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Deciphering language processing in the human brain through LLM representations

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Re: Deciphering language processing in the human brain through LLM representations

#11

Post by hacker_homie »

cs702 wrote: post #6

I view this as compelling evidence that current models are more than "stochastic parrots," because as the OP shows, they are learning to model the world in ways that are similar (up to a linear transformation) to those exhibited by the human brain. The OP's findings, in short: * A linear transformation of a speech encoder's embeddings closely aligns them with patterns of neural activity in the brain's speech areas in…

I view this as the language processing is similar but we’re not talking about thinking, just processing language.

I see this as maybe it’s not a statistical parrot, but it’s still only some kind of parrot, Maybe a sleep deprived one.

Re: Deciphering language processing in the human brain through LLM representations

#12

Post by [deleted] »

willy_k wrote: post #9
goatlover wrote: post #3

Earlier quoted context omitted.

Why do you want hackable brains?

Brains are already hackable in multiple senses. Namely through exogenous chemicals, electrical stimulation, hypnosis, and combinations of these. They aren’t necessarily reverse engineerable, which is what computational models like LLM-tied ones would enable, but they are hackable. We have the Cold War to thank for that.

[deleted]

Re: Deciphering language processing in the human brain through LLM representations

#13

Post by hakaneskici »

cs702 wrote: post #6

I view this as compelling evidence that current models are more than "stochastic parrots," because as the OP shows, they are learning to model the world in ways that are similar (up to a linear transformation) to those exhibited by the human brain. The OP's findings, in short: * A linear transformation of a speech encoder's embeddings closely aligns them with patterns of neural activity in the brain's speech areas in…

> current models are more than "stochastic parrots"

I believe the same, and also I'm willing to accept that the human brain can intentionally operate in an stochastic parrot mode.

Some people have the ability to fluently speak non-stop, completely impromptu. I wonder if it's similar to an LLM pipeline, where there'a constant stream of thoughts being generated based on very recent context, which are then passed through various output filters.

Re: Deciphering language processing in the human brain through LLM representations

#14

Post by johnisgood »

outworlder wrote: post #10
taosx wrote: post #5

Earlier quoted context omitted.

Due to my current condition. I feel that I could do more both for myself and the world but unfortunately motivation plays a big role or otherwise I have to trick myself into feeling stressed in order to do things like work that might be boring or feeling observer. So many reasons: absorb information faster; improve spatial visualization; motivation, intrinsic motivation hacking ; simulations...etc Give me the code to…

The problem is that it would also let others edit your brain. Even indirectly.

You can do that already, it is called behavior modification. :P And medications!

Re: Deciphering language processing in the human brain through LLM representations

#15

Post by [deleted] »

hacker_homie wrote: post #11
cs702 wrote: post #6

I view this as compelling evidence that current models are more than "stochastic parrots," because as the OP shows, they are learning to model the world in ways that are similar (up to a linear transformation) to those exhibited by the human brain. The OP's findings, in short: * A linear transformation of a speech encoder's embeddings closely aligns them with patterns of neural activity in the brain's speech areas in…

I view this as the language processing is similar but we’re not talking about thinking, just processing language. I see this as maybe it’s not a statistical parrot, but it’s still only some kind of parrot, Maybe a sleep deprived one.

[deleted]

Re: Deciphering language processing in the human brain through LLM representations

#16

Post by johnisgood »

taosx wrote: post #5
goatlover wrote: post #3

Earlier quoted context omitted.

Why do you want hackable brains?

Due to my current condition. I feel that I could do more both for myself and the world but unfortunately motivation plays a big role or otherwise I have to trick myself into feeling stressed in order to do things like work that might be boring or feeling observer. So many reasons: absorb information faster; improve spatial visualization; motivation, intrinsic motivation hacking ; simulations...etc Give me the code to…

Have you ever considered or tried nootropics before?

Nootropics may help with stimulation, as well as memory and cognition in general. There is a whole community dedicated to it with effective stacks.

Re: Deciphering language processing in the human brain through LLM representations

#18

Post by foobarqux »

cs702 wrote: post #6

I view this as compelling evidence that current models are more than "stochastic parrots," because as the OP shows, they are learning to model the world in ways that are similar (up to a linear transformation) to those exhibited by the human brain. The OP's findings, in short: * A linear transformation of a speech encoder's embeddings closely aligns them with patterns of neural activity in the brain's speech areas in…

The correlations are 0.25-0.5, which is quite poor (Gaussian distribution plots with those correlations look like noise). That's before analyzing the methodology and assumptions.

Re: Deciphering language processing in the human brain through LLM representations

#19

Post by ogogmad »

foobarqux wrote: post #18
cs702 wrote: post #6

I view this as compelling evidence that current models are more than "stochastic parrots," because as the OP shows, they are learning to model the world in ways that are similar (up to a linear transformation) to those exhibited by the human brain. The OP's findings, in short: * A linear transformation of a speech encoder's embeddings closely aligns them with patterns of neural activity in the brain's speech areas in…

The correlations are 0.25-0.5, which is quite poor (Gaussian distribution plots with those correlations look like noise). That's before analyzing the methodology and assumptions.

Isn't correlation a wrong way of measuring statistical dependence anyway? I thought that entropy-based method like Relative Entropy did this better.
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