Earlier quoted context omitted.
> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago. Well for something to be G or I you need them to solve novel problems. These things have interested most of the Internet and I've yet to see a "reasoning" disentangle memorization from reasoning. Memorization doesn't mean they aren't useful (not sure why this was ever conflated since... Computers are useful...), but it's…
I have seen far, far too many people say things along the lines of "Sure, LLMs currently don't seem to be good at [thing LLMs are, at least as of now, fundamentally incapable of ], but hey, some people are pretty bad at that sometimes too!" It demonstrates such a complete misunderstanding of the basic nature of the problem that I am left baffled that some of these people claim to actually be in the machine-learning f…
The issue is that it's not LLMs that can't perform a given task, but that computers already can. Counting the number of Rs in strawberry or comparing 9.11 to 9.7 is trivial for a regular computer program, but hard for an LLM due to the tokenization process. Where LLMs are a pile of matrixes and some math and some look up tables, it's easy to see that as the essential nature of LLMs, which is to say theres no thinking or reasoning happening because it's just a pile of math happening and it's just glorified auto-complete. Artificial things look a lot like the thing they resemble, but they also are artificial, and as such, are markedly different from the thing they resemble. is the very nature of an LLMs being a pile of math mean that it can not perform said task if given more math and more compute and more data? given enough compute, can we change that nature?
I make no prognostication as to whether or not AGI will come from transformers, and this is getting very philosophical, but I see it as irrelevant because I don't believe that AGI is the right measure.