analiza aiqbench
The article critically examines current limitations in LLMs regarding autonomous long-form non-fiction and scientific reasoning, arguing that mastering established science is a prerequisite for solving open-ended problems. It suggests that despite rapid progress in other areas, models currently struggle to organize complex scientific concepts compellingly, which may slow revolutionary insights in AI-driven research.
I wrote an AI textbook — how long until AI can do it better?

Reflections on AI's writing ability and how AI models get more capable.
There are a lot of criticisms of AI writing, but most of them are focused on more creative, high-voice writing like this blog. Those — including my own piece — often argue that it is because good writing is high-voice, has a point of view, has a deep human expression that needs to come across, and or a process of thinking that you peek into with the chosen words. As LLMs get more refined as tools, rather than conversational assistants, I think we are actually going backwards on our goals of having models produce inspiring writing. On the other side of things is non-fiction writing. Filler, copy text was one of the genuinely useful abilities of an LLM (Sam Altman said so much about the early business of GPT-3 on a recent podcast). It has seemed like any flaws here were mostly down to a general lack of intelligence in the models, or some other training issue, and all non-fiction and explanatory text would get obliterated by the rapid pace of progress eventually. Having worked with the models as a writing assistant over the last few years, they’ve gotten a bit better, but it’s worth reflecting on what’s holding them…
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