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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.

by ChuckMcM7 hours ago|

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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.

by FeepingCreature3 hours ago|

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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.