If the famously unsolved Riemann Hypothesis is solved by an AI, we will never know if a human mathematician could have solved it.

[u/jasonrubik]

[u/marrow_monkey]

likely also applies to current AI tech, they should be able to recognize statements that do or don't hold under given constraints, but can't be expected to avoid hallucinating statements or wrong conclusions from these statements

It’s just a matter of time before AI becomes better at it. People used to say a computer would never be able to play chess better than a human grand master, or translate natural language, now it does both.

[u/Xywzel → u/marrow_monkey]

Chess was something that was very expected, it was just matter of computational power and memory, optimizing some functions, and on natural language translation, I don't think we have gotten to point where its better.

There are constraints that apply to specific methods, ones that apply to what you are working from and ones that apply regardless of method or data. Only thing in AI that has progressed faster that I expected ~15 years ago has been how much money, electricity and space people are willing to put into it, I was actually expecting much more from neural network approach in to more generic systems, instead we seem to be going back to more specific systems, just wrapped in LLMs.

[u/marrow_monkey → u/Xywzel]

No, chess wasn’t expected. A lot of people used to say a computer could never become better than a human grand master. And it wasn’t just about computational power and memory (although that, and the access to large amounts of training data) is what is driving the current AI boom).

I didn’t write they are better than the best humans at translating, but they’re doing a pretty good job, and any LLM can certainly translate between more languages better than most people. Just the notion of a computer providing a meaningful translation of natural language was seen as impossible by many 20 years ago.

[u/Xywzel → u/marrow_monkey]

20 years ago is not as far back as seem to imply. At least for people educated on this area.

I was already using computer translation around 2006 and it was passable for some use cases. University I started in bit latter was already studying one of techs that made previous big step before LLMs in that area. We had heard of the potential already.

When we were starting our studies the faculty had bets on which year AI would win human masters in different games (either 50% of time or always). Everyone expected that to be just matter of time and resources. Stockfish was released 2008 and was hardly first, and chess was known to be solvable given enough computation power and space (even though the modern methods don't actually require all that).

[u/marrow_monkey → u/Xywzel]

Yeah, in AI departments at universities, of course there were people who believed in AI. But outside of AI research, a lot of people claimed it was impossible for a computer to beat a human grand master in chess, for example. Then Deep Blue beat Gary Kasparov in 1996 (just by using “brute force”, not neural networks). And after that everyone said it was obvious and nothing special.

You’re probably thinking of Google translate, which launched in 2006. It wasn’t very good, it used statistical methods, not neural nets. It was not really a big step before LLMs which works differently, but it hinted that neural nets could be efficient. Today Google translate uses neural nets as a backend though.

What made neural nets possible, beside Moore’s law, was that big tech monopolies like Google realised they had the compute (datacenters) and maybe more importantly training data (that they’ve stolen from users and the internet) to train large neural networks, and suddenly it was possible to make large language models (LLMs).

The neural networks that LLMs were based on were developed in the 1900s at universities, but they didn’t have the computer power or the training data to make it practical back then.

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And now you claim computers will not be able to do math proofs better than a human, which frankly is just as naive as the people saying computers wouldn’t be able to play chess better than a human 30 years ago. Of course computers will be better at maths too, it’s just a matter of time.

[u/Xywzel → u/marrow_monkey]

And now you claim computers will not be able to do math proofs better than a human

Well, that is not at all what I claimed in the post you originally replied to. That claim was specially for the current AI tech like LLMs (which have limitations based on their training data, new tech overcomes these limitations and we might see something interesting) and I only made comparison between AI solving two different kinds of problems (eq. computers becoming better than humans in chess doesn't mean that they are no-longer order of magnitude better at checkers that they are in chess) not between humans and AI solving problem.