How LLMs are unlike the human brain – Andrej Karpathy

What is it the part about human

intelligence that we like have most

failed to replicate with these models?

Um I almost feel like um just uh just a

lot of it still. So maybe one way to

think about it, I don't know if this is

the the best way, but I almost kind of

feel like again making these analogies

imperfect as they are. Um we've stumbled

by with the transformer neural network,

which extremely powerful, very general.

You can train transformers on audio or

video or text or whatever you want and

it just learns patterns and they're very

powerful and it works really well. That

to me almost indicates that this is kind

of like some piece of cortical tissue.

Uh it's something like that because the

cortex is famously very um plastic as

well. you can rewire um you know parts

of brains and there was the slightly

gruesome experiments with rewiring like

visual cortex to the auditory cortex and

this animal like learn fine etc. Um, so

I think that this is kind of like

cortical tissue. I think when we're

doing reasoning and planning inside the

neural networks, so basically doing a

reasoning traces um for thinking models,

that's kind of like the prefrontal

cortex. Um, and then um I think we uh

maybe those are like little check marks,

but I still think there's many uh brain

parts and nuclei that are not explored.

So maybe for example there's a basic

ganglia doing a bit of reinforcement

learning when we fine tune the models on

reinforcement learning but you know

whereas like the hippocampus not obvious

what that would be some parts are

probably not important maybe the

cerebellum is like not important to

cognition it's thought so we can skip

some of it uh but I still think there's

for example the amydala all the emotions

and instincts um and there's probably

like a bunch of other nuclei in the

brain that are very ancient that I don't

think we've like really replicated I

don't actually know that we should be

pursuing you know the building of an

analog of human brain I'm again an

engineer mostly heart. But um I still

feel like maybe another way to answer

the question is you're not going to hire

this thing as an intern and it's missing

a lot of it's because it comes with a

lot of these cognitive deficits that we

all intuitively feel when we talk to the

models.

>> Um

>> and so it's just like not fully there

yet. You can look at it as like not all

the brain parts are checked off yet.

>> This is maybe relevant to the question

of thinking about how fast these issues

will be solved. So sometimes people will

say about continual learning. Look

actually you could already you could

easily replicate this capability just as

in context learning emerged

spontaneously as a result of

pre-training

continual learning over longer horizons

will emerge spontaneously if the model

is incentivized to recollect information

over longer horizons or horizons longer

than one session. So if there's um some

like outer loop RL which has many

sessions within that outer loop then

like this continual learning where it

uses like it fine-tunes itself or it

writes to an external memory or

something will just sort of like emerge

spontaneously. Do you think

>> do you think things are that are

plausible? I just I don't have really a

prior over like how plausible is that?

How likely is that to happen?

>> I don't know that I fully resonate with

that because I feel like these models

when you boot them up and they have zero

tokens in the window, they're always

like restarting from scratch where they

were. So I don't actually know in that

worldview what it looks like. Uh because

um again making maybe making some

analogies to humans just because I think

it's roughly concrete and kind of

interesting to think through. I feel

like when I'm awake I'm building up a

context window of stuff that's happening

during the day but I feel like when I go

to sleep something magical happens where

uh I don't actually think that that

context window stays around. Um I think

there's some process of distillation

into weights of my brain. Yeah.

>> Um and this happens during sleep and all

this kind of stuff. We don't have an

equivalent of that in large language

models. And that's to me more adjacent

to when you talk about continual

learning and so on as absent. These

models don't really have this

distillation phase um of taking what

happened, analyzing it, obsessively

thinking through it

>> um basically doing some kind of a

synthetic data generation process and

distilling it back back into the weights

and maybe having uh you know specific

neural net per person. uh maybe it's a

Laura it's not a full uh yeah it's not a

full weight uh neural network that's

it's just small some of the small sparse

subset of the weights are changed

>> but basically we do want to create ways

of creating these individuals that have

very long contexts it's not only

remaining in the context window because

the context windows grow very very long

like maybe we have some very elaborate

sparse attention over it

>> but I still think that humans obviously

have some process for distilling some of

that knowledge into the weights we're

missing it and I do also think that

humans um have some kind of a very

elaborate sparse attention scheme.

>> Um which I think we're starting to see

some early hints of. Uh so Deepseek v3.2

just came out and I saw that they have

like a sparse attention as an example

and this is one way to have very very

long context windows. So I almost feel

like we are redoing a lot of the

cognitive tricks that evolution came up

with through a very different process,

but we're I think going to converge on a

similar architecture cognitively. If you

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