Could this lead us to being able to upload our brains onto computers? To kill death. Very cool.
No.
Deciphering language processing in the human brain through LLM representations
21–30 of 111 posts
Re: Deciphering language processing in the human brain through LLM representations
#22Earlier quoted context omitted.
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.
Yeah, Taleb has some pretty convincing arguments that correlation is generally bad/misleading and entropy is better.
Re: Deciphering language processing in the human brain through LLM representations
#23Earlier quoted context omitted.
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.
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Re: Deciphering language processing in the human brain through LLM representations
#24I 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 throug…
That certainly would explain how Trump can rattle on for hours and hours, despite a notable lack of high quality training data.
Re: Deciphering language processing in the human brain through LLM representations
#25I 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.
Correlation of 0.25-0.5 being poor is very problem dependent.
For example, in difficult perceptual tasks ("can you taste which of these three biscuits is different" [one biscuit is made with slightly less sugar]), a correlation of 0.3 is commonplace and considered an appropriate amount of annotator agreement to make decisions.
Re: Deciphering language processing in the human brain through LLM representations
#26Re: Deciphering language processing in the human brain through LLM representations
#27Earlier quoted context omitted.
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.
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> the vast number of internal models of the world that are generated strictly from text input
Do you mean in the sense than an LLM can emit documents following grammar rules or pirate-talk?
For humans, text doesn't generate our models so much as as it triggers them.
Re: Deciphering language processing in the human brain through LLM representations
#28I 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 do not understand what you find convincing about this that changes your mind.
We have a closed system that we designed to operate in a way that is similar to our limited understanding of how a portion of the brain works, based on how we would model that part of the brain if it had to traverse an nth-dimensional array. We have loosely observed it working in a way that could roughly be defined as similar to our limited understanding of how a portion of the brain works given that limitation that we know is not true of the human brain, with a fairly low confidence level.
Even if you put an extreme level of faith into those very subpar conclusions and take them to be rigid... That does not make it actually similar to the human brain, or any kind of brain at all.
Re: Deciphering language processing in the human brain through LLM representations
#29Earlier quoted context omitted.
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.
Correlation of 0.25-0.5 being poor is very problem dependent. For example, in difficult perceptual tasks ("can you taste which of these three biscuits is different" [one biscuit is made with slightly less sugar]), a correlation of 0.3 is commonplace and considered an appropriate amount of annotator agreement to make decisions.
Yes for certain things like statistical trading (assuming some kind of "nice" Gaussian-like distribution) where you have lots of trades and just need to be more right than wrong it's probably useful.
Not here though, where you are trying to prove a (near) equivalence.
Re: Deciphering language processing in the human brain through LLM representations
#30I 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 further evidence that we less different from "stochastic parrots" than we would like to believe.