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Comment by ChuckMcM | Hacker News Reader

I suspect you're being snarky :-) but this is a really interesting question, and one that has had a lot of research done. I'm not current (I stopped following folks doing this research closely around 2019) but what we 'didn't' know about how brains work was still huge. Signaling levels, enzymes, the connectome, quantum effects, it is a really deep question.

That said, once we do get a working idea of how it works, and can perhaps synthesize a brain artificially with proteins, it will inform us on the next steps for silicon realization of that.

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I think the point is that tremendous complexity can arise from relatively simple mechanisms. That is what life is, at many levels. I’m not at all convinced that the current LLM approach will yield something we can broadly call consciousness but saying that it’s a simple concept and therefore won’t support consciousness is a specious argument imo.

I completely agree, tremendous complexity can arise from simple mechanisms. Gleick's Chaos is a really good introduction to that. I was talking about the article though, and the mechanisms currently used for training and inference in LLMs. Those mechanisms are mathematically precise (unlike Chaotic attractors) and as the author points out, achieve the same function as compressors do in a strict bit pattern minimization role. Sometimes tensor math is pretty complex, like the FFT and DCTs on JPEG compression, but with the same inputs you get the same results. And while a JPEG will never decompress to a different image than the one that was compressed in the first place, LLMs do not 'infer' token streams that haven't been trained in their training process. The big difference here is that if you imagine a JPEG compressor that compresses 100 different images into one 'chunk', you can see how to provoke it to produce any one of the images it previously compressed. And with a bit of creativity you can have it express different images in different parts of the resulting composite. FWIW I looked at patenting something like this for digital cameras to give them more "shots" space for a given amount of SD storage.[1]

Given the way that models work in 'inference' mode (vs 'training' mode) you can't forward bias the result into the correct result when there are multiple forward results that have identical weights. It's the root cause of hallucinations, and you've lost information in the training phase that you can't then use to discriminate between the 'right' answer and an equally valid 'wrong' answer.

[1] FWIW I could never recover enough state to insure that the image it regenerated was all of the same image you took. So you might get the street but one of the houses might be a house that was in a different picture you took. That kind of bug. Mostly arising out of the same kind of problem you have with using hashes to find documents, when you get a hash collision two documents have the same hash, so you don't know which one to return.

I do not understand this intuition that "true consciousness has to be random". The things that make me me are highly deterministic!

> LLMs do not 'infer' token streams that haven't been trained in their training process

While we're at it, this is simply untrue (in-context learning) unless you generalize "token streams" so radically that it could be readily analogized to humans as well.

> The things that make me me are highly deterministic!

Are they though? :-) There are some interesting papers in the tissue regeneration space which are working on building tissue (and organs) from stem cells for medical purposes (transplants, injury treatment, Etc.) and one of the things that comes out from that is that a set of stem cells make unique tissue every time in that it's compatible but the fine structure is always randomly different!

While the growth of brain matter is a minefield of ethical issues, at some point I suspect we're going to have to figure out how to do that to treat things like TBI and neurodegenerative diseases. In terms of understanding how randomness plays a part in your existence though cellular biology papers are a pretty good source.

Yes they are :-) At some point it's a question of definition. If I am anything, it's a pattern of behaviors; I am not defined by thermal noise. Thermal noise may be a reason my brain grew as it did, but having grown that way, I disagree that a version of me that grew the same way due to less random reasons would thus "not be me."

edit: And of course, any pattern that you can recognize about yourself, as "you", has to be deterministic by definition of pattern.

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