I have always been saying: don't be optimistic about AI. Software people prepared their end with this optimism without knowing how the economy works. "AI will make our jobs easier, I will have more time with my family," blah blah. Now we will see more news like that in the coming years. I am okay with supporting AI for curing cancer, but I am against supporting AI for disrupting the white-collar economy without a proper government plan.
It's hard to cut through the AI hype when there are billions of dollars at stake. I usually trust negative comments more, as long as the person isn't trying to sell a course. Even though Terence Tao is a respected scientist, I wonder if his recent comments are driven by a need for funding due to federal cuts. I’ve had similar experiences with LLMs—whenever I ask them about hard math or RL theory, they almost always give me the wrong answers.
This is response from mathematician:
"This is quite something, congratulations to Boris and Aristotle!
On one hand, as the nice sketch provided below by tsaf confirms, the final proof is quite simple and elementary - indeed, if one was given this problem in a maths competition (so therefore expected a short simple solution existed) I'd guess that something like the below would be produced. On the other hand, if something like this worked, then surely the combined talents of Burr, Erdős, Graham, and Li would have spotted it.
Normally, this would make me suspicious of this short proof, in that there is overlooked subtlety. But (a) I can't see any and (b) the proof has been formalised in Lean, so clearly it just works!
Perhaps this shows what the real issue in the [BEGL96] conjecture is - namely the removal of 1 and the addition of the necessary gcd condition. (And perhaps at least some subset of the authors were aware of this argument for the easier version allowing 1, but this was overlooked later by Erdős in [Er97] and [Er97e], although if they were aware then one would hope they'd have included this in the paper as a remark.)
At the moment I'm minded to keep this as open, and add the gcd condition in the main statement, and note in the remarks that the easier (?) version allowing 1 and omitting the gcd condition, which was also asked independently by Erdős, has been solved."
The commentator is saying: "I can't believe this famous problem was solved so easily. I would have thought it was a fake proof, but the computer verified it. It turns out the solution works because it addresses a slightly different set of constraints (regarding the number 1) than what Erdős originally struggled with. (Generated by Gemini)
I started fully coding with Claude Code. It's not just vibe coding, but rather AI-assisted coding. I've noticed there's a considerable decrease in my understanding of the whole codebase, even though I'm the only one who has been coding this codebase for 2 years. I'm struggling to answer my colleagues' questions.
I am not defending we should drop AI, but we should really measure its effects and take actions accordingly. It's more than just getting more productivity.
test time scaling is based on methods from pre-2020. If you look details of modern LLMs its pretty small prob to encounter method from 2020+(ROPE,GRPO). I am not saying IMO is not impressive, but it is not breakthrough, if they said they used different paradigm then test-time scaling I would say breakthrough.
> We are not close to solving IMO with publicly known methods.
The point here is not method rather computation power. You can solve any verifiable task with high computation, absolutely there must be tweaks in methods but I don't think it is something very big and different. Just OAI asserted they solved with breakthrough.
Wait for self-adapting LLMs. We will see at most in 2 years, now all big tech are focusing on that I think.
IMO is not breakthrough, if you craft proper prompts you can excel imo with 2.5 Pro. Paper : https://arxiv.org/abs/2507.15855. Google just put whole computational power with very high quality data. It was test-time scaling. Why it didn't solve problem 6 as well?
Yes, it was breakthrough but saturated quickly. Wait for next breakthrough. If they can build adapting weights in llm we can talk different things but test time scaling coming to end with increasing hallucination rate. No sign for AGI.
This is bad use of AI, we spend our compute to make science faster. I am pretty confident computational cost of this will be maybe 100x of chatgpt query. I don't want to think even environmental effects.
its not like sonnet, yes current ai tools are increasing productivity and provides many ways to have chance to be rich, but agi is completely different. You need to handle evil competition between you and big fishes, probably big fishes will have more ai resources than you. What is the survival ratio in such a environment ? Very low.
It is not exactly AGI but huge step toward it. I would expect this step in 2028-2030. I cant really understand why people are happy with it, this technology is so dangerous that can disrupt whole society. It's neither like smartphone nor internet. What will happen to 3rd world countries. Lots of unsolved questions and world is not prepared for such a change. Lots of people will lose their jobs I am not even mentioning their debts. No one will have chance to be rich anymore, If you are in first world country you will probably get UBI, if not you wont.