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by kybernetikos
766 days ago
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This still doesn't make sense to me. As far as I'm concerned the gold standard of understanding something is being able to construct a program that replicates it, which is exactly what we can do with LLMs. We know exactly how llms work (relatively simple maths), and to a large extent even why they work (backpropagation updates weights to more closely approximate the desired function). There are open questions relating to LLMs of course - we don't understand what the space of potential LLM-like things looks like and how the features in that space relate to subjective performance (although note that transformers were designed based on a theory that they would perform better, not just randomly generated or inspired by the muse). We also don't know to what extent the output of LLMs can be approximated by simpler symbolic systems, or how to extract such systems from LLMs when they do exist. Those are really interesting questions, but they're not questions about 'how LLMs work'. I dislike the 'LLMs are magic' framing that seems to be taking over the world. Nobody thinks that Taylor expansion is magical, but LLMs are doing the same sort of thing - approximating a function through a bunch of weights on a bunch of simpler functions. Just because the function we're approximating (intelligent output) is not known in advance (but can be sampled), and multi-dimensional does not fundamentally change how mysterious the process is. |
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Cloning animals or even humans did not automatically make us understand how brains work. In fact, these were quite unrelated endeavors.
> I dislike the 'LLMs are magic' framing that seems to be taking over the world
Don't take that out on me. That's not what I'm saying. I'm saying there is a lack of determinism (mathematically provable, per se) in our current understanding of all AI (LLM included). There are many attempts to solve this problem. I've sat in on seminars about it myself. So far, we're not there yet