Let me tell you a little story. 1960s in

the summer, a little kid named Carl

[music] was playing around in the back

of his garden and he noticed all of

these wood lice crawling around. You

know, the little insects [music] that

can curl up into a ball. And what he

noticed was that depending on whether

they were in the sun or in the shade,

they would move faster or slower. They

behaved differently. And that's it.

Paul grew up to be Professor Carl

Friston, one of the most cited [music]

neuroscientists alive. He's been on this

channel before, more times than I can

count. And that childhood observation

about wood lice, [music] it never left

him. He spent decades developing what he

calls the free energy principle, which

tries to explain all of behavior with

one equation. perception, [music]

action, learning, why you scratch your

nose, all of it, [music] Fston claims,

comes down to minimizing a single

mathematical quantity. There's an old

physics joke, assume that we can model a

spherical cowl in a vacuum. The joke is

about how scientists grotesqually

[music]

simplify messy reality to tame it. The

free energy principle might be the

ultimate spherical cow. It promises

[music] to explain self-organization,

this bewilderingly complicated

phenomenon with something so emaciated

[music] we might as well call it

tortological. Even Friston himself

agrees with this by the way. This is

what he said to us last time we spoke

with him. The free energy principle is

not meant to be complicated or difficult

to understand. It's actually you know

almost logically simple. Um [gasps]

so the your the whole free engine

principle is just basically a principle

of least action pertaining to density

dynamics. The the the dynamics or the

evolution of not densities but

conditional densities. That's just it.

This is before thermodynamics. It's

before quantum mechanics. It's just

about conditional probability

distributions.

>> So what do we do with this? Has

Fristston actually found some deep truth

about how minds work? Or is he doing

what many scientists do, which is

mistaking the simplification for the

actual thing? Well, it turns out there's

a philosopher who has spent an

incredible amount of time thinking about

this exact problem. Professor Marvita

Chiramuta teaches at Edinburgh

University. Her book, The Brain

Abstracted, is basically about what

happens when neuroscientists simplify

brains to study them. What gets

captured? What gets lost?

>> One of the answers that might seem

obvious to people is that we pursue

science because we're curious. We just

want to know how the world works. We

want to

reveal discover the underlying

principles of the universe which apply

in all cases. switching off the idea

that you're just interested in nature

for its own sake out of curiosity and

saying, "Okay, how can we engineer these

systems to actually do things that we

want?" Getting them to behave in

artificial ways. If those

simplifications sort of allow you to

achieve your technological goals,

there's no in principle problem with

oversimplification. If you're going to

say, "I'm not just interested in nature

for its own sake. I just want applied

science." I should say, by the way, that

the brain abstracted probably influenced

my thinking more in 2025 than anything

else. She's an inspirational lady. I

look up to her very much, and certainly

thinking back on many of the episodes

we've done in 2025, I can see her

influence in the questions I ask and how

I think about things. So, here's her

starting point. Scientists have to

simplify. We're limited creatures trying

to wrap our heads around systems way

more complex than we can actually

comprehend. Our working memory holds

maybe seven items. Our attention is more

scattered than a group of toddlers with

iPads. Um, we die after 80 years if

we're lucky. So, we build models, right?

We leave stuff out on purpose. We tell

ourselves stories about how the world

works. But the question is, why does any

of this even work at all? Science is a

humanistic endeavor,

right? The purpose of science in the

universe is to make the universe

intelligible to us, not to control it,

not to predict it, and not to exploit

it. Now, you can do all those wonderful

things if you like, but in the end, as

far as I'm concerned, uh science is no

different from poetry is that we're

trying to make sense of the world,

trying to give it meaning, uh in

relation to our own existence.

>> If you'll allow the indulgence, I want

to tell a little story. It's a boxing

match in the red corner. Simplicius. He

thinks science works because the

universe is actually simple underneath.

Find an elegant equation and you've hit

the real thing. Simplicity tells you

that you're on the right track. And in

the blue corner, ignorantio. He thinks

we simplify because we're too dumb to do

otherwise. Our models work well enough

for our purposes, but they're

approximations, just useful fictions, if

you like. The map, not the territory.

Now both of them agree that scientists

need to simplify but where they disagree

is what that means about reality.

Simplicius had history on his side or at

least a certain type of history.

Galileo, Newton, Einstein, they all

believed pretty explicitly that nature

was fundamentally orderly and that

finding simple laws meant you'd found

something true.

Einstein famously said, "God doesn't

play dice." And no, he didn't actually

think God had anything to do with it,

but he was expressing faith that the

universe is at the very bottom legible.

Now, Chiramuta has gone allin on

ignorantio's position. She thinks

successful science tells us we've become

good at building useful simplifications,

and that doesn't prove that nature is

simple. The philosopher Nicholas of Kusa

had a phrase for this attitude, doctor

ignorant. Basically, learned ignorance.

You study hard, you learn a lot, and

what you learn includes what you don't

know. Now, when we interviewed

Cherimuta, she had been following

Francois Schlay's videos. And for those

of you who don't know, Francois is a

friend of the channel. He's our mascot.

He's one of my heroes. And um he's got

this idea called the kaleidoscope

hypothesis, which is basically that the

universe is made out of code. And

underneath all of the apparent gnarly

mess that we see, there is intrinsic

underlying structure. Everyone knows

where the kaleidoscope is, right? It's

like this uh cardboard tube with a few

bits of colored glass in it. these uh

these just like few bits of uh uh

original information get uh mirrored and

repeated and transformed and they create

uh this tremendous richness of complex

patterns. You know it's it's beautiful.

The kaleidoscope hypothesis is this idea

that the world in general and any domain

in particular follows the same structure

that it appears on the surface to be

extremely rich and complex and uh

infinitely novel with every passing

moment. But in reality it is made from

the repetition and composition of just a

few atoms of meaning. A big part of

intelligence is the process of mining

your experience of the world to identify

bits that are repeated

um and to extract them extract these

unique atoms of meaning. Uh when we

extract them we call them abstractions.

Now she's not saying that Chole is

wrong. She's saying that he's making a

philosophical bet. Might be right, might

be wrong. It's the same bet that Plato

made. 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.

>> Now I feel like I should defend Charlay

a little bit here you know because

obviously we love Charlay. He's not

making any weird metaphysical claims. At

least I don't think he is. If scientific

theories actually explained reality the

way it is, you would expect fewer

U-turns. Now, the biggest simplification

in the 21st century, the final boss of

simplifications is this idea that the

mind is a computer or that the mind is

running a software program. So, we have

inputs, we have processing, we have an

output. This metaphor has become so

established in the collective zeitgeist

that no one even questions it anymore.

It barely even registers in our brains

as a metaphor. So is it or isn't it

isn't it a little bit weird that

computation is this abstract formalism

like you know an an automter that makes

these state transitions something

completely non-physical and we're

describing the mind as if it is that

abstract thing that sounds a little bit

weird. There are many movies made about

this who talk about uploading their

minds into the matrix. Neuralink talks

about interfacing with your brain's

software. Yosha Bach thinks that

consciousness is a software program

running on your brain.

>> That this is the universal that you have

these invariances in nature that you can

have patterns that have causal power

that have the ability to reproduce

themselves [music] that have the ability

to shape reality that uh are invariances

that you cannot simply explain more

simply by looking at what atoms are

doing in space. But you have to look at

these abstract patterns to make sense of

them. every [music] other explanation is

going to be more complicated in the same

way as money is going to be impossibly

complicated if you try to reduce it to

atoms. So you have to look at these

causal invariances and spirits are

actually such causal invariances. They

are actually disembodied, right? They

they're not bodies. They're not stuff in

space. They're not mechanisms in the

same way, but they are causal mechanism,

abstract mechanisms. And so we put the

spirit back into nature using the

concept of software. A lot of people

think that's metaphorical, but I don't

think it's metaphorical at all. It's the

literal truth. Software is spirit. We're

all just talking about this stuff

without even batting an eyelid. Like,

where's the skepticism, man? It just

sounds so plausible to us. So, we assume

that it just kind of has to be the case.

There is something super interesting

about computers. What a computer

ultimately is is it's a causal

insulator.

The computer is a layer on which you can

produce an arbitrary reality. For

instance, the world of Minecraft. You

can walk around in the world of

Minecraft and it's running very well on

a Mac and it's running very well on a

PC. And if you are inside of the world,

you don't know what you're running on,

right? It's not going to have any

information about the nature of the CPU

that it's on, the color of the casing of

the computer, the voltage that the

computer is running on, the place that

the computer is standing in in the

parent universe, right? Our universe. So

the um computer is insulating this world

of Minecraft from our world. It makes it

possible that an arbitrary world is

happening inside of this box. And our

brain is also such a causal insulator.

It's possible for us to have thoughts

that [music] are independent of what

happens around us. Right? We can

envision a future that is not much

tainted by the present. We can remember

a past that is independent from the

present in which we are. And that's

necessary for us. Our brain has evolved

as such a causal insulator as well to

allow us to give rise to universes that

are different from this one. For

instance, future worlds so we can plan

for being in them. B says that money is

an example of a causal pattern. It's not

the ink on a bank note. It's not [music]

the electrons in your bank server. It

persists across and inconces in various

physical [music] instantiations. So

paper, coins, gold, digital ledgers. And

yet they say money causally affects the

world. It gets you fed. It starts wars.

It builds cities. He says that software

is the same. A program is an abstract

pattern that can run on many types of

chips, maybe even neurons. And that

pattern has causal power because it

controls whatever substrate it's running

on. [music] The same algorithm produces

the same effects regardless of what

physical stuff implements it. So the

invariance that sameness across

substrates is [music] the causal

mechanism the pattern itself at least

according to Yosha. He even accepts that

physics is causally closed. He says that

the abstract description and the

physical description are two ways of

looking at the same causal structure.

Neither is reducible to the other. Both

are real. But I'm pretty sure Chiramuta

would ask who identifies that invariance

when we say the same algorithm runs on

different chips. Completely different

things are actually physically

happening, right? Different voltages,

different electrons doing different

things. The sameness is something that

we impose. It exists in our description,

not in nature. And as for the money

example, money only works because of

human interpretive practices. Right? If

you take away the humans and their

agreements, it's just paper, right?

Money is just paper and the causal power

is actually in the social substrate that

participates in it. Now, I think Yosha

has taken a useful way of talking about

complex systems and promoted it to

metaphysics. And that's simplicious all

over again, right? Mistaking the

elegance of our descriptions for the

structure of reality itself. I mean,

maybe information really is more

fundamental than matter, but that's

another philosophical wager. And we've

made these bets many, many times before.

Just look at the history of all of this.

So, Daycart thought that the nervous

system worked like the hydraulic

automter in French royal gardens. Fluids

pumping through [music] tubes, pushing

levers. That was the high-tech metaphor

of his day. Later, when scientists

figured out that nerves carry electrical

signals, the brain became a telegraph

network. Then it was a telephone

switchboard, signals traveling down

wires, operators routting calls. And now

in our era, the brain is a computer. To

be precise about what we mean by

physical and everything has to be

physical because even GitHub, you know,

has to store its data in some sort of

hard drive or magnetic field or whatever

technology, but it's not storing it in

nothingness, you know. So, so knowledge

information always has this form of

physical embodiment.

I think we tend to think about it as

non-physical because it is a thing that

is not a thing which is the same as

temperature. You wake up, you look at

your phone and you see the temperature

and you decide how you're going to dress

and nobody has any doubt that

temperature is something that can be

measured. But it took about like 2,000

years for us, you know, as a species to

[music] figure out, you know, what

temperature was and the fact that it

could be measured. And there were two

fundamental difficulties that I would

say [music] made it difficult for us to

understand you know uh temperature. The

first one is that first people thought

that hot and cold were two separate

things. Okay. So that temperature was

like a mixture of the two. It's like

when you make green out of blue and

yellow. Okay. And it took a while for

people to understand that cold was the

absence of heat and not that cold and

heat were two different quantities that

were tempered together. They were mixed.

So temperature actually mix means

mixture not you know like what we

[music] now mean by temperature. The

other thing that was very difficult to

understand is that people thought that

temperature was a thing was some sort of

fluid that grabbed onto things. So let's

say if you had a steel uh rod that is

hot is that steel rod kind of like has

this sort of invisible fluid that is

heat and they had good reasons to

believe that it was an invisible fluid

because it could flow. Let's say you

could connect that rod to something that

was cold and that cold thing was going

to warm up because that fluid was going

to be flowing in that direction and so

forth. So they thought that it had a

physicality as a thing. A brilliant

Englishman Jou basically figures out

that that is not the case that you know

temperature is not a thing. And the way

that they do it is through this

observation in which I don't know if you

know how cannons used to be built, you

know. So if you just grab a piece of

sheet metal and you make it into a

cylinder and you try to make a cannon

out of that, the moment exactly that you

that you shoot the cannon, that's going

to open up like a flower in a cartoon,

you know, like like you know, like a

Looney Tunes type of situation. So what

they would do is they would make these

solid you know h cylinders of metal and

they would bore a hole in it you know to

create the cannons and boring those

holes released an enormous amount of

heat. So J thought well how come all of

that heat is there it's like an infinite

amount of heat. If I continue to bore a

hole in a piece of metal for an infinite

amount of time I'm going to it cannot be

a thing then. And that you know leads

him to realize that temperature is

actually something that has to live in

things but it's not a thing itself is

related to the kinetic energy of the

particles in the thing but it's not a

thing itself. It doesn't have its own

particle. [music] There isn't kind of

like a temperature particle. Temperature

is kind of like a property that matter

has and that holds on to things.

Knowledge is similar, you know, in that

it holds on to you and to me, you know,

and and and to the collective to exist,

but it doesn't have kind of like a

physicality in itself, but it always

exists in some sort of physical medium

or substrate. So, in that sense, it's

always going to be physical. No matter

how virtual it gets, it has maybe a

different type of physicality. But even

electromagnetic waves that are

transmitting, you know, data from your

Wi-Fi router to your laptop are

technically a physical embodiment. Now,

I spoke with Professor Luchiano Fuidi a

few years ago, and it was actually one

of my favorite ever episodes of MLST. I

think very highly of him, which is why

we're going to show some clips of him in

in this show because it's very apppropo.

But this is what he had to say about it.

Ontology, on the other hand, is how we

[snorts] structure the world in the

sense that we think that that's the way

it is. With the kind of eyes we have and

the kind of light around the world, that

those are the colors we we perceive. But

certainly a world full of colors uh is

the world which I take it to be the

world. That's my ontology.

Reontologizing means changing some of

that particular nature. Allow me a

distinction. So I hope it's not too

confusing. Reality in itself call it

system description of reality as we

perceive it enjoy it conceptualize it

live through model of the system.

Ontology to me is the ontology of the

model is not the metaphysics of the

system. I hope I haven't uh no made a

complete mess here. Okay. So metaphysics

noon system whatever the source of the

data that we get fantastic the data

don't speak about the source the music

of the radio is not about the radio but

there is a radio of course the music is

what we perceive the music has it own

ontology structure etc the model the

model is at that point what we enjoy why

dig the digital revolution has changed

the the nature of the world around us

not metaphysically but ontologically so

the ontologizing because some of the

things that we have inherited from

modernity a sense of the world that is

now being restructured and a certain

understanding of the world. So

re-epemologizing as well of that world.

We go back to this temptation of talking

about reality as if it were something

that we need to grasp, catch, portray,

uh hook uh spears. um when in fact uh

the the way I prefer to uh understand it

is as

malleable understandable in a variety of

ways um something that provides

constraints. It doesn't mean that you

can interpret in any possible way but

leaves room for different kind of

interpretations. So if the flow of data

that come from whatever is out there and

again I rather be sort of agnostic about

it can be modeled in a variety of ways.

Um one way is to especially 21st century

given the technology we have etc to

interpret that as know an enormous

computational kind of uh environment.

It's perfectly fine as long as we don't

think that there is a right metaphysics

is the correct ontology for the 21st

century. Now this is not relativism

because on the other hand different

models of the same system are comparable

depending on why you're developing that

particular model. And let me give you a

completely trivial example. Suppose you

ask me whether that building is the same

building.

That question has no real answer because

it depends on why you're asking that

question. If your question is asked

because you want to have directions I'm

going to say oh yeah that's the same

building. So the same building. Yeah.

Absolutely not. Go there, turn left. No

traffic lights. But if your question is

like same function as I know it's

completely different building. It was a

school now it's a hospital.

Next question. So is it or is it not the

same? That that question is the mistake.

an absolute question that provides no

interface what computer scientists call

level abstraction chosen for one

particular purpose so that I can compare

whether an answer is better than another

let me crack a joke for the philosophers

who might be listening this

is it the same or it's not the same who

is asking why because if it is the tax

man the tax man

you're doomed man I mean there is no way

you can play any oh I change every plank

that you're going to pay their tax. It's

the same ship. I don't care. But if it

is a collector, that ship is worth zero.

You change all the planks, you must be

joking. It's worthless. So, is it or is

it not the same? Depends on why you

asking that particular question. Tell me

why and I can give you the answer. No.

Why? In other words, no frame within

which we have chosen the interface that

provides the model of the system. No

potential answer. So the question is

like is the universe a computational

gigantic? Yes or no? Meaningless.

Is it worth modeling the universe as a

gigantia for the purpose of making sense

of our digital life? Oh yes, definitely.

Because we are informationational

organisms. Aha. So metaphysics. No, I

meant [music] in the 21st century the

best way of understanding human beings

today is as information organisms. Last

century we thought that biologically not

made much more sense. A lot of water and

a sprinkle a little bit of extra and so

on. Mechanism time etc. Not absolute

answers not relativistic answers but

relational answers. The relation between

the question the purpose and the actual

[music] answer. But it takes three not

two. So the computational model isn't

literally true but it's useful. [music]

The mistake is forgetting that it's a

model. So the early cybernetics guys, so

we mccullin pits, they knew that they

were working with analogies. McCullikin

pits wrote their famous paper showing

that neurons could theoretically work

like logic gates. Now they weren't

claiming neurons actually were logic

gates, but they were using it as a kind

of functional description. But somewhere

along the way, the metaphor hardened. A

lot of neuroscientists today don't say

that the brain is like a computer. They

say it is one and the metaphor became

the thing itself. Now Chiramuta

borrowing from Whitehead by the way she

said that this is the fallacy of

misplaced concreteness. This is another

one of those leaky abstractions I was

talking about. By the way there's a

great book called um the brain

abstracted by Marvit Chimuta. I

interviewed her recently and she said

that one of the most pervasive myths in

neuroscience is that we use these leaky

abstractions and idealizations to talk

about cognition and usually it's using

the most recent technology at the time.

So you know a few hundred years ago we

were describing the brain in terms of

pulley

>> pullies and levers. Yes,

>> that's right. And and you know and then

it was um you know as a prediction

machine as a computer and all this kind

of stuff.

>> At the end of this is an example of the

these are grounded things that we

understand. They're really good models

because we can both talk about

computers. We both know what computers

are, but the brain doesn't work like

that in any sense. Jeff Beg put it even

more bluntly when we spoke. It will

always be the case that our explanation

for how the brain works will be by

analogy to the most sophisticated

technology that we have.

Is that how's that for a non-answer?

Right. So, so you know you know couple

thousand years ago, right? How' the

brain work? It was like levers and

pulleys, man. I mean, duh. Don't be

ridiculous. Why? That was the, you know,

it at some point in the middle ages, it

became humors, right? Because fluid

dynamics was like the, you know, was the

kind of techn, you know, the technology

that was like the most advanced or

technology that took advantage of of

water power was like the most advanced

technology that we had. Now, the most

advanced technology is computers. So,

duh, that's exactly how the brain works.

>> Now, here's something that kind of bugs

me, right? You go into any AI conference

or you drink from the well of San

Francisco by spending too much time on

Twitter and you develop this mindset

that AGI is inevitable. You start

feeling the AGI and you'd be forgiven

for thinking this because I've been

using claude code and my god I feel that

there's been more interesting stuff

happening in the world of software

development in the last 6 months than

there has been in [music] the previous

20 years. This this technology is

genuinely amazing, but it is automation

technology. It's it's not really

intelligence, which means it's only

really as good [music] as your ability

to specify and supervise and delegate to

the system. But it is absolutely

amazing. But why do we have this view?

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

impossible so much as why does it seem

so possible so inevitable to people and

saying that what I'm arguing that is

that if you look at the history of the

development of the life sciences of

psychology 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 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 [music] actual

machine do what we do? So with all that

all of that mechanistic history in the

background, AI could seem very

inevitable. But if that [music]

mechanistic hypothesis is actually

wrong, then these claims for the

inevitability of a biological like AI

would not actually be wellounded. But we

could be subject to a kind of cultural

historical illusion that this is just

going to happen.

>> Cultural historical illusion. I've been

thinking about that phrase. um maybe our

confidence says more about what we've

inherited intellectually than about how

minds actually work. Now another thing

that um Marvita has inspired me to think

about a lot is the difference between

prediction and understanding. Indeed

when I interviewed the Nobel Prize

winner John Jumper at Google Deep Mind a

couple of months ago um this was the

question I asked and he had quite an

interesting way of distinguishing those

two things. It's almost like it's at any

point learning how to refine and

optimize the structure.

>> Okay. So I think we should distinguish

three things. Predict, control,

understand first.

>> So predict means that you say I'm going

to do a thing. What am I going to what

will be this value of my machine? What

will appear on my computer screen in the

future? That is predict. Control is I

want to measure this thing in the future

and I want it to come out 17. Right?

That's control. Understand is a lot like

predict except there's a human in the

loop. understand means that I have such

a small collection of facts that you

will predict and you will do it with

facts that I can communicate to another

human

um in kind of this compact fix fits on

an index card that's almost understand

and so I think these machines let us

predict they let us control

we have to derive our own understanding

at this moment right we can experiment

now on the artifact we can look at the

200 million predicted structures, not

just the 200,000 experimental structures

in order to help us understand, but it

doesn't do the act of understanding for

us. It does the act of predict and maybe

control.

>> The problem is these two goals actually

pull against each other. I think we're

at this moment in science now because we

have these tools like LLMs for language

and um connets and visual neuroscience

are being used um as predictive models

of neuronal responses which don't have

that mathematical legibility that

originally so when I was trained in the

field that people aspired to have and so

you have this um possible conflict you

can either

pursue that goal of understanding or you

can pursue the goal of prediction. But

it seems like you can't have both at the

same time.

>> Now, on the one hand, people go into

neuroscience because they want to

understand the mind. They want that

feeling where something clicks and you

suddenly get how it works. That's what

drew Chiramuta to the field in the first

place. That's what keeps people up late

at night reading papers. But on the

other hand, there's just prediction,

building tools that work. If your model

forecasts data accurately, maybe you

don't care whether it's true in some

deeper sense. So, LLMs are getting

unreasonably good. They are winning math

Olympiads. They are I mean, as of last

week, actually, GPT 5.2 apparently um

discovered a new theor Well, it's it's

solved one of these problems that

Terrence Tao had on on his website. This

is insane, but does it actually

understand anything? And does it matter

if it does or doesn't as long as it

works? Chomsky had an amazing commentary

on this a few years ago when we spoke

and I think it's still as relevant today

as it was then.

>> Suppose that I submitted an article to a

physics journal saying, "I've got a

fantastic new theory and accommodates

all the laws of nature, the ones that

are known, the ones that have yet to

have been discovered. And it's such an

elegant theory that I can say it in two

words. Anything goes." Okay, that

includes all the laws of nature. The

ones we know, the ones we do not know

yet, everything. What's the problem? The

[clears throat] problem is they're not

going to accept the paper. Because when

you have a theory, there are two kinds

of questions you have to ask. Why are

things this way? Why are things not that

way? If you don't get the second

question, you've done nothing. GPT3 has

done nothing.

>> Classic Chsky. So maybe theories are

overrated, maybe prediction is enough.

But Chiramuta worries about that

trade-off, right? When you give up on

understanding, you don't know when your

tools will break. You're stuck with

black boxes. They work until they don't

and you won't see it coming when they

don't. I spoke with philosopher Anna

Tunika about this recently and she had a

beautiful way of describing it.

>> Suppose [music] you want to climb a

mountain and you arrive on the top of

the mountain. What's the argument to say

that actually it's only when you're on

the top [music] of the mountain that

that what the climbing on the mountain

is. I mean you cannot really arrive on

the top of the mountain if you don't do

the first step. Every single step

matters. First step [music] is as

important as the last one. Actually we

are more conscious when we take the

first [music] steps in climbing the

mountains than when we are on the top of

the mountains and we have all this like

full-blown capacities [music] and

sometimes we shut ourselves in the legs.

And of course, I brought this up when I

debated Mike Israel. And the biggest

misconception in all of AI, what all of

the folks in San Francisco believe in is

this philosophical idea called

functionalism. That we're walking up the

mountain and when we get to the top of

the mountain, we have all of these

abstract capabilities like being able to

reason, play chess, but that disregards

that the path that you took walking up

the mountain is very important. and not

only the path, the physical

instantiation, the stuff that the

mountain is made out of. So Mike's view

is that if something produces

intelligent outputs, why does the

substrate matter? Silicon neurons, it

doesn't make any difference. It's all

information processing. Needless to say,

he pushed back hard. You can climb

mountains. You can touch stuff. But you

never truly embodied experience anything

if you push on that philosophical button

hard enough because you can always

abstract out to like these are just

neural network pings from groups of

neurons. And so you don't truly deeply

know anything in some kind of weird

philosophical way because it's just

neural network calculus all the way

down. You know, you climb the mountain,

that's cool. Helicopter can climb the

mountain much better than you. does not

have the ability to reason and

abstractly and plan and predict things

at all.

>> So, it's possible that what you can do

or how you can function isn't the whole

story. Or maybe if that's wrong, we

should just start using helicopters. So,

[snorts] individual minds are limited.

But what about collective minds? What

about humanity as a whole? We've built

this incredible thing over centuries,

right? Libraries, universities,

Wikipedia, an expanding [music] store of

knowledge that no single person could

ever hold. Doesn't that escape our

individual limitations? So there's this

dream of universal knowledge accessible

anywhere perspective free. There is a

tacid and implicit idea there that

knowledge is something that something

can have while my view is that knowledge

is a much more collective phenomenon.

Okay. So and it's not something also

that you can put in something like a

book. In my opinion the book doesn't

have knowledge. The book is an archival

record of some ideas that I was able,

you know, to put together in a nice

structure. But you cannot have a

conversation with the book. Knowledge

only can go to work when it's embodied.

You cannot throw like, you know, a bunch

of engineering manuals and cement into a

gorge and expect to get a bridge because

the books don't have knowledge. Teams

have knowledge. Organizations have

knowledge. Yes, knowledge is social.

Communities accomplish what individuals

can't. But collective knowledge is still

knowledge from somewhere. This matters,

right? It's shaped by particular

questions, particular tools, and

particular blind spots.

>> I think one of the interesting things

about this phenomenon, not only of LLMs,

but the internet as this idea that it's

the repository of all human knowledge is

that it goes along with this idea almost

that knowledge doesn't have to be

perspectival. It doesn't have to be like

of a place of a community. It kind of

can float free of the situation in which

this knowledge was acquired. That's kind

of the aspiration of these ideas sort of

of a universal repository of knowledge.

But what this perspectivalist position

actually sort of points us to is

actually knowledge is inherently

of a place um of a community. We acquire

knowledge not by being like completely

open-minded to everything that's

possible to know, but actually by sort

of narrowing our view, discounting

possibilities actually is what allows

you to pursue a line of inquiry and

actually pin down um some information

about say the natural world which is

humanly achievable. So the contrast I'm

trying to make here is between a view

which says that knowledge is

perspectival. It's inherently from a

human point of view which means that

it's inherently finite. We cannot aspire

to this sort of universal free floating

knowledge because as finite human beings

we can only achieve knowledge of the

world through recognizing our

limitations. And this notion of like you

can have non-perspectal knowledge like

everything in the internet based on like

all of the different possible

perspectives all blended together that

this somehow gives us a god's eye view.

LLMs aspire to be this like every person

voice, but it's precisely because they

don't have a particular so socialization

into a finite community that they're not

reliable that we can't pin them down to

actually um what would be a sort of

honest trustworthy perspective.

>> So Chiramuta has this idea that she

[music] calls haptic realism. Most of

the philosophy of science treats

knowledge like vision. You stand back

and you observe reality from a distance.

She thinks it's more like touch.

>> 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.

>> Neuroscientists are more than passive

observers of brains. They poke them,

they prod them, they stimulate them,

they model them, and in doing that, they

change what they find. The patterns that

emerge are real, but they're also

partially created by the process of

investigating itself. It takes all the

messiness of biological cognition and it

reduces it to one imperative. Minimize

free energy. Everything else supposedly

follows from that. Now, Simplicius loves

this. I mean, finally, the simple truth,

the one principle to explain it all. But

Ignorantio says, "Wait a minute. The

math is elegant. The framework is

unified, but does that mean it's

captured what brains actually are? Or

did we just build another beautiful

simplification and started forgetting

that it was a simplification? So,

Chiramuta said to me that we should ask

different questions, right? Not is this

true, but what does this help us do?

What does this light up? What does it

leave in the darkness? And the other

thing, of course, is that we are finite

biological creatures, right? We there

are limits to our cognition and Chomsky

spoke about this fascinating concept of

a cognitive horizon when we when we

chatted with him.

>> If we are organic creatures, we're going

to be like other organic creatures and

that there are bounds to our cognitive

capacities. So, for example, a rat can

be trained to run pretty complicated

mazes, but it can't be trained to learn

a prime number maze. Turn right at every

prime number. It just doesn't have the

concept. And no matter how much training

you do, you're not going to get

anywhere. Well, I suspect there's

reasons to suppose we're like rats. We

have capacities. We have a nature. We

have a structure. They yield all sorts

of extensive range of things that we can

do, but they probably impose limits. And

I think we could even make some guess

about what these limits are.

>> So our best theories, they bump up

against the walls of the limits of our

cognition, of our cognitive horizon. And

maybe that's fine. But maybe even

knowledge of where the walls are is

useful in of itself. Science makes

things simple and it's not a flaw,

right? Without simplification, we'd have

nothing. You can't study everything at

once. But simplification has risks,

right? You forget your model is a model.

You mistake elegance for truth. And you

think you found solid ground when really

you're just building another floor. So

look at Opus 4.5, right? Foundation

models today. They are artifacts of

staggering complexity. We've trained

them on everything humans have ever

written. We treat their outputs like

they came from somewhere authoritative,

somewhere outside of us, somewhere that

knows, but the knowing was ours all

along, right? Just compressed,

refracted, reflected back to us from the

silicon. Whether that reflection

captures the actual thing, that is a

question that we're barely starting to

ask. You can use powerful frameworks

like the free energy principle, but just

remember, they're frameworks, right?

They're tools for building. They're not

the final word. So the brain is not a

hydraulic pump. It's not a computer.

It's not a telephone network. It's

probably not a free energy minimizer

either. I mean, at least not in some

like literal way. What the brain

actually is, we will only ever caps

glimpses of, right? That is through our

limited instruments and theories. And

that's okay because that's what it means

to be finite. So Chiramutus, he had this

amazing example from Greek mythology uh

called Proteius, right? And if you could

pin him down, he'd have to answer your

question correctly. But if you let go

and you let him get away, then he would

shapeshift and shapeshift. Nature is

like that, right? You can pin it down,

you can ask questions, but it's always

perspectival. As soon as you let go,

there's always a myriad of other

perspectives that can be interpreted

from reality. Carl Friston's woodlice,

they were doing something very similar,

right? So slow down in the sun, move

faster in the shade. But Friston isn't a

woodlouse and neither are you.