"We are also asking government to commit to reviewing service costs after a year, to help plug any costs gaps."
Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.
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but every now and then there’s something like this, where I feel like Go wants me to die an early death from high blood pressure.
换言之，在他看来，AI 不是能源杀手，而是未来文明的必需品，就像电灯发明时也有人担心蜡烛业失业一样。这个观点不是 Altman 首创。早在 AI 热潮前，就有专家比过生物大脑和硅芯片的效率。但 Altman 作为 OpenAI 老大，说出来影响力大，瞬间成了 X 上的热点，视频有两千多万次浏览，引爆了讨论。