>it is repeating the most common...
This nonsense hasn't been true since GPT-2, and even before that it was a poor description.
For instance, do you think one just solves dozens of Erdős problems with the "most common textual sequence": https://github.com/teorth/erdosproblems/wiki/AI-contribution...
A slight oversimplification, as LLMs are also capable of generating the most statistically plausible textual sequence, which can be a sequence not found in the dataset but rather a synthesized combination of the likely sequences of multiple preceding sets of tokens, but yes, that is in fact what it is doing. Computer software does what it is programmed to do, and LLMs are not programmed to do logical inference in any capacity but rather operate entirely on probabilities learned from a mind-bogglingly large corpus of text (influenced by things like RLHF, which is still just massaging probabilities).
The claims about solving Erdos problems have been wildly overstated, and notably pushed by people who have a very large financial stake in hyping up LLMs. Nonetheless, I did not say that LLMs are useless. If they are trained on sufficient data, it should not be surprising that correct answers are probabilistically likely to occur. Like any computer software, that makes them a useful tool. It does not make them in any way intelligent, any more than a calculator would be considered intelligent despite being completely superior to human intelligence in accomplishing their given task.
>not programmed to do logical inference in any capacity
Yet have no problem doing so when solving Erdős problems. This isn't up for debate at this point.
>The claims about solving Erdos problems have been wildly overstated
These are verified solutions. They exist, are not trivial, and are of obvious interest to the math community. Take it up with Terence Tao and co.
>pushed by people who have a very large financial stake in hyping up LLMs
Libel.
>It does not make them in any way intelligent
Word games.
Honestly big noobquestion: isn't math just very very nested patternmatching based on a few foundational operators? ive always felt, that im bad at math, cause i forget all the rules, but seeing solutions (and knowing the used pattern) always made "sense".
I always thought the hard math problems are so deeply nested or you have to remember trick xyz that people just didnt think about it yet..
The amount of mathematical structures and transformations you can apply (the possible rules) is effectively infinite. Simply remembering the rules might work at first, but you'll soon run into the combinatorial explosion: https://en.wikipedia.org/wiki/Combinatorial_explosion
You could go a step further, and simply say "well, ok, then the LLMs are merely doing some form of incremental/heuristic search!". Yes, but at that point you'd also be hard-pressed to claim that humans themselves are doing anything beyond that. You run out of naturalistic explanations.
> This isn't up for debate at this point.
If by not up for debate, you mean that it is delusional and literally evidence of psychosis to suggest that computer software is doing something it is not programmed to do, you would be correct. Probabilistic analysis can carry you very, very far in doing something that looks like logical inference at the surface level, but it is nonetheless not logical inference. LLM models have been getting increasingly good at factoring in larger and longer contexts and still managing to generate plausibly correct answers, becoming more and more useful all the while, but are still not capable of logical inference. This is why your genius mathematician AGI consciousness stumbles on trivial logic puzzles it has not seen before like the car wash meme.
>delusional and literally evidence of psychosis to suggest that computer software is doing something it is not programmed to do
These are just insults and outright lies, and you know that. We're done here.
AI progress from here on out will be extra sweet.
You don't have the ability to predict progress, either.
Well, I'm not clairvoyant, but this is a very easy prediction to make. And we're not talking about decades in the future, this is simply a matter of letting the near-future unfold.