3 ms·
he did well, he managed to convince the entire world that LLMs are "intelligent" for over 3 years but no con can endure forever
by blibble 1y ago
he did well, he managed to convince the entire world that LLMs are "intelligent" for over 3 years
but no con can endure forever
- dismalaf 1y agoI think he truly believed that scaling LLMs would lead to AGI, so he over-promised assuming the technology would catch up to the claims. The wall is very obvious now though.
- dinkblam 1y ago> I think he truly believed that scaling LLMs would lead to AGI. no one in the industry could have believed that
- stephc_int13 1y agoIt is easy to say retrospectively. I am not in the industry but I've been following closely and I am usually skeptical, but while I erred on the side of "this is just a tool" I also wondered "what if?" more than once.
- Herring 1y agoI'm in the industry. It's still on track. https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/ https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
- nwienert 1y agoThose tasks they are measuring are extremely well-defined problems that are extremely well-known. They don't really represent general programming as it is practiced day to day. "Find a fact on the web", "Train a classifier" these are trivial things given the answers are all over the place on Github, etc. So they're getting exponentially better are doing some easy fraction of programming work. But this would be like self-driving cars getting exponentially better at driving on very safe, easy roads, with absolutely no measurement towards something like chaotic streets, or rural back-roads, or edge cases like a semi swerving or weird reflections.
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