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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

#41

Post by whymeogod »

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…

meditation, if you want to try a drug free approach.

Make it simple. Stare at a clock with a big second hand. Take one breath every 15 seconds. Then, after a minute or so, push it out to 20 seconds, then one every 30 seconds.

For the 30, my pattern tends to stabilize on inhale for 5-7, hold for 5-7, and then a slow exhale. I find that after the first exhale, if I give a little push I can get more air out of my lungs.

Do this once a day, 7-10 minutes session, for a week, and see if things aren't a little different.

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

#42

Post by hatthew »

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…

Yeah, I have always firmly maintained that there is less fundamental difference between LLMs and human brains than most people seems to assume.

Going a bit further, I'll speculate that the actions made by a human brain are simply a function of the "input" from our ~5 senses combined with our memory (obviously there are complications such as spinal reflexes, but I don't think those affect my main point). Neural nets are universal function approximators, so can't a sufficiently large neural net approximate a full human brain? In that case, is there any merit to saying that a human "understands" something in a way that a neural net doesn't? There's obviously a huge gap between the two right now, but I don't see any fundamental difference besides "consciousness" which is not well defined to begin with.

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

#43

Post by famouswaffles »

fernly wrote: post #38

I find the OP very difficult to comprehend, to the point that I question whether it has content at all. One difficulty is in understanding their use of the word "embedding", defined (so to speak) as "internal representations (embeddings)", and their free use of the word to relate, and even equate, LLM internal structure to brain internal structure. They are simply assuming that there is a brain "embedding" that can b…

>They mention a profound difference in the opening paragraph, "Large language models do not depend on symbolic parts of speech or syntactic rules. "Human language models very obviously and evidently do.

Honestly do they ? To me, they clearly don't. Grammar is not how language works. It's useful fiction. Language even in humans seems to be a very statistical process.

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

#44

Post by canjobear »

fernly wrote: post #38

I find the OP very difficult to comprehend, to the point that I question whether it has content at all. One difficulty is in understanding their use of the word "embedding", defined (so to speak) as "internal representations (embeddings)", and their free use of the word to relate, and even equate, LLM internal structure to brain internal structure. They are simply assuming that there is a brain "embedding" that can b…

> They are simply assuming that there is a brain "embedding" that can be directly compared to the matrix of numerical weights that comprise an LLM's training.

If there were no such structure, then their methods based on aligning neural embeddings with brain "embeddings" (really just vectors of electrode values or voxel activations) would not work.

> They mention a profound difference in the opening paragraph, "Large language models do not depend on symbolic parts of speech or syntactic rules". Human language models very obviously and evidently do. On that basis alone, it can't be valid to just assume that a human "embedding" is equivalent to an LLM "embedding", for input or output.

This feels like "it doesn't work the way I thought it would, so it must be wrong."

I think actually their point here is mistaken for another reason: there's good reason to think that LLMs do end up implicitly representing abstract parts of speech and syntactic rules in their embedding spaces.

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

#45

Post by AlotOfReading »

hatthew wrote: post #42
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…

Yeah, I have always firmly maintained that there is less fundamental difference between LLMs and human brains than most people seems to assume. Going a bit further, I'll speculate that the actions made by a human brain are simply a function of the "input" from our ~5 senses combined with our memory (obviously there are complications such as spinal reflexes, but I don't think those affect my main point). Neural nets a…

The UAT is a pretty weak result in practice. A lot of systems have the same property, and most of them are pretty poor approximators in practice. It may very well be that no reasonable amount of computing power allows approximating the "function of consciousness". Plus, if you're a certain kind of dualist the entire idea of a compact, smooth "consciousness" function may be something you reject philosophically.

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

#46

Post by dboreham »

hatthew wrote: post #42
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…

Yeah, I have always firmly maintained that there is less fundamental difference between LLMs and human brains than most people seems to assume. Going a bit further, I'll speculate that the actions made by a human brain are simply a function of the "input" from our ~5 senses combined with our memory (obviously there are complications such as spinal reflexes, but I don't think those affect my main point). Neural nets a…

Ah, so there's two of us now :)

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

#48

Post by foobiekr »

wao0uuno wrote: post #20
ogogmad wrote: post #17

Could this lead us to being able to upload our brains onto computers? To kill death. Very cool.

Would your brain uploaded onto computer still be you?

Hans Moravec has an incrementalist approach to this which I think actually does accomplish eliminating the copy issue.

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

#49

Post by hatthew »

AlotOfReading wrote: post #45
hatthew wrote: post #42

Earlier quoted context omitted.

Yeah, I have always firmly maintained that there is less fundamental difference between LLMs and human brains than most people seems to assume. Going a bit further, I'll speculate that the actions made by a human brain are simply a function of the "input" from our ~5 senses combined with our memory (obviously there are complications such as spinal reflexes, but I don't think those affect my main point). Neural nets a…

The UAT is a pretty weak result in practice. A lot of systems have the same property, and most of them are pretty poor approximators in practice. It may very well be that no reasonable amount of computing power allows approximating the "function of consciousness". Plus, if you're a certain kind of dualist the entire idea of a compact, smooth "consciousness" function may be something you reject philosophically.

I agree there are issues with the UAT, but I feel like my conclusion is still valid: a neural net, given the memories and senses that a humans has, is capable of approximating a human's response accurately enough to be indistinguishable from another human, at least to another human.

I philosophically reject the notion that consciousness is an important factor here. The question of whether or not you have a consciousness doesn't affect what I take away from this conversation, and similarly the question of whether an AI has a consciousness doesn't affect what I take away from my actions with it. If the (non-)existence of others' consciousnesses doesn't materially affect my life—and we assume that it's a fundamentally unanswerable question—why should I care other than curiosity?

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

#50

Post by achierius »

famouswaffles wrote: post #43
fernly wrote: post #38

I find the OP very difficult to comprehend, to the point that I question whether it has content at all. One difficulty is in understanding their use of the word "embedding", defined (so to speak) as "internal representations (embeddings)", and their free use of the word to relate, and even equate, LLM internal structure to brain internal structure. They are simply assuming that there is a brain "embedding" that can b…

>They mention a profound difference in the opening paragraph, "Large language models do not depend on symbolic parts of speech or syntactic rules. "Human language models very obviously and evidently do. Honestly do they ? To me, they clearly don't. Grammar is not how language works. It's useful fiction. Language even in humans seems to be a very statistical process.

Linguists however know that grammar is, indeed, important for linguistic comprehension. For example, the German "Ich sehe die Frau mit dem Fernglas" (I see the woman with the binoculars) is _unambiguous_ because "die Frau" and "mit dem Fernglas" match in both gender and case. If this weren't the case, it could be either "I see (the woman with the binoculars)" or "I see (the woman) with [using] the binoculars". Even in German you might encounter this e.g. if you instead had to say "Ich sehe das Mädchen mit dem Fernglas", as das Mädchen (the girl) is neuter rather than feminine in gender.
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