What should we say [music] as

philosophers about the relationship

between neuroscience and philosophy of

mind? So, how much of our ideas about

how the mind works can we read off from

the results that neuroscience um [music]

is telling us? The results you get in

the lab can be wellestablished and fine.

There's nothing wrong with those data,

[music]

but there's more of a problem of

generalizing from what you learn in the

lab to outside of the lab with

neuroscience. for cognition in the real

world. It's precisely all of that

complexity [music] and all of that

interactivity that is really important

to how for example animals are able

[music] to negotiate their environment.

It's not an argument [music] that AI is

impossible so much as why does it seem

so possible so inevitable to people. If

you look at the history of the

development of the life sciences of

psychology, [music] there are certain

shifts towards a much more mechanistic

understanding of both what life is and

what the mind is, which are very

congenial to thinking that whatever is

going on [music] in animals like us in

terms of the processes which lead to

cognition, they're just mechanisms

anyway. So why couldn't you put them

into an actual machine and have that

actual machine do what we do?

Yes. But anyway, much sweeter. Um,

welcome to MLST. It's amazing to have

you here.

>> Thanks so much for having me along.

>> So, um, you wrote this book, The Brain

Abstracted. Uh, it's an amazing book.

Folks at home should definitely buy this

book. It's really, really good. Um, tell

me about this book.

>> It was quite a few years in the making.

I think officially I started writing it

maybe 2018 and it came out in 2024 but

it was really based on ideas that I've

been working on um maybe since 2014 I

started publishing some philosophy of

science papers about computational

explanation in neuroscience and then

going back beyond there um some of my

own experiences when I was doing

training in neuroscience on uh the

visual system um and I was using um

computational models of the era before

there was deep learning or anything that

fancy. Um and thinking about really what

does understanding the brain through

this lens of computation by saying that

we have models which not only simulate

the brain as you know biological

simulation using computers and all kinds

of things or weather simulations such

and so forth but actually kind of

alleged to um duplicate the function of

cells in the brain which is this kind of

additional claim which is made of about

computational modeling when it's applied

to the brain as this unique [laughter]

um unique structure which is not only a

biological organ but also a kind of

computer itself.

>> The arc of your book is we have this

problem with simplification because as

scientists we want to build legible

theories about how the world works.

>> A lot of philosophy of science in recent

years um has p picked up this topic of

abstraction and idealization. So

abstraction is sort of quite a general

word which can just mean sort of

ignoring details which are there in

concrete real life situations. Um so it

would be you know familiar to you from

um doing sort of Newtonian problems in

physics where your teacher tells you

well there's always friction in real

life but we'll pretend that the friction

isn't there. So you're leaving out a

detail which is known to be there in the

concrete system. Um idealization

means um sort of attributing properties

to the system that you're modeling in

science which are known to be false. Um

so for example in genetics modeling the

assumption is made of infinite

populations. These kinds of

idealizations often make the

calculations more tractable. But of

course there's no such thing as an

infinite population in real life. In

some way, an abstraction is also always

a false representation, always an

idealization.

Um, so sometimes the difference between

the two can be subtle. How I put this in

the book is that an idealization kind of

points us to the thought that when we

have a scientific representation, we're

kind of presenting something which is

kind of cleaner and better than the

thing in real life. When we talk about

something someone being idealistic, it's

like they have a view of how things

should be and unfortunately reality does

not um live up to that. So idealization

in science is often

to do with sort of representing things

mathematically in a way which is kind of

cleaner and neater than could be

possible in real life. And on

abstraction, you said in your book that

there's the the lofty philosophical

version of abstraction, which is, you

know, upstairs in the heavens of Plato,

I think you said, um, or even Galileo,

there's this idea that these natural

forms exist which are disconnected

entirely from the the sort of the

spatial the the temporal realms. And

then there's the the more deflationary

view of abstraction, which is simply

that we just ignore details. Now, I'm

speaking with my good friend France

again tomorrow. he's releasing the new

version of the ark challenge and I I

think he does have this and many AI

researchers do they have this

platonistic idea he calls it the

kaleidoscope effect which is that the

universe um basically is written in code

and what we see is like a kaleidoscope

when all of the rules of the universe

just get composed together in different

ways and all we need to do as AI

researchers is kind of decompose back

into the into the rules.

>> What could possibly go wrong? So I um I

watched some of the videos with France.

I found it really fascinating precisely

this kaleidoscope hypothesis because

seeing that as a philosopher I thought

that's Plato because France precisely

says we have the world of appearance.

It's complicated. It looks intractable.

It's messy but underlying that real

reality is neat um mathematical

decomposible. This is precisely this

sort of contrast between the world of

forms and the world of being sort of

eternal stable truth and the world of

becoming appearance um messy flowing

complicated reality. And so it goes back

thousands of years in philosophy. Um

it's really interesting that this is an

assumption not only that AI researchers

um make often but it's runs through

science as a kind of justification for

the pursuit of mathematical

representations even when they sort of

depart from known facts about the

concrete physical systems in reality.

the idea that the mathematical

representation is getting you more to

the truth the underlying truth of how

things are as opposed to what I call the

diff like the downto- earthth view of

what abstraction is and mathematical

representation is that it's something

that we do because of our complic um

cognitive limitations. So instead of

thinking that the abstraction gets you

like the higher level of reality, just

saying that we do abstraction because

we're finite knowowers. There's limits

to how much complexity any individual

person or group of people can actually

encompass in their modeling strategies

or representations. And actually it's

only by pretending things that are more

simple than they actually are that we

get some traction. So that's like the

downto- earthth um mundane explanation

of why abstraction is so much used in

science.

>> Yeah, it's it's so pervasive in the deep

learning world. I mean um I also

interviewed the the folks who pioneered

this geometric deep learning blueprint

and that's the same idea basically that

you know the world is described with

geometry and all we need to do is imbue

these geometrical um inductive prior

into deep learning models and and then

they can you know essentially by

reducing the degrees of freedom to ones

which are aligned with how the universe

works then then we get where we where we

want to go. I think the notion of like

patterns and real pan patterns um to uh

invoke Danet's term there is a helpful

one. So one one thing that you could say

is going on here is that yes there's

lots of complexity there in the natural

world. It's apparent in the data, but

like if you just um dn noiseise the data

a bit underlying there, there's a real

pattern and we should we don't have to

be like plonist and weird about it, but

there's just regularity that is

sometimes masked by noise. That doesn't

seem like too metaphysically

problematic.

But one of the questions that I sort of

pose to that as a challenge to that, you

know, very moderate view and I and I say

this frequently in the book is when

you're saying that some of the apparent

disregularity in the data is irrelevant.

That's your decision as a scientist.

It's not relevant to you at the moment,

but that it could be relevant to someone

else. it could be really important to

how that system works in the natural

world for reasons that you're not aware

of. So when we sort of classify the

signal versus noise in our data sets, we

shouldn't ignore that the fact that

those are decisions that we're bringing

to bear on our investigation. We

shouldn't assume that we're just reading

off the signal, the real pattern that is

there in reality and that there aren't

very many other significant real

patterns there. And to the extent that

we're probably also kind of creating

pattern through the through the very

denoising process that we bring about.

>> Interesting. I mean um physicists aren't

under any um illusion. So they know that

Newton is is an idealization.

>> And just to contrast I mean you you

cited reflex theory. I mean of course

Pavlov and the dogs you know folks at

home will know about that. And Newton is

still around. We still use that but we

don't use reflex theory anymore. Yeah.

Yeah. So, this is um a chapter that I

present in the book as a case study of

how oversimplification can get

scientists on the wrong track. So, the

history of science is like hindsight

2020. We're looking at a a theory about

how the brain worked which was really um

dominant for a few decades at the end of

the 19th century, beginning of the 20th

century. Um it's yeah familiar to us. um

in the in with Pavlov with this idea

that we can explain behavior in terms of

reflexes which get conditioned and there

can be there's obviously learning

involved with that. The most ambitious

version of the theory said that all of

the functions in the brain are basically

versions of u reflex arcs so sensory

motor loops. So a very prestigious and

sort of well- reggarded um physiologist

like Charles Sherington was heavily

invested in the reflex theory. But he

admitted in his um in his book the

integrated action of the nervous system

that this notion of a simplex simple

reflex is an idealization. It probably

doesn't exist in real life. And yet this

is the key that's going to kind of

unlock neurohysiology. it's going to

help us decompose and make sense of all

of these different interactions that

could be observed experimentally. So

what seemed to be going on there is that

scientists were sort of taking that

age-old meth um method which is that

it's a good huristic to seek

parsimonious um explanations to use

Okam's razor and the obvious thing to do

was like let's assume there's this thing

that's there's a simple reflex and then

running with it way too far actually

never being able to um explain the

amount of data that they had initially

thought that they would with

And it's not clear how long the reflex

theory could have gone on for if it

hadn't been for the computational theory

sort of coming in during the um second

world war era and basically providing an

alternative um explanatory framework

which was also quite neat and um and and

I would say provides its own kind of

idealization toolbox. So a very popular

thing in cognitive science is to say

well if something behaves you know the

same way as a cognizing human for

example then we can maybe we might draw

inferences that it has consciousness and

it has many other cognitive faculties

but there there's always this kind of

you know almost ignorance of the actual

mechanism of of the object of study. I

think behaviorism is it has a bad name

but it's not that discontinu

discontinuous with a lot of thinking

which is sort of normal and still

acceptable in science which is to treat

things as black boxes. This is precisely

what the behaviorist said. It's like the

mind is opaque. It's hidden within the

walls of someone else's individual

subjectivity. As scientists all we know

are the inputs and the outputs and we'll

just track those. Um, and that's like a

version of what you just said. If well,

if the inputs and the outputs, the

behavior of this system are looking like

um, uh, what we know to be a conscious

system elsewhere, well, let's just treat

them as all of the same class of objects

given that the only available

information is the inputs and outputs. I

think that kind of reasoning can be fine

in certain contexts, but it's a

philosophical leap to say

the

access that we have to our own thoughts.

Um, and the presence or absence of

subjectivity that we're aware of with

other people is irrelevant to making

these decisions or judgments about what

other kinds of systems can have

consciousness. Um so I think it's much

too quick to just go behaviorist and say

well there's no relevant difference um

between X and Y even if one is a person

one is a machine just because we can say

that there's some similarities and

inputs and outputs. I think if I

remember correctly at one point you drew

an imaginary kind of graph where you

said on one axis we have science realism

which is where you know our scientific

theories actually represent things in

the world and then we have empiricism

which is the idea that you know facts we

receive you know tell us something about

the world and and then there's this more

interesting axis which I think you're

very inspired by which is this kind of

constructivist idea.

>> Can you can you explain that? Yeah. So

the constructivist

um path sort of which is different from

the scientific realist and empiricist

one sort of really runs with the idea

that we are um active makers of

knowledge. It shouldn't be confused with

the kind of constructivism that we have

in some kind of like more extreme

branches of sociology of knowledge which

say that all scientific theories are

social constructs and not constrained by

phenomena that have been observed in

nature. So it's so I'm not saying that

scientific theories are merely

constructed in the way that like poems

could be like a work of imagination and

so forth but the idea is that there's

this interactivity between

um humans um groups of scientists their

plans as epistemic agents going out into

the world with an agenda to find stuff

out about certain phenomena in order to

achieve certain goals often

technological applied science and its

goals and there's a some push back from

the things in nature themselves that

they're investigating. But the idea that

knowledge is always the product of this

interactivity. So we cannot discount

that there is a human framing side to

this. We can't go along with this idea

that a scientific theory is just sort of

reading off the source code of the

universe as if the human way of

conceptualizing those phenomena had no

bearing on the theory as it ultimately

turns out. But we also can't discount

that the theory that arises is

constrained by how things happen to be

that is worked out through that process

of experimental interaction.

>> You said I I think you were inspired by

Emanuel Kant. So he he had this

transcendental idealism and and please

bring that in but that somewhat informed

your your own view which is this um

haptic realism. C can you introduce

that?

>> Yeah so that's that's um saying that

knowledge is comes about through this um

process of interaction. So this notion

of haptic realism is emphasizing

that it's through engagement. So haptics

being like the sense of touch. Um the

contrast here was is with an ideal of

knowledge which is based on this idea

that we can know things in a disengaged

way. If you think of um vision as the

archetype for of knowledge, what happens

when we look around at our surroundings

um and use sight as a source of

knowledge? We can get into this um

mindset where it seems like we do not

have to interact with things in in order

to know them. we can just kind of absorb

information passively and then because

we're not bringing about our

representations in a kind of active way

it would seem to us and I'm not saying

this is how vision works but it's a kind

of conceit that often comes about if you

use this very visual model for knowing

um John Dwey called it the spectator

theory of knowledge so this is a clear

predecessor from what I'm saying here is

that like we just look around we absorb

how things are our knowledge is sort of

entirely objective. It's almost like a

God's eye view on reality. But if you

think that scientific knowledge in

particular is more kind of touchlike,

you can't ignore the fact that we um

sort of run into things. We have to pick

things up, engage with them, ultimately

change them in order for us to acquire

knowledge of them. So you cannot

discount the fact that we're kind of

meddling with things in the process of

um bringing about our our knowledge. Um

and another sort of dimension of this

haptic metaphor is that our hands are

not only a sensory organ, but they're

also the means by which we manipulate

things. So manipulation means precisely

working with the hands. And so I think

that really captures, if you like, the

double face of scientific models.

They're both of uh means of acquisition

of knowledge in the way that hands are

also sensory organs. We sort of find

things out about the world through the

sense of touch, but we're also they're

also means for changing things, for

doing things.

>> We speak about this in evolution. What

would happen if you could just rerun

evolution? You know, what would happen

if we could just have a parallel

universe and the entire enterprise of

science just ran again? And what you're

alluding to is that it wouldn't be

completely different. Maybe there are

some guardrails but but it is actually

quite divergent.

>> Yeah. Yeah. There's certainly like

contingency in the history of science,

you know, where people start out,

cultural factors which prompt them to

people to ask certain kinds of questions

and not others. Um uh so a view quite

similar to what I say about hapsic

realism in the book um is by Hassok

Shang who's a professor of philosophy of

science at Cambridge. Um and he has a

view which he calls realism for

realistic people. That's the title of

his new book. And he is an outand-out

pluralist about science. So he says that

because there is contingency in the

history of science, it means there are

paths not taken, but we could maximize

the acquisition of knowledge if we just

like explored as many of those different

paths as possible, which isn't something

that I say in the book myself because I

think there are also reasons why it

makes sense to narrow views and paths of

inquiry. And also we don't have like un

unlimited resources. Um but yeah sure

there are opportunity costs that come

along with like taking a certain path

and there are others not pursued

>> in the enterprise of science there might

be a trope or or an idealization that

we're getting closer to the truth.

>> Yeah.

>> And is is do you think that's the case?

Do do you think as as the enterprise of

science just progresses that we're

getting closer to the truth or could we

be in culde-sac basins of attraction and

so on? that's very much associated with

scientific realism. So there's this view

that there is one way nature is and

science succeeds in so far as scientific

representations conform to this one way

that nature is

for my from my view sort of takes very

seriously the idea that nature could

just be sort of inexhaustibly

complex. So if you ever try to sort of

if you ever pin it down in one

representation,

there are ways also that it could be

represented sort of inexhaustibly many

different varieties of ways that you can

investigate it and then also um ways

that any one representation is lacking.

So there's a kind of inherent sort of

lack of convergence that that picture

brings about. Um, one of the ways of

expressing this is to say that nature is

protein. Um, there's this mythological

character um called Proteius

who was a shape shifter. This sort of

this yeah mythological being that lived

in the sea and it would he would keep

changing his shape. You couldn't but if

you could pin him down he would answer

you a question and tell you the truth.

But the thing was you had to pin him

down. And I think this is a really nice

illustration of like what's going on

with our interactions with nature as

scientists.

Nature is sort of inexhaustibly complex.

There's all kinds of patterns and things

going on there. It can be pinned down

and we can get true answers. But it but

[laughter] when we sort of release our

grip, it will carry on shape-shifting.

And there's lots of other ways that it

could be. So yeah, one final theory. I'm

not so convinced by that. This is very

much a view that I think makes sense if

your basis for your theory as a

philosopher of science is really the

biological sciences which is where I'm

coming from. If you're a physicist, it

seems much more natural to think that

there is one fundamental set of laws of

the universe which is going to be nailed

down once and for all and could explain

everything. Biology just sort of throws

up lots and lots of examples,

particularities. it tends to be um less

considered sort of less intellectually

satisfying in comparison with physics.

Oh, you can just spend all your time in

biology doing stamp collecting because

there's this thing and there's this

thing and there's this thing. How do you

tie it all together theoretically? But

on the other hand, I think that if you

take that particularity and that

shifting quality to biological

phenomena, then actually it just forces

you to think about knowledge

differently. in in your book you you you

spoke about a trajectory I suppose of um

possibly failures of of simplification.

So we just spoke about reflex theory but

one one of the big things is this

metaphor of cognition or or the brain

perhaps as being a kind of computer and

you spoke about the the early roots of

this from reflex theory to cybernetics

and computationalism. Can you sketch

that out? So this connecting thread is

really this idea that what cognition is

is something that is machine-like that

what um I don't know going back to the

17th century this is a view associated

with the philosopher and physicist and

physiologist Rene Deott who said we need

to give up we need to go along with this

idea that everything that happens in the

body um is explicable in terms of quite

simple mechanistic forces and this you

idea that biological systems are

machine-like has obviously been hugely

influential in the different branches of

science. The reflex theory was one

instance of that. People often said

machine-like reflexes um and making

comparisons with sort of Newtonian

decomposition.

Um with the computational framework, you

had an actual machine, a digital or

analog computer which could be compared

with brain processes. I mean cybernetics

is an interesting stage along the way

because they were building sort of

little devices um which had some degree

of autonomy made up of um and and

supposed to be um emulating like

versions of negative and positive

feedback and and as hypothesized to

occur in the body. But yeah, I would say

at the core of this research idea is

that if what's going on in the body is

ultimately a mechanistic process. Then

by redoing engineering with this

non-living system which is capturing

some of the core operating principles

that we find in biology, then we can use

that device as a map as a as a resource

to then reinterpret what's going on in

the biological system. You saw that with

for example McCullik and Pitts in their

1943 sort of landmark paper of

interpreting neuronal cells as logic

gates and then saying yeah you could

build a computer out of neural nets.

This is the origin of neural nets as we

know them today. This is the birth of

the idea. but then using that notion

that neurons are logic gates to then

interpret what's going on in physiology.

So what I describe in it's in chapter

four of the book is a sort of back and

forth um thing of of making

devices which are somewhat inspired by

biology and then using those then as the

lens through which to review biology

again. And I say that the advantage and

the appeal of this pro process is that

it allows you to or gives you kind of

license to ignore so many things that

are happening in the brain and nervous

system which are just not shared with

non-living machines like all of the

biochemistry, all of the ways that

neural tissue is shaped by vasculature

and interacts with the immune system and

all of that sort of background stuff

that if you're a theoretical

computational neuroscience, you can say,

I'm only interested in the computational

properties of the brain. Um, I don't

need to care about all of that messy

biological detail. So, it gives you a

kind of tunnel vision, which as

scientists can be fine to have tunnel

vision. You can't take in everything at

once all of the time. But what I take

issue with is the kind of ontologization

of that, saying that because

computational neuroscience is this

successful field of inquiry, we know now

that the brain is a computer. I think

that is not an inference we should make.

>> Yeah. I mean, I don't think

connectionists typically argue that. I

mean, they would say it's a different

mechanism.

>> Yeah.

>> But they they think that there's some

kind of functional equivalence. Mhm.

>> And that's the things because so many

folks in AI at the moment they are

interested in biologically plausible

architectures. So what if you know like

the cyberneticist did what if we have

more autonomy, diversity, agency and and

so on. And they they fundamentally think

that I guess they make the assumption

that the world is a machine and if we

replicate it with sufficient fidelity

then we can reproduce the behavior.

>> Yeah. to what extent are the mechanisms

of the brain inherently bound up with

the fact that the um implementation here

is in living tissue. So I think it's

really um there's sort of tantalizing

um evidence about how the extent to

which sort of brain processes and

signaling between neurons not just the

electrical specialized signaling that

neurons do but biochemically it's kind

of outgrowths of signaling that's

happening elsewhere in the body all of

the time. So that there's nothing

that we shouldn't think of neuronal

cells as sort of distinctively cognitive

as opposed to the other cells in the

body, but that they're extensions of the

ways that um cells signal anyway.

And if neuronal function is so much just

a manifestation of what's happening with

metabolizing cells anyway, that makes it

more of a stretch to say that a machine

that's not living could have the same

functionality. Yeah. I mean, no one's

trying to sort of build artificial

neural networks with living cells.

>> No. No. But I mean, there is there's a

there's an analogy in neural networks.

There's this thing called um like the

lottery ticket hypothesis which spoke

about pruning

>> and what the researchers found is that

you train this big dense neural network

and after it's trained because you need

the density for stocastic gradient

descent for you know training

tractability

>> after it's trained you can prune away

90% of the connections and it still

works the same way and maybe

>> maybe evolution and and our kind of like

biological instantiation maybe it's the

same thing. It's it's we've been through

this billion plus year training process

>> and all of these things that we think

are important like you know the the the

the instantiation the auto poesis the

you know the the the agency and so on

maybe those are vestigial

>> and we can now just kind of snip snip

snip and we can just kind of create this

abstract version right

>> it seems reasonable

>> what do you think

>> that it's just vestigial Huh. I I mean I

think we really need to take seriously

the economy that is there and of um

biological information processing like

we do a lot more with a very limited um

energy budget running our brains every

day than is is like um artificial neural

networks are really really expensive to

run. It doesn't strike me that

biological cognition could get away with

being that wasteful. That surely

to keep things sort of blowing up in

terms of like energy being consumed for

information processing biologically that

there must have been a fair amount of

pruning on the way. I kind of think of

agency as being a bit of a spectrum

from, you know, you can think of it in a

deflationary sense as being this

autonomous thing that's the cause of its

own actions and then the deep

philosophical sense is that there's this

intentionality and you can control the

future and whatnot. And it rather speaks

to like the the the physicist's view is

that you know all of these you have this

light cone and you have all these micro

interactions and of course that's beyond

our cognitive horizons. So, so we we

develop ideas of of representations

where we can have these distal

relationships between things that are in

our mind and and and things that are far

away in in time and space. And I

suppose, you know, you think of that as

being another form of idealization. But

the fascinating thing though when I

think about agency, I think about it in

terms of like

apparent causal disconnectedness.

We are agents because you have

consistent beliefs and ideas and you're

not just an impulse response machine

that's being your actions aren't

determined entirely by this situation.

You're a person and and I I kind of

perceive that as a kind of causal

disconnectedness.

>> Yeah. Yeah. I agree. I mean the what I

say in the book in that chapter I I set

out and I say this to be sort of very

metaphysically neutral about what

representation is what intentionality is

but at the same time um not what I

directly wrote in the book. I think I

agree with you that there is something

very important about connecting the

notion of agency and intelligence with

this thing of like being responsive to

what is actually very distal. It could

be distal in time and space. It could be

like distal because it happened a long

time ago, but this is what biological

memory is. Things that happen like to

you when you're a baby affect how you

are now.

physical systems like non-living

physical systems. They're much more

constrained in their um in their

actions, and I don't mean that in like

action, but just what they do, what

happens to them, by what's proximal to

them. There's like the distal is always

screened off by the proximal, if that

makes sense. It's like the

um whereas for you all of these things

that happened in the past could be as

relevant as anything that happens in the

room right now or your ideas about the

future would be relevant to what you're

saying right now. So yeah, this this

notion of being

sensitive to what's not immediately

driving you in your surroundings. Um I

think that's a really important like

thing to latch on to and like

delineating at least the class of

systems that we want to call cognitive

to ones that we would say sort of merely

physical, not intelligent in any

important sense of the word.

>> Very cool. So, so, so we we're we're

trying to um I suppose partition the

world into logical units that we can

understand and and agents are a great

version of that. And Daniel Denny, of

course, he had the three stances. He had

like the the um the physical stance, the

design stance, and the intentional

stance as a way of kind of like, you

know, building useful explanations of of

of how, you know, things behave and

introduce those. But you said that you

didn't quite agree with that because to

Dennit it's a hierarchy which means you

know like the intentional stance perhaps

has precedence over the other ones.

>> Oh, so it so actually it's kind of the

reverse. He it's it's as if the physical

stance has an onlogical priority like

that's what's really there but it's

useful to use the design and the

intentional stance.

>> Very interesting. But but you said for

you you don't really have that

prioritization. You kind of you're

open-minded.

>> Yeah. So that's part of the sort of

metaphysical neutrality

of that I set out with the chapter is to

say okay

let's not go in with the assumption that

low-level physical causes are the like

the primary causes of everything.

Yeah. It's a it's a way of

of if you like taking um intentional

phenomena at face value. intentional in

the sense of like bearing

representations. And I think one of my

criticisms in that chapter is this

agenda which is Sarin philosophy of mine

to said to say that okay if

representation is real we need to be

able to tell a physical story about how

it comes about and I'm just saying why

not why go along with that project if

there's no and it's this is actually

going a denian view is is um you might

see it as that if talking about

representations and intentionality is

useful within the sciences. Why not just

take that in face at face value and not

say that that needs to be established by

making it coherent with some causal

story about what's going on um in terms

of non intentional physical

interactions. So that was the position

there

>> with Putnham's rock right you know he

said that you can take any open physical

system and you can configure it in such

a way to have the same types of

information processing and then why

wouldn't that have all of the you know

all of the the cognitive properties that

>> when you're making a claim that you know

the brain is a computer and that that

explains cognition what have what

grounds have you got for saying that any

arbitrary physical system is actually

implement a computation

um just from looking at its physical

dynamics. If it's purely a question of

mapping the physical dynamics to a

computational formalism, then any

physical system can afford a mapping of

that sort, whether it's a rock, whether

it's the sofa, whether it's my stomach

as opposed to my brain. Um and so I so

that's a challenge to the computational

theory of mind that it's assuming that

brains implement computations just

because we can sort of model them as

computationally but we can model all

kinds of things computationally what

makes brains special

>> so so what about this idea of whether

computation itself has causal powers

>> I don't think it does um so computation

itself is mathematical formalism it's

like exists it's it's uh it is a

mathematical structure. Things that have

causal powers are concrete physical

systems. So I just think they're

different kinds of things.

>> So So S famously argued that you know um

the the reason why we can't build strong

AI AI is that computation doesn't have

causal powers. It's implemented in in

silicone. So what does have causal

powers are the machines that actually

implement the computation. But but

couldn't you sort of say, well, there is

still a causal graph. Perhaps you you

would argue that computation isn't a

node in that causal graph. It's just

some kind of an aspect of it.

>> Yeah. I mean, I think it just goes back

to this issue like computation in and of

itself is not the kind of thing that

could have causal powers. I I think I

think Soul's point, and this is in the

rediscovery of mind on this, was an

interesting one. It was kind of maybe of

kind of subtle and it kind of gets lost

in the wash of like AI back and forth

and um soul bashing which happens a lot

but um it was about the kinds of ways

that we form explanation in the sciences

and he what his point was that cognition

if it's anything is something as part of

the [snorts] physical realm the realm of

causation.

The assumption of the computational

theory of mind and he argues that this

is very dominant within cognitive

science is that you can explain this

phenomenon which is a phenomenon of the

concrete physical world through this

non-causal thing which is computation

and suddenly

there's no gap that needs to be closed

and I and I think that's that's a fair

point is that there's something

inherently

um that needs like further justification

of why of all of the things that happen

in the concrete physical world that

demand explanation, why we reach out

outside of the concrete realm of

physical causation into computation in

order to explain this thing cognition.

>> Another argument S was making was you

know how machines couldn't understand.

Yeah.

>> And of course he was talking about

things like semantics.

>> Yeah.

>> Do do you feel that they could

understand? So what do I think? Um I

think certainly there's more to human

understanding than that. I I think that

a thing about human cognition and animal

cognition in general is that my view is

that it's not a set of discrete modules

that work separately from one another. I

think ling language is bound up with

sensory motor engagement and how we

likewise how we perceive the world is

shaped by um linguistic um concept

formation and everything like that.

So the idea that you could just sort of

detach off um a language faculty have it

replicated in an LLM that doesn't have

the other bits of our cognition and it

doesn't have embodiment doesn't have the

capacity to engage with the world and

that it could have understanding in the

same way that we do. I find that

implausible.

>> Interesting. But but again, you know, if

we do the the galaxy brain thing and we

say we can embed robots in the physical

world, give them sensory motor

affordances and all the rest of it. Um,

you know, there are many replies to the

Chinese room argument about this, like

the robot replying. Um, you know, would

would they have a little bit more

understanding?

>> Yeah, I mean that's getting more along

the lines of the kind of thing that

could have understanding. Um, I think

there's

sort of also more relevant stuff in the

background of what it is to be a

biological thing in that some things are

inherently meaningful to you and

relevant to you because they're

connected with the demands that the

challenges of your environment place to

you. Like I was saying before, you know,

life is a being alive is a way of being

always in a situation that is

problematic to you. Um, so saliency and

meaning I think are connected to that.

Um, it's not to say that you couldn't

have robots with more and more

precarious lifelike um situations and

maybe I don't know it's not beyond the

realms of possibility that then you

start seeing um understanding uh in ways

that are more like that as well.

>> Let's talk a little bit about um H

Highiger. So he spoke a lot about our

relationship with technology and I know

this is something you've been thinking

about. Can you tell us about that? So

one of the things that Haidiger said and

he in many ways he's a grandiose and

unpleasant person but he's kind of his

grandiosity kind of manifested in how he

could have read the importance of

philosophy to things about the modern

world today. So one of the things he

said was that technology and um

cybernetics for him we could say AI for

us today was the culmination of a

metaphysical tradition right so it's

because the history of philosophy set

out on the certain path that it did that

we are here today now with these um

technologies which seem to be kind of

having such a um transformative role in

our lives and and to the point that we

we feel like not in control of these

technologies. So I'm not a kind of

philosophical determinist like that. I

don't think you know just because some

philosophers in ancient Greeks said

certain things that this is why we have

AI today. But I think there are some

features of his the contrast he draws

with the philosophical tradition um in

his own account of what it is to be a

human person that give us like an

interesting perspective on like what is

being assumed in that path towards AI.

So one of the things he really insists

on is human finitudes like we are

inherently finite bounded knowowers as

communities and when we're communities

of knowers but that the philosophical

tradition has encouraged a kind of um

leap that encouraged the idea that

something about us as knowers kind of

crosses beyond the boundaries of

finitude into like a universal boundless

realm of knowledge. And I I do think

that

the very idea that a non situated sort

of non-embodied

absorber of facts like an LLM that kind

of just sits there and like sucks in all

the information in the world that that

could somehow be a counterpart to how we

know things as human beings. I think

that's an instantiation of this like

lack of acknowledgement of human

finitude because it's human finitude

like coming from a culture which is

expert at some things but not other

things and whose knowledge is grounded

in a discrete set of sensory experiences

that are not accessible to other people.

that boundedness of knowledge to say

that that is not inherent to what it is

to be a knower that a purely disembodied

um absorber of facts is what we are. I

think that sort of revealing that kind

of lack of acknowledgement of finitude

which is there in that tradition that he

criticized.

>> We're kind of moving into this

technologically embedded world and and

it's changing our nature in some way.

this tradition that Haidiger good

criticizes from that path from

philosophy to technology of this

aspiration to sort of transcend

embodiment, transcend materiality to

create for ourselves um this leap into a

almost spiritual world of pure

information.

And it's interesting how like technology

infrastructure is presented to

consumers, people like me, as like

behind the scenes, immaterial, not

really connected with real world

constraints. Like it's the cloud. It

floats above us. There's no [snorts]

there's no almost cost to it. It's

weightless. um that's not how technology

infrastructure works, but it seems like

that's what we'd like it to be. We'd

like this idea that all of this

information age that we have us around

today is not connected with uh real

world stuff like actually building

computers and shortages of resources and

consumption of energy and all of those

things. We like to think of it as

disconnected from that in ways that it's

obviously not. I agree that it's not

because clearly we we live in the

physical world but but there's an

apparent disconnection. There was the

digital divide of course in the 1980s

and it was very difficult even to to

work and get a job if you didn't know

about computers. Now even to get a

driving license, you know, it's all done

online.

>> The the the legal landscape is not

physical anymore. It's virtual. And even

like the European GDPR regulations as

Fluidi says, you know, you're you're a

digital entity and it's diffused and so

on. So,

>> you know, Facebook and whatnot. It

certainly feels like we live our world,

we live our lives in the information

world.

>> Yeah.

>> And it's becoming more and more kind of

confusing in that sense. Mhm.

>> So living in the information world, do

you mean by that kind of the information

that we get through devices, through

kind of this diffuse spread of

technologically connected things is more

salient to us than our experience with

like a concrete here and now situation.

You know, there was that John Ronson

book about um uh you've been shamed or

something like that, you know, basically

saying that there's there's almost more

controlling pressures in the social

media world than there is in our

physical world.

>> Sure. Sure. Yeah. I mean, I think all of

that is possible because human beings

are imaginative creatures. Like we live

in imaginative worlds and

it's not so much the social media is

always in our face. Well, maybe it is

people looking at the screens, but our

way of thinking through our own life

history and how we project and

everything else. There's a whole world

that we're also constructing around

that. So, I think we're cocreating this

digital world and it it doesn't work

unless we're imaginatively and

emotionally invested in this. So my view

goes back to this Canian idea um which

is also goes back to this idea of human

finitude that there's something wrong

with thinking that what you are as a

knower is the kind of being that can

float free of your environment and just

sort of regard it from above and like

take in all the information that's there

um as it is by itself without your

impact on the world. Um and my point is

that we're not those kinds of beings. We

only acquire knowledge through this

arduous process of interaction which

means that we cannot claim to have that

god's eye neutral view on things but

that it would be it's a mistake about

knowledge and about ourselves as knowers

to think that that is an aspiration that

makes sense for us.

>> Is it possible for both things to be

true at the same time? I'm I'm not

making any like weird claims that we're

we're not actually

>> really physically, you know, situated,

but but it's almost like we're

increasingly

disconnected in many aspects of our

mental life.

>> Mhm. From

>> from our direct physical experiences.

>> Yeah.

So I So I think that's true in terms of

like what we pay attention to. I mean,

people that look at their phones instead

of looking out the window when they're

on a train, not looking at the people

around them. So, there's this where our

attention goes can be wherever the

internet wants to place us. But the

phone is still like a concrete physical

device that with little um light

emmitting diodes that you know produce

like photons in our retinas, right? Um

so I think it's about again I go back to

the thing it's about the imaginary

around that around that that in our

minds

this isn't just a device that just is

showing images right now but it's this

portal to this other place where we can

be disconnected or dissociated from

where we are right now but I think that

need to live in an imaginary to be

disconnected is probably always there

and manifests in different ways in human

culture through fantasy, mythology, all

kinds of things

>> and and we can debate whether it is

imaginary or not with I interviewed

Charas about his reality plus book and

he was talking about you know whether

virtual worlds are real and stuff like

that. I mean certainly with social media

it is still real and you know like the

these people on Instagram they're having

real experiences and I'm vicariously

experiencing them through my phone.

>> So the question is whether that's a sort

of like a truncation of our mental life

or whether it's expansive. Yeah,

I mean this this is um something that I

think becomes a question of like ethics

and phenomenology. This like the part of

philosophy that like really studies

experience and what we draw from it in

its own sake. So there's there's always

an opportunity cost people if you're

looking at your friends absorbed into

one thing then you're not absorbed in

other things and ethically is it right

to do that? I think that's really

important. Um, we're running a big

experiment on the next generation

because kids,

young young children nowadays, spend a

lot less time looking at people's faces

than they used to. Will they be

socialized in a way that will allow them

to lead happy lives later on? We don't

know.

>> Is that something you worry about?

>> I do. I do. Yeah.

>> Tell me more.

>> Um, well, just because it's a massive

experiment. I mean, it's obvious that um

from developmental psychology that young

children are predisposed to be sort of

paying attention to social interactions

with the people around them, the gaze

and faces and features and all of that.

If they have less

opportunity to make those connections,

have those experiences at a very young

age, it seems like that's bound to have

an effect on how they relate to people

later on. don't see how it couldn't.

>> I mean, there's a thought experiment

that um I had a debate with an AI doomer

and because I'm a big externalist, I

think, you know, we're embedded in these

cognitive ecologies as you do. And, you

know, I gave the example, imagine that,

you know, some a child was brought up in

a hermetically sealed chamber and you

you gave them a computer and the

internet and they could, you know, they

could still learn to do lots of clever

things and so on. But, um, but we're

becoming a bit more like that. we're,

you know, it's not quite a hermetically

sealed chamber, but we're mediating

through the internet and so on. And that

could go one of two ways, right? As, as

you say, it could dramatically kind of

truncate our mental life and cause lots

of problems, or or maybe maybe it just

won't be a big problem.

>> Um, I mean, there were already

experiments done on monkeys in the 1950s

of depriving them of like maternal

contacts, and it didn't work out well

for those monkeys. I mean, should we be

doing that experiment on children? Sort

of depriving them of what is

instinctively the kind of social

engagement that, you know, young humans

seem to need to develop normally.

>> Amazing. Mita, thank you so much for

joining us today. It's been a great

conversation.

>> Thank you very much, Tim. I've enjoyed

it.