It's really not that big. Yeah Navier-Stokes was easier than Riemann but that's not really the issue.
AI has and will improve at a much greater rate than human mathematicians. So it's really a question of if AI gets good enough to tackle it before any human does. It doesn't look like humans will be solving it anytime soon but where will AI be in 2 years ?
Hell, it looks like at least one other result will be announced soon too.
The thing about mathematics is that it can be arbitrarily hard, including impossible to prove a given theorem.
I don’t know the details of RH, it might very well be solved soon, but it could also be impossible or just so difficult that even orders of magnitude more intelligent AI can’t solve it even.
If it is impossible to prove, it might be possible to prove that it is impossible to prove, or that itself might be difficult or impossible…
Has and will. Are you going to back that assertion up at all, or just repeat it like that other viral thought-terminating cliche: ‘this is the worst the models will ever be’?
No it isn't. Best and worst and ill-defined anyway but the chess ELO score of various LLMs has fluctuated up and down, it's not been montonically increasing. What is the best answer to "how do I make cocaine"? The models are getting larger, with more compute and RAM backing them, but that doesn't automatically make them better if you don't define how you're measuring better-ness.
None of the frontier labs care about Chess as it's already a solved problem. If they did, the models would be much better. It's really not that hard. Google has a paper on grandmaster level chess without search from transformers.
Better obviously means better, like how they became better than they were 6 months and a year ago.
"Better" is not one dimensional across all use cases even if model capabilities are improving in aggregate.
e.g. If someone said "this is the worst they'll ever be" in response to some writing with obvious LLM cliches in 2024, I'm not convinced that prediction was actually correct.
The focus of OpenAI/Anthropic pivoted aggressively to the agentic performance arms race instead of making a more human sounding chatbot so regressions in writing ability aren't really a concern anymore if agentic benchmarks improve.
The first time I heard a recommendation to use Claude was specifically because it sounded much more "human" and natural than ChatGPT. Fast forward to now and idiosyncratic Claude-isms repeated every other sentence and its convoluted verbosity has become a widely mocked meme.
Chess is not solved in any meaningful sense of the term. Computers have been better than humans since the 90s, but better chess programs are released all the time.
I would describe better as how much of my work I can delegate to the agent. Right now I'm delegating much more to Astra high than 6 months ago to Opus 4.6. Every dev has this feeling, it's weird to even argue what a better model/harness means.
AI has and will improve at a much greater rate than human mathematicians. So it's really a question of if AI gets good enough to tackle it before any human does. It doesn't look like humans will be solving it anytime soon but where will AI be in 2 years ?
Hell, it looks like at least one other result will be announced soon too.