34

face with that. We can have a Zoom time. Yeah.

52:36

And sorry. And the thing there is, like,

52:39

I actually don't know how artificial eyes work.

52:41

Is it like there's an

52:43

eye made of some things that are non

52:45

biological and they

52:47

interface with the brain in

52:49

in some way that allows people to see? I

52:52

also don't really know. Okay. I definitely

52:54

know that's possible with with

52:56

cochlear implants. Okay. I mean, I'm

52:58

interested in that one too then. But that's basically,

53:00

like, they connect

53:02

to, like it's a I'm picturing, like, wires.

53:05

Like, wires going from, like, a hearing aid

53:07

into the brain. I'm sure that's not quite right. But

53:10

it sounds like it's something like they

53:12

communicate, and that's, like, some evidence

53:14

that we can feed electricity

53:17

through silicone based things to

53:19

the brain and communicate with

53:20

it. Bauchner: Yeah, one recliner is it might not

53:22

be yeah. I don't think it's reaching into the brain. It might

53:24

be doing, like, the the right stuff to your inner

53:26

ear. To your

53:27

ear. Right. Okay. Okay.

53:29

Yeah. That makes sense. So

53:31

so we think that maybe

53:34

you think a neuron could be replaced

53:36

with a silicon based

53:38

version of it.

53:39

Cost thesis. Yeah. Cost thesis. Nice.

53:41

And then

53:42

Cost cutting your own.

53:43

Is is that a term? Is that a word? Is

53:45

that how people talk about it? I

53:46

think people have used that term. There's not a canonical

53:48

term since it's an imaginary case for

53:50

now. Right. Right. Okay.

53:52

So you have a prosthetic neuron

53:55

and you can

53:57

replace a single neuron at a time.

54:00

And like every time you make that replacement, it

54:02

stays like you work the same way,

54:04

your brain does the same things, nothing

54:06

about your behavior or thoughts

54:08

change? Yeah. So maybe it's

54:10

good to start with the first replacement. If

54:12

the first replacement is possible, I don't think anyone

54:14

would

54:14

think, oh, no. You have now destroyed Luis's

54:17

consciousness. Now she was like a walking --

54:19

So -- single copy.

54:20

Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. And then you this is

54:22

a common argument form in philosophy.

54:25

Two doesn't seem like it makes a

54:26

difference. Mhmm. And then Yeah.

54:28

So on and so forth. And then eventually, you replace

54:31

all my neurons with the silicone prosthetic

54:34

neurons and than I have an

54:36

entirely silicone based

54:37

brain, but there's no reason to think

54:39

I wouldn't feel I think the same things. Is

54:41

that basically it? That's the idea.

54:43

It's if you did think that you don't

54:45

feel the same things. Mhmm. It's supposed

54:47

to be really counterintuitive that you would

54:50

still be saying, oh, the this

54:53

worked. I'm still listening

54:55

to Rob talk. Yeah. I'm still

54:57

seeing colors. You would still

54:59

be saying that stuff since that's a good behavioral

55:02

function. Yep. Yep. Yep. And,

55:04

yeah, that's the basic thrust. So then what

55:06

that is is that that's at least one silicon

55:08

based system that could be

55:10

conscious. So that kind of opens the door --

55:12

Right. -- to

55:14

being able to do this stuff in Silicon. Yeah.

55:16

Yeah. Yeah. It feels very similar to

55:18

the, like, to this the ship that,

55:20

like, has all of its planks replaced

55:23

one by

55:23

one. And, like, at the end, you're asked if

55:25

it's the same ship. Yeah.

55:27

It is similar. It this this sort of thing shows

55:29

up a lot in

55:29

-- Yeah. -- in

55:30

philosophy, as I said, it's like a it's like

55:32

an old trick. Yeah. Listeners might

55:34

recall the podcast with

55:36

Al Hayek. Right? And he has

55:38

all these great examples of sort of, like,

55:40

argument patterns that you can use in philosophy

55:43

and you can, like, apply to different domains. You can

55:45

think of this as an application of a,

55:47

like, gradual replacement or,

55:49

like, bit by bit kind of argument and

55:51

philosophy. 146 thing I would like to say --

55:53

Mhmm. -- and maybe I'm qualifying too much, but full

55:55

disclaimer. I I think a lot of people

55:57

are not super convinced by

56:00

this GPT. Like,

56:02

Walteriro Piccinini is,

56:04

like, an excellent philosopher who thinks about

56:06

issues of computation and what it would mean for

56:08

the brain to be computing. Mhmm.

56:10

And I think he's sympathetic to the idea that

56:13

he's sympathetic to computationalism, but he

56:15

thinks that this argument isn't really what's

56:17

getting us there. I think he relies more

56:19

on that point I was saying about, well, if you look

56:21

at the brain itself, it does actually look

56:23

like computation is a deep

56:25

or meaningful way of carving

56:28

it up and Right.

56:29

Right. Right. Right. And so if you could get

56:31

the right computations doing some other

56:33

things or doing things that make

56:35

up

56:36

sentence, then, like, It doesn't matter

56:38

what's doing it.

56:39

Yeah. What

56:40

reasons do people think that that argument

56:42

doesn't hold up? Well, for one thing,

56:44

it's like You might worry that it sort of

56:46

stipulated what's at issue, at

56:48

the outset, which is that silicon is able

56:51

to do all the right sort of stuff. Mhmm.

56:53

So there's this philosopher of biology and philosopher

56:55

of mind called Peter Godfrey Smith who

56:57

would be an excellent guest by the way.

56:59

He's written book about Octopus minds

57:02

And he has a line of thinking where

57:05

functionalism in some sense is

57:07

probably true, but It's

57:09

not clear that you can get the right functions

57:12

if you build something out of silicon because

57:14

he's really focused on the low level biological

57:17

details that he thinks might actually matter

57:19

for at least the kind of consciousness

57:21

that you have. And that's sort of something that

57:23

think you can't really settle with

57:26

with an argument of this form. Yeah.

57:28

Can you settle it? So

57:31

I actually have sort of set

57:33

aside this issue funnily enough since it's

57:35

like the the foundational issue. For

57:37

now? And I'll say why I'm doing that.

57:39

Yeah. think these, like, debates about multiple

57:42

realizability and computationalism have

57:44

been going on for a while. And

57:46

I'd be pretty surprised if in the

57:48

next few decades someone has just nailed

57:50

it and they've proven it one

57:52

way or the other. And so the way

57:54

I think about it is think it's

57:57

plausible that it's

57:59

possible in Silicon to have the right

58:01

kind of computations that matter for consciousness

58:04

And if that's true, then you really need to worry

58:06

about AI sentence. And so

58:08

it's sort of like, let's look at the world where that's

58:10

true and try to figure out which ones could be conscious.

58:13

And it could be that none

58:16

of them are because of some deep reason having

58:18

to do with the biological hardware

58:21

or something like that. But it seems unlikely

58:23

that that's gonna get, like, nailed anytime soon.

58:25

Yeah. And I I just don't find it

58:28

crazy at all to think that

58:31

the right level for consciousness is the sort

58:33

of thing that could show up on a silicon

58:35

based system. Uh-huh. Uh-huh. Okay.

58:38

Yeah. Are there any other arguments for

58:40

why people think artificial sentence is

58:42

possible?

58:43

This is related to the computational neuroscience

58:46

point, but 146 thing people have

58:48

noticed is that lot of the leading scientific

58:50

theories of what consciousness is are

58:52

in computational terms. And

58:54

posit computations or some other

58:57

sort of pattern or function as

58:59

what's required for consciousness. And so

59:01

If you think they're correct in doing so,

59:04

then you would think that it's possible for

59:07

those patterns or computations or

59:09

functions. Being made

59:11

or realized in something other than biological

59:14

neurons. Does anyone disagree on

59:16

this? Like, do some people just think artificial sentience

59:18

is not possible?

59:20

Yeah. So there are these views called biological

59:23

theories. Maybe you can call them. So

59:25

netblock is one of the

59:28

guess like foremost defenders of this biological

59:30

view that consciousness just is,

59:32

in some sense, a biological phenomenon.

59:35

And you won't be capturing it if

59:37

you go to something too far

59:39

outside the realm of biological looking things.

59:41

John Searle is also a proponent

59:44

of this view. So there's views where,

59:46

like, that's definitely true and

59:48

it's just kind of a it's just like

59:50

what consciousness is. There's

59:52

also views on which consciousness is something

59:54

functional, but also you're not going to be able

59:57

to get it on GPUs or anything like

59:59

what we're seeing today. And those are kind of

1:00:01

different sorts of positions.

1:00:03

But, yeah, I mean, it should be noted that plenty

1:00:05

of people who've thought about this have

1:00:07

concluded. Yeah. You're not going

1:00:09

to get it if you have a bunch of

1:00:11

GPUs and electricity running

1:00:13

through

1:00:14

them. It's just not the right sort of thing. And

1:00:16

that's just like So the first argument

1:00:19

is like there's something really

1:00:22

special about biology and

1:00:24

biological like parts that make

1:00:26

whatever consciousness and sentence is

1:00:29

possible. And the other argument is,

1:00:31

like, it's theoretically possible,

1:00:34

but, like, extremely unlikely

1:00:37

to, like, happen with the technology

1:00:40

we have or could create or something? Yeah.

1:00:42

So, like, for that second position, Like,

1:00:44

most people will hold some version of that

1:00:46

position with respect to Swiss cheese.

1:00:49

Like, I would be really surprised if very

1:00:51

complicated arrangements of Swiss cheese ended up

1:00:53

doing these computations. Mhmm. Because it's just, like,

1:00:55

it's not the right material to get the right thing

1:00:58

going. Even if I think there are it's

1:01:00

it is multiply, realizable. You don't have

1:01:02

to think it's, you know, you could feasibly

1:01:05

do it in any sort of material at all.

1:01:07

Okay.

1:01:08

Interesting.

1:01:08

One one thing I'll add, since I am being

1:01:10

like very obsessive to range of positions, which

1:01:12

think is appropriate, I would like to note

1:01:15

that large numbers of philosophers

1:01:17

of mind and consciousness scientists in

1:01:19

surveys say, yeah, artificial

1:01:22

sentient is possible. Machines could

1:01:24

be conscious I don't have the exact numbers

1:01:26

off the top of my

1:01:27

head, but --

1:01:27

Yeah. -- David Chalmers has this great thing, the

1:01:30

Phil Paper Survey. And, like, yeah,

1:01:32

it it has asked people this question, and it's not like

1:01:34

a friend view. Yeah. Like

1:01:36

a substantial a substantial share of

1:01:38

philosophers of mind think that our official sentence

1:01:41

is possible and maybe

1:01:43

plausible. And dido like surveys of consciousness

1:01:46

scientists. Yeah. Yeah. Yeah. Yeah. We'll we'll stick

1:01:48

those in the show notes. Cool.

1:01:50

So sounds like there's like a couple

1:01:52

of counter arguments that are about

1:01:55

biology and and just

1:01:57

like what's possible with like silicone

1:01:59

and GPUs as building

1:02:01

blocks for entities. Are

1:02:04

there are there any other counter GPT? People

1:02:06

think for why artificial sentence

1:02:08

might not be possible?

1:02:10

Yeah, one thing it might be worth mentioning

1:02:12

is I'm GPT be doing this interview

1:02:14

talking about consciousness and sentient as

1:02:17

things where we know what we're talking about and, like,

1:02:19

we know what we're looking for, and it is this phenomenon

1:02:22

that we can wonder about. There is

1:02:24

a position in philosophy called

1:02:26

illusionism, which holds that consciousness

1:02:29

is kind of a confused concept and it doesn't

1:02:31

actually take anything out. So

1:02:33

on that view, it's like straightforwardly

1:02:36

false that AIs could be

1:02:37

conscious. It's also false that

1:02:39

in a certain sense of the word -- Yeah. -- humans

1:02:41

are conscious.

1:02:42

Right. Well, can you explain the

1:02:44

view of illusion of some? Yeah. So, like,

1:02:46

illusionists hold that

1:02:49

this concept of

1:02:51

subjective experience or what

1:02:53

it's like to be having a certain experience.

1:02:56

Even though a lot of people now find

1:02:58

it intuitive, illustrious would argue that,

1:03:01

actually, it's kind of a philosophers

1:03:04

intuitive notion and and not that deep.

1:03:06

I think I think I would argue with that. But

1:03:08

Yeah. It doesn't refer to anything,

1:03:11

actually. It's kind of like incoherent

1:03:14

or fails to pick anything out. The same way

1:03:16

that This is a popular example

1:03:18

in philosophy. People used to wonder

1:03:20

about phlogiston, which I think

1:03:22

was this substance that was going to explain

1:03:24

fire. I know we talked about it and

1:03:26

look for it. But, ultimately, it's

1:03:29

just not part of our, you know,

1:03:32

oncology. It's not part of our worldview. And

1:03:35

they think consciousness will end up being like that on

1:03:37

reflection. We'll ultimately have

1:03:40

a lot of functions and ways of processing

1:03:42

information and behavioral dispositions and

1:03:44

maybe representations of things. But

1:03:47

this question, but which of them

1:03:49

are conscious? Which of them have

1:03:51

subjective

1:03:52

experience? Ultimately won't

1:03:54

be a meaningful one. Okay.

1:03:57

Right. So I guess if you don't think humans

1:04:00

or non human animals are, can't just

1:04:02

to any

1:04:02

degree. It's like not a meaningful question to ask

1:04:05

whether artificial intelligence is

1:04:07

sentient. In a certain sense of the

1:04:09

word, Yeah. What they deny is

1:04:11

what philosophers have called phenomenal consciousness,

1:04:14

which is used to pick out whether there's something

1:04:16

it's like to be something or

1:04:19

whether it has subjective experience or

1:04:21

this kind of subjective quality to its mental

1:04:23

life. They don't deny that

1:04:26

things are conscious in the sense that

1:04:29

they may might process information in

1:04:31

certain ways and sometimes be globally

1:04:33

aware of that information they

1:04:35

don't deny that things feel

1:04:38

pain, for GPT. But they

1:04:40

deny this way of construing it in terms

1:04:42

of subjective experience.

1:04:43

Okay. Okay. I mean, that doesn't seem

1:04:45

that damning for artificial sentience,

1:04:48

I guess. Like, as long

1:04:50

as you think that they

1:04:52

can still feel pain. And

1:04:54

if you think that's morally significant,

1:04:57

then, like, artificial sentence

1:04:59

could maybe feel the same thing and

1:05:01

that would still be morally significant?

1:05:03

Yeah, so this is roughly my

1:05:05

position and I think it's the position of

1:05:08

I was talking to Keith Frankish

1:05:10

on Twitter the other day. Keith Frankish is one

1:05:12

of the leading proponents of illusionism. And,

1:05:16

yeah, I asked him, like, what do you think about people

1:05:18

who are looking for animal sentient? Is

1:05:20

that kind of an entirely misguided quest

1:05:22

-- Mhmm. -- on illusionism? And his answer

1:05:25

is no, and he rightly thinks.

1:05:27

And I GPT, that even if you're an

1:05:29

illusionist, they're going to be mental phenomena

1:05:31

or information phenomena that matter.

1:05:34

And you're going to want to look for

1:05:36

those. You won't be looking for

1:05:38

maybe quite the same thing. Right? You think

1:05:40

you are if you're a realist about consciousness?

1:05:42

And think that's like a very important lesson.

1:05:44

I think in like the kind of circles

1:05:47

we run-in, a lot of people are very sympathetic

1:05:49

to illusionism. And occasionally,

1:05:51

I hear people say, oh, well, then

1:05:54

there's like no question here or it's like

1:05:56

a meaningless question. And that

1:05:59

might be true for like phenomenal consciousness. But

1:06:01

I just wanna point out there are like scores

1:06:03

of extremely meaningful and vexing questions

1:06:05

even if you're an illusionist. And I would still

1:06:08

like a theory of what sort of things

1:06:10

feel pain in the illusionist sense or

1:06:13

have desires or whatever

1:06:15

it is that we on reflection

1:06:17

think matters

1:06:18

morally. Right. Right. So is

1:06:20

it basically like some

1:06:23

people think that the kind

1:06:25

of consciousness I think I'm experienced

1:06:28

sing might not be a meaningful

1:06:31

concept or thing. Like, I might not actually

1:06:33

be experiencing that. I have the illusion

1:06:35

of experiencing it, but, like, there's no

1:06:37

sense in which I actually truthfully really

1:06:40

am. But like, I still

1:06:42

feel like I feel pain and I still don't

1:06:44

like that. And that in itself is,

1:06:46

like, still more like significant even if

1:06:49

something called consciousness

1:06:50

is, like, happening, underlying that

1:06:53

pain or whatever? Yeah. That's one position

1:06:55

you could have. You could think that being

1:06:57

disposed to judge that you have phenomenal

1:06:59

consciousness that matters morally. think

1:07:02

a more plausible position you could have is it doesn't

1:07:04

matter if you have whatever cognitive

1:07:06

illusion makes philosophers think phenomenal

1:07:09

consciousness is real. It could also just be

1:07:11

if you feel pain in this functionally defined

1:07:13

sense that that

1:07:15

matters. Or if you have desires that are thwarted

1:07:17

or preferences that are thwarted,

1:07:19

Cool. There's really excellent work by

1:07:22

Francois Cameron, who's another

1:07:24

illusionist trying to

1:07:26

see what value theory looks like

1:07:29

and questions about animal centients and animal

1:07:31

welfare look like on the illusionist picture.

1:07:33

I think it's a very underexplored issue

1:07:36

and like an extremely important issue. So

1:07:38

put that on the show notes

1:07:39

too. Yeah. Yeah. Plug. Nice. Okay.

1:07:42

Yeah. Where where do you personally come down on

1:07:44

on artificial

1:07:45

sentence? And yeah, I guess, whether

1:07:47

it's possible.

1:07:48

Yeah. I think I'm, like, eighty

1:07:50

five percent that

1:07:52

artificial consciousness

1:07:54

or sentient And here's a

1:07:56

real wiggle or something in that vicinity

1:07:58

that we morally care about.

1:08:00

That makes sense to me.

1:08:01

Is is possible? Yeah. Yeah.

1:08:04

Okay. That's pretty high.

1:08:05

Yeah. So that's, like, you know, ever and

1:08:08

impressive. Yeah. So I guess

1:08:10

if if that's right, and,

1:08:12

like, artificial sentence is possible

1:08:14

and if it ends up

1:08:15

existing. Yeah. Can you walk me through the

1:08:17

case that it definitely matters morally? Yeah.

1:08:20

It's almost hard to give a thought experiment

1:08:22

or an argument for the claim -- Mhmm.

1:08:24

-- that suffering matters. I

1:08:27

I think that suffering matters. Is

1:08:29

something where common sense and

1:08:31

the majority of philosophers agree, which

1:08:34

doesn't always happen. So like Bentham,

1:08:37

Jeremy Bentham, has this famous

1:08:39

and off quoted passage, off

1:08:41

quoted by animal rights and animal welfare

1:08:43

people among others, where he says, like,

1:08:46

the question about animals is not if they can

1:08:48

reason or if they can talk.

1:08:50

It's whether they can suffer. And

1:08:53

it doesn't seem like there's any other boundary

1:08:55

that seems like the right boundary

1:08:58

of moral concern. Now,

1:09:00

as we've noted, you can have quibbles

1:09:02

about what suffering actually is and if it

1:09:04

involves phenomenal consciousness and

1:09:06

things like that. But yeah, it's

1:09:08

just extremely intuitive that If

1:09:10

something feels bad for something, and

1:09:13

maybe you also add that it doesn't want it,

1:09:15

and it's trying to get away from it, that

1:09:18

matters

1:09:18

morally. And that sort of thing

1:09:20

should be taken into account in our moral decision

1:09:23

making. Yeah.

1:09:23

Yeah. One thing I'd like to add is,

1:09:25

like, there's a there's a position on

1:09:28

which that's all it matters. And,

1:09:30

like, the only things that are good and bad for

1:09:32

things are experiences

1:09:34

of pleasure and displeasure.

1:09:37

That's not a consensus view at all.

1:09:39

But even among people who think that other things

1:09:42

matter, like knowledge or friendship or justice

1:09:44

or beauty, they still also think

1:09:46

that, you know, experiencing pain is

1:09:48

is really

1:09:49

bad.

1:09:49

Right. Right. Right. Okay. Yeah. That makes

1:09:51

sense. So, like, the other

1:09:53

main alternative for this like focus

1:09:55

on experiences pain or experiences

1:09:58

of pleasure is a focus on desires

1:10:00

and preferences, and whether those are being

1:10:02

satisfied. Uh-huh. So that's

1:10:04

a big debate and debates of, like, what

1:10:07

welfare is you know,

1:10:09

what what makes things go well or badly

1:10:11

for something. Yep. And it's also a debate

1:10:13

in like what sort of things are moral

1:10:15

patients, like the sort of things that are in the scope

1:10:17

of moral consideration. And I would like to

1:10:19

note a position on which

1:10:22

what ultimately matters is not pain

1:10:24

or pleasure, but desires.

1:10:26

And desires seem like they're much easier

1:10:29

to define in this functional

1:10:31

way that maybe doesn't make reference to consciousness.

1:10:34

And that might be in some ways easier to get

1:10:36

a grip on than consciousness. That's the

1:10:38

position of Francois Cameron. Who has

1:10:40

a paper about how we should think about welfare

1:10:42

if we don't really believe in consciousness. I

1:10:45

find those issues very difficult

1:10:47

to tease apart So, like, Shelley

1:10:49

Kagan has this apt remark

1:10:51

that in human life,

1:10:54

our experiences and our desires are like

1:10:56

so tightly linked be really

1:10:58

hard to be like, is it bad that I'm in in pain

1:11:00

or is it bad that I don't want to be in pain?

1:11:03

Like, those just seem really

1:11:05

hard

1:11:05

to, like, tease apart conceptually. Yeah.

1:11:07

I mean, can I imagine

1:11:10

being in pain and not not wanting

1:11:12

to be in pain?

1:11:13

So there are cases where people have the

1:11:15

sensory experience of pain that report

1:11:18

don't minding it. So they can

1:11:20

fully feel that there's skin is being

1:11:22

pinched or something like that. But

1:11:24

they're

1:11:24

like, yeah. But it's just not bad.

1:11:27

So that's called pain asymptalia, and it's like

1:11:29

a fascinating That is fascinating.

1:11:32

And there's a lot of philosophical work, which is

1:11:34

like, well, is that really pain? Are they

1:11:36

like lacking some unpleasant quality to

1:11:38

the

1:11:38

pain? And that's why they don't mind it.

1:11:40

Could you really have that unpleasant quality and not

1:11:42

mind it?

1:11:43

Yeah. Yeah. Yeah. Yeah. Yeah. One thing I can say that

1:11:45

pain is simply it does seem too many people to

1:11:47

have shown. Is that there's a surprising association

1:11:49

between the way you process the sensory

1:11:51

information about pain, and then this,

1:11:54

like, affective like,

1:11:56

felt unpleasantness thing. And I think there

1:11:58

are differences in, like, the brain in,

1:12:00

like, in terms of how those are processed and

1:12:02

things, which is why things like this are possible.

1:12:04

Yeah. Yeah. No. That's that's interesting.

1:12:07

Okay. So it sounds like philosophers

1:12:09

would basically mostly agree

1:12:12

that if AI systems are feeling

1:12:14

something like pleasure or pain, that just,

1:12:16

like, probably matters morally.

1:12:18

Is that is that basically sound right? That

1:12:20

sounds right to me. And if it's not, it it should

1:12:22

be. Okay. Great.

1:12:24

Yeah. So where are we

1:12:26

with current systems on this? I GPT, there's

1:12:29

been some public conversation around

1:12:32

current large language models

1:12:34

being sentient. There's a whole thing

1:12:36

there that we could talk about. But just, yeah,

1:12:38

from the ground up, what do you think about where we

1:12:40

are? Yeah. So the short answer is

1:12:43

after thinking about a lot of current

1:12:45

theories of consciousness and how large

1:12:47

language models work,

1:12:50

I think it is quite unlikely that

1:12:52

they have conscious experiences of the

1:12:54

kind that we will morally care about. That is

1:12:56

subject to a lot of uncertainty because there is so much

1:12:58

we don't know about consciousness and how they

1:13:01

work. I can definitely say there's not

1:13:03

like a straightforward case where you're like,

1:13:05

here's what consciousness is and here's

1:13:07

how large language

1:13:08

models have it.

1:13:09

Yeah. Yeah. And I also think

1:13:11

I would be quite surprised if

1:13:14

large language models have developed

1:13:16

pleasurable and displeasurable experiences.

1:13:19

You know, they're having really bad time. They

1:13:21

don't like writing poetry for us.

1:13:23

Right. Right. Right. Like, we have stumbled into

1:13:25

pass through here.

1:13:26

Yeah. Yeah. I'm glad that people are actually,

1:13:29

like, you know, raising the the issue.

1:13:31

It's good practice for future things. And

1:13:33

there is also the small

1:13:35

chance that we have. And in

1:13:37

general, like part of what I try

1:13:39

to do is Just get people thinking

1:13:42

about it and and and provide pointers for ways

1:13:44

of having, like, as

1:13:46

evidenced based conversations as

1:13:48

possible. Because as listeners

1:13:50

will have noted, it's, like, very easy

1:13:53

for it to descend into,

1:13:54

like, Twitter madness and Right.

1:13:57

Complete free form speculation. Yeah.

1:13:59

Yeah. Yeah. Yeah. Yeah. Yeah. I guess guess

1:14:01

that was maybe arguably the case with

1:14:04

with Lambda, which we can talk about. But I

1:14:06

GPT, first, just kind of clarifying there

1:14:09

are a bunch of different kinds of AI

1:14:11

systems

1:14:12

that exist right now. Which 146 most

1:14:14

likely to be sentient?

1:14:16

I would be somewhat surprised if large language

1:14:18

models are the most likely current systems.

1:14:20

And

1:14:20

those are things like GPT three or GPT

1:14:22

chat. Right?

1:14:23

And Lambda.

1:14:24

And Lambda. Of course. Yeah. Yeah. And I yeah.

1:14:26

I can say more about why. I I think that that will

1:14:28

probably be getting into, like, the substance of this investigation.

1:14:31

Right. Right. Well, I guess,

1:14:33

do you do you mind starting by telling me,

1:14:35

like, what other systems are

1:14:38

plausibly? Like, we even wanna be asking

1:14:40

the question, are they sent in? They're, like,

1:14:43

possibly closer?

1:14:44

Yeah. There's things that there's

1:14:47

at least things that seem to do

1:14:49

more human like or agent

1:14:51

like things. And I think that can maybe

1:14:54

put us closer to things

1:14:56

that we could meaningfully call pain

1:14:58

or pleasure or things like that? Like what?

1:15:00

So yeah. So there are, like,

1:15:02

virtual agents that are trained

1:15:04

by reinforcement learning and which navigate

1:15:07

around, like, a Minecraft environment. Mhmm.

1:15:10

There are things that incorporate large language models,

1:15:12

but do a lot more than

1:15:15

just answer text inputs.

1:15:17

You can plug large language models into

1:15:19

robots, and it's

1:15:22

really helpful for the way those robots plan.

1:15:24

That's like a really cool line of research.

1:15:27

There's obviously just robots, and

1:15:29

I I would like to look more into just

1:15:32

you know, actual robots. Yeah.

1:15:34

Yeah. Which sometimes get, like, a bit of short shrift

1:15:36

even though it's kind of the canonical

1:15:38

-- Right. --

1:15:39

like, WiFi thing.

1:15:40

Right. Right. And robots

1:15:43

like, we're literally talking about, like,

1:15:45

things in Star Wars. What's the

1:15:47

closest thing to that that we have right now?

1:15:50

Like, what's the, like, smartest

1:15:52

or most impressive

1:15:54

robot? Sorry. You might not know the answer.

1:15:56

But, like, what is AI's smart and impressive

1:15:58

robot? Yeah. I was, like, I was I was not being modest

1:16:01

when I'm, like, I need to look more into that. Like,

1:16:04

I'm really not up on the the state of art.

1:16:06

Like, the first thing I wanna look at is people

1:16:09

who explicitly want to try

1:16:11

to build more self awareness into robots I

1:16:13

definitely wanna see how that's

1:16:16

going.

1:16:17

Yeah. Yeah. Yeah.

1:16:18

Thanks, sir. You know, what you're gonna do

1:16:20

if you have a robot that can feel pain.

1:16:22

Like, are we Are we, like, ready

1:16:24

for that -- Yeah. -- as like a as

1:16:26

a society? And, yeah,

1:16:29

another thing about robots is they It

1:16:31

would be, like, more straightforward to maybe see

1:16:33

how they feel pain because -- Totally. --

1:16:35

have bodies in mind. They're trying to train

1:16:38

them to protect their bodies and send damage

1:16:40

to them.

1:16:41

Right. Right. Yeah. That makes a lot of sense.

1:16:43

Yeah. You mentioned kind of a a line of research

1:16:46

on feeding in large language

1:16:48

models into robots. And that

1:16:50

having an impact on how well they plan. Is

1:16:52

there more you can say about that? It sounds

1:16:54

like it might just be a really interesting topic.

1:16:57

Yeah. Like the cool factoid, which

1:16:59

I can't probably technically elaborate that much.

1:17:01

As my understanding is that

1:17:04

Large language models have to learn all

1:17:06

kinds of abstract representations in

1:17:08

in the course of learning to predict next words.

1:17:11

And those representations just seem

1:17:13

to be very useful for agents that

1:17:15

want to, like, decompose plans into

1:17:18

subactions. It's kind of like

1:17:20

an as such a fact from a certain

1:17:22

point of view that the kind of things learned

1:17:24

by large language models would so straight forwardly

1:17:27

and kind of I think without that much

1:17:29

tweaking end up helping with other

1:17:31

agents. Right. But it's true. Sorry.

1:17:34

And what's is there a specific

1:17:36

robot you have in mind with a specific set

1:17:38

of goals? Just I I'm not totally

1:17:40

sure I

1:17:40

understand, like, what plans we're talking about and

1:17:43

how they're deconstructing them or whatever.

1:17:45

Yeah.

1:17:45

We can find the real paper and link to it in the show

1:17:47

notes.

1:17:48

Yeah. So The epistemic status of this is

1:17:50

half remembering some slides from the lecturer

1:17:53

that I saw at a reinforcement learning conference. Right.

1:17:55

Yep. I think it was a virtual agent

1:17:57

and it was doing things like

1:17:59

fill up a cup of coffee in the kitchen and

1:18:01

then decomposing that

1:18:02

into, okay, get the cup. Put on the counter.

1:18:05

Right. Okay. That

1:18:07

is wild. So you have

1:18:09

an agent that's like get

1:18:12

some coffee is the GPT. And then you give

1:18:14

it a large language model somehow or

1:18:16

you, like, give it access to a large language model.

1:18:18

And the thing is like, how do I do

1:18:20

that? And then the large language model

1:18:23

helps it be like, here are the steps.

1:18:25

You go to the kitchen. You pull a cup

1:18:27

from the cupboard or whatever. Is that

1:18:29

basically?

1:18:30

Yeah. It it it might not I think it's not

1:18:32

that kind of, like, direct kind of querying. Okay.

1:18:34

Okay.

1:18:34

Okay. In in some vague way that

1:18:36

I would have to read the paper to know So it's it

1:18:38

has that, like, in its

1:18:39

machineries. So I'm

1:18:40

happy with it. Right. Right. Okay.

1:18:42

Presentations and knowledge of the large language model.

1:18:44

Got and the baseline didn't and it was

1:18:46

worth it planning. But then when you feed

1:18:48

it in to the whatever processor

1:18:50

algorithm, it gets much better at it.

1:18:53

Yeah.

1:18:53

My understanding is that, like, decomposing plans

1:18:55

into subplans has always been very hard

1:18:57

problem.

1:18:59

Okay. Interesting. I mean, if you think about

1:19:01

all the different ways that there

1:19:03

are to fill up a cup of coffee

1:19:05

and I mean, there's, like, an infinite

1:19:08

number of yeah. Infinite number of, like, little variations

1:19:10

on that. And you kinda need

1:19:12

to know which ones are relevant. You sort

1:19:14

of know how to transfer knowledge

1:19:16

from one case of getting the coffee

1:19:18

to, like, a slightly different one. Yeah. Right.

1:19:20

And like, I think one traditional problem

1:19:23

people have in reinforcement learning, which

1:19:26

is training things by, like,

1:19:28

just giving score on how well they did

1:19:30

it, is it's it can just

1:19:32

be very hard to scale that to, like,

1:19:34

very complex actions. And my understanding is

1:19:36

that large language models entering the

1:19:38

scene

1:19:38

has, like, really helped with that. Okay.

1:19:42

Wild. Okay. How do we get here? So I guess this

1:19:44

is, like, these are some of the

1:19:46

different systems that,

1:19:49

like, you could ask the question of whether

1:19:51

they're sentient. And somewhere

1:19:54

in there, you'd put large language

1:19:56

models. But you'd put some other

1:19:58

things kind of at the higher end of

1:20:00

probability of sentient. And

1:20:02

it's like, you're not totally sure what those are. It sounds

1:20:04

like. But maybe robots with large language

1:20:06

models feeding in

1:20:07

are, like, a bit higher than large language models

1:20:10

alone? Yeah. And so even without

1:20:12

having yet examined, like,

1:20:14

specific system. One quick argument

1:20:17

is just whether or not I agree with

1:20:19

them, there are bunch of preconditions for

1:20:21

Sentience that a lot of people think are plausible.

1:20:23

146 of them is embodiment, say,

1:20:26

or maybe another one is having

1:20:28

a rich, like, model of a

1:20:30

sensory world or something like that. And

1:20:34

there's just a straightforward argument. Yeah.

1:20:36

Like pure text, large language

1:20:37

models, don't have that. Sort

1:20:40

of thing,

1:20:40

probably. Yeah. But it's not hard to imagine

1:20:43

augmenting them with those things

1:20:45

or plugging them into other stuff, and

1:20:48

people are already doing that. So if

1:20:50

you're worried about some limitations of LOMs,

1:20:52

there's definitely other places you can

1:20:53

look. And I myself haven't yet looked,

1:20:56

but it's like definitely on my list.

1:20:58

Cool. Cool. Yeah. Makes sense. Yeah.

1:20:59

I decided to start with pure text LLMs,

1:21:01

like, you know, as a base case and as an

1:21:03

exercise. Cool. Yeah. What

1:21:06

what would you look at next? I guess you said robots.

1:21:08

Anything else you'd be especially excited to look

1:21:10

at? Yeah. That

1:21:11

might not be robots. It might be it

1:21:13

might be virtual. Virtual agents. Yeah.

1:21:15

Okay.

1:21:16

Yes. That and maybe stuff that's

1:21:18

closer to a pure text LLM, but just

1:21:21

something that also has like sensory

1:21:23

channels Right.

1:21:25

So, like, getting input

1:21:27

in systems. Sorry. What what

1:21:29

are multimodal

1:21:30

systems? I

1:21:31

mean, a multimodal modal

1:21:33

just means, like, kind of input in this context.

1:21:35

Oh, I see. So

1:21:36

it'd be something that's trained both on text and

1:21:38

on images.

1:21:39

GPT it. Okay.

1:21:41

So dolly two, which

1:21:43

you've probably seen, make Yeah. But Beautiful

1:21:45

pictures.

1:21:46

Love it. Yeah.

1:21:46

Like, that has to be trained on both images

1:21:49

and text and because it's, like, translating

1:21:51

between them.

1:21:51

Right. Okay.

1:21:53

I'm not saying that's my next like, you know,

1:21:55

best candidate or whatever just as an example of

1:21:57

mobile. Right. Right. Jumping in again quickly

1:21:59

to flag that as some of you might have heard,

1:22:01

GPT is actually set to be released this

1:22:04

week, and it's expected to be multimodal

1:22:06

in the way that Rob's talking about here. I

1:22:08

haven't gotten chance to ask Rob if

1:22:11

this changes his view significantly on whether

1:22:13

GPT is conscious or sentient.

1:22:16

But I'm guessing he'll be sharing his take

1:22:18

on Twitter at GPT Long.

1:22:20

Or probably on his sub stack experience

1:22:23

machines. So I'd encourage curious

1:22:26

listeners to check those out. And so

1:22:28

what's the reason that you think that, like, the more

1:22:30

kind of types of inputs

1:22:33

or, like, words and pictures, for GPT, is

1:22:36

more likely to

1:22:39

result in something being sentient?

1:22:41

Yeah, that's a great question. I

1:22:43

don't think it's like a strict

1:22:45

condition that you have to be processing more

1:22:47

than one thing. Yep. I have this

1:22:49

rough intuition that processing

1:22:52

more than one type of thing might

1:22:54

make you develop the kind of

1:22:56

representations or resources for

1:22:58

handling multiple sources of input. That

1:23:01

might correspond to consciousness. Got

1:23:03

it. Another way of putting that

1:23:05

is, like, if you get closer

1:23:08

to something kind of human ish

1:23:10

Yeah. That can make puts you on a little

1:23:12

bit firmer ground even if it's not

1:23:14

strictly necessary.

1:23:15

Yeah. Yeah. Yeah. And One fact about us is

1:23:17

is we have to handle all sorts of different

1:23:19

input streams and decide which ones

1:23:21

to pay attention to and form representations that

1:23:23

incorporate all of

1:23:24

them. Totally. And things like that. Yeah. Yeah.

1:23:27

I'm realizing I feel like I have to

1:23:29

understand what you when you say form representations,

1:23:32

but, like, do you basically mean I

1:23:35

don't know. Like, what dolly's doing when it

1:23:37

gets a bunch of when it gets, like, trained on

1:23:39

a bunch of pictures of dogs. It,

1:23:41

like, is it forming representation of

1:23:43

a dog? And and, like, we're

1:23:45

also doing things like that as humans.

1:23:48

Like, we've got some resolution of

1:23:50

GPT. I'm gonna cheat and not answer the four.

1:23:52

Question of what representation is. Okay.

1:23:55

I I will stick

1:23:55

to, like, the multimodal element.

1:23:57

Okay. Whatever it is to represent a dog

1:24:00

our representations seem to contain information

1:24:02

about what they look like and what they sound

1:24:04

like, and how people talk about them and how they're

1:24:06

defined and and all sorts of things. Got it.

1:24:09

Is

1:24:09

it kind

1:24:10

of like a

1:24:10

concept of a dog? Yeah. We can

1:24:13

we we can use that word. Exactly. And,

1:24:17

yeah, there's really interesting work

1:24:19

from Chris Ola who has been on

1:24:21

the show. Mhmm. And his name usually

1:24:24

comes up if you have some fascinating interpretability

1:24:26

thing to talk about. Where, yeah, I

1:24:28

think he looked for neurons that

1:24:31

seem to represent

1:24:34

or encode or whatever certain concepts

1:24:36

in multimodal systems. And somehow,

1:24:39

like, be, yeah,

1:24:41

emerging in this, like, cross metal or or

1:24:43

multimodal

1:24:44

way. Cool. Cool. Okay. Yeah. That makes sense.

1:24:46

Yeah. Okay. So I

1:24:48

guess yeah. It sounds like there there's like range

1:24:51

of types of AI systems,

1:24:53

and there are some different reasons to think

1:24:56

maybe there's a bit more evidence for some

1:24:58

being sentient or conscious.

1:25:01

I guess I've heard you give the example of,

1:25:03

like, the fact that humans

1:25:05

have multiple kind

1:25:08

of like I don't even know. What are what

1:25:10

are we calling it? Like, we process words.

1:25:12

We process images. I guess we process sounds.

1:25:15

I'm I'm kind of calling it inputs in my head,

1:25:17

but I don't know if that's fair. Okay. Yeah.

1:25:19

Awesome. Cool. So we've got lots of inputs.

1:25:21

Maybe a thing that has lots of inputs, maybe in

1:25:23

a system that has lots of inputs. It's a

1:25:25

bit more like a human, and that's, like, maybe

1:25:28

a bit more evidence that it might

1:25:30

be sentient or conscious. What other

1:25:32

types of evidence can we have about

1:25:34

whether an AI system is conscious?

1:25:37

Yeah. So the perspective I've been taking

1:25:39

is let's try to think

1:25:41

about the kind of internal processing

1:25:45

it's using or the kind of computations

1:25:47

or representations it's manipulating as

1:25:50

it does a task and see if

1:25:52

we can find GPT to things that

1:25:54

we have reason to think. Are associated

1:25:56

with consciousness and humans. Mhmm.

1:25:59

So the dream would be,

1:26:02

oh, we studied humans enough and we kind of

1:26:04

identified what the mechanism is

1:26:06

and specified it in computational terms.

1:26:09

And maybe that's a very complicated thing. Right. Maybe

1:26:11

it's somewhat simple. And then

1:26:13

we use interpretability tools to

1:26:15

say, ah, there is that structure

1:26:17

-- Right. -- in this AI system.

1:26:20

Yeah. I think that scenario is unlikely

1:26:22

because it we have the great

1:26:24

interpretability, we have the detailed thing

1:26:26

of

1:26:26

consciousness, and we have the exact match, which

1:26:28

think is unlikely unless you're doing the cold

1:26:30

brain emulation. Yeah. Yeah. Yeah. I see.

1:26:33

So the idea is, like,

1:26:35

we figure out that what

1:26:37

sentience is is

1:26:39

like this formula.

1:26:42

It's like you could put the formula

1:26:44

in Excel sheet and then the Excel sheet

1:26:46

would feel sentience. It's

1:26:48

like when you get

1:26:50

a pinprick, you feel this kind of pain

1:26:52

or something. And we're like, know exactly the formula

1:26:54

for that kind of pain, and then we

1:26:57

find it in an AI

1:26:59

system. It, like, has the exact

1:27:01

same, like, if given this input

1:27:04

do this process, and then feel

1:27:06

this thing, and that thing is pinprick

1:27:08

pain. And then if we saw that exact

1:27:10

match, it should be like, cool. That's doing the same

1:27:12

thing. It must be experiencing the same thing.

1:27:15

Obviously,

1:27:15

it's, like, infinitely more complicated. But

1:27:18

it's, like, that's roughly the thing.

1:27:20

Yeah. Just with one clarification, which

1:27:23

I think is in what you said, it's

1:27:25

not just that there's the same

1:27:27

input to output mapping. Is

1:27:29

that the algorithm or

1:27:31

process that it's using to process

1:27:33

it -- Got

1:27:34

it. -- works in the relevant sense to be

1:27:36

the same. The

1:27:37

same process. Oh, and that's actually key.

1:27:39

Yeah. In my in my view.

1:27:41

Yeah. Yeah. Yeah. Yeah. Otherwise, it's this

1:27:43

it could just be like a view like lookup

1:27:45

table.

1:27:46

Exactly.

1:27:47

Did you almost say V Lookup? Because you have to excel

1:27:49

in mind? I I do think about a

1:27:51

lot of this stuff in I'm,

1:27:53

like, imagining excel bunches we're talking.

1:27:56

Nice. Okay. And that process

1:27:58

might be something like

1:28:00

I mean, is there any way to simplify it

1:28:02

for me just to get a bit

1:28:05

better of an intuitive understanding of what

1:28:07

what kind of process we could find?

1:28:10

Yeah. So this is a great question

1:28:12

because, like, part of what I'm trying

1:28:14

to do more myself and get more people to do

1:28:17

is actually

1:28:18

think about processes that are identified

1:28:21

in neuroscience. And actually think about

1:28:23

what those are. So we could we could

1:28:25

do that. Great. If you would like.

1:28:26

I would love to do that. And, like,

1:28:28

learning the theories of consciousness

1:28:31

are going to be sketchy and unsatisfying

1:28:33

intrinsically, and and also my

1:28:35

understanding of them. And

1:28:38

and maybe kind of hard to explain verbally. But

1:28:40

we'll link to papers explaining them. Cool.

1:28:42

So, like, global workspace, theory is

1:28:44

a pretty popular neuroscientific theory

1:28:47

of what's going on when humans

1:28:49

are conscious of some things rather

1:28:52

than others. And

1:28:54

let's, like, start with, like, kind of, the picture of

1:28:56

the mind or the brain that it's,

1:28:58

like, operating within, and then I'll say

1:29:00

how it then builds the theory of

1:29:02

consciousness. Okay. Top of that. So it

1:29:04

has this kind of picture of the the

1:29:06

mind where there are a bunch of different kind

1:29:09

of separate and somewhat encapsulated

1:29:12

information processing systems

1:29:14

that do different things. So

1:29:16

-- For GPT, like,

1:29:18

So, yeah, like a like a GPT system.

1:29:21

Uh-huh. It's

1:29:21

like helps you generate speech. Maybe,

1:29:24

like, a decision making system. Maybe

1:29:26

that's not one system though, but -- Sure. -- also,

1:29:29

like, the sensory systems. They're in

1:29:31

charge of getting information from outside world

1:29:33

and, like, building some representation

1:29:36

of what they quote, quote, they

1:29:38

quote, quote, think is, like, going on.

1:29:40

Yeah. Like, memory memory

1:29:43

be one? Memory would memory definitely

1:29:45

is. It's one of them. Yeah. And

1:29:48

those things can operate

1:29:50

somewhat independently. And it's like

1:29:52

efficient for them to be able to do so.

1:29:54

And they can do a lot of what they're doing

1:29:57

unconsciously. Like, it's not going to feel

1:29:59

like anything to

1:30:01

you for them to be doing it.

1:30:03

Right. Here's a quick side note, and this

1:30:05

is separate from GPT workspace.

1:30:07

This is something like everyone agrees on.

1:30:09

Okay.

1:30:10

An interesting fact about the brain is that

1:30:12

it is doing all kinds of stuff,

1:30:14

a lot of it extremely complex, and

1:30:17

involving a lot of information processing. And

1:30:20

I can't ask what is it like for Louisa

1:30:22

when her brain is doing

1:30:23

that.

1:30:24

That's some other thing. Right.

1:30:25

So,

1:30:25

like, your your brain is, like, regulating, like,

1:30:28

Hormonal

1:30:29

race. It's like pumping blood.

1:30:31

Your heartbeat? Exactly. Yeah. I have no

1:30:33

idea what that's like. I'm not conscious

1:30:35

of it. That's actually a really I think that might

1:30:37

be the most helpful clarification of

1:30:39

consciousness. Consciousness. I feel like

1:30:41

people have said, like, what it is like

1:30:44

consciousness is what it is like

1:30:46

to be a thing. And

1:30:48

they've distinguished between like, we don't

1:30:50

know or like there's nothing that it's like to be a

1:30:53

chair or they're but there is something that it's like

1:30:55

to be a Louisa. And that,

1:30:57

like, doesn't do much for me, but,

1:30:59

like, there is something that it is like

1:31:01

for me to, like, I don't know,

1:31:04

see the sunshine. But

1:31:06

there is not something that

1:31:08

it is like for me to I

1:31:11

guess, have the sunshine regulate

1:31:13

my internal body clock or something, or

1:31:15

maybe that's a bad

1:31:16

one. But Yeah.

1:31:17

Yeah. And, like and I do have the intuitive

1:31:19

sense that, like, one of those is conscious and one of those

1:31:21

is unconscious. And

1:31:24

yeah. I'm just finding that really helpful. That's

1:31:26

great because, you know, we've been we've been

1:31:28

friends for a while, and I remember having

1:31:30

conversations with you where you're like, I just don't