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Comment by esafak | Hacker News Reader

Countless people are increasing their productivity and talking about it here ad nauseam. Even researchers are leaning on language models; e.g., https://mathstodon.xyz/@tao/114139125505827565

We haven't successfully resolved famous unsolved research problems through language models yet but one can imagine that they will solve increasingly challenging problems over time. And if it happens in the hands of a researcher rather than model's lab, one can also imagine that the researcher will take credit, so you will still have the same question.

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The actual posts totally undermine your point:

  My general sense is that for research-level mathematical tasks at least, current models fluctuate between "genuinely useful with only broad guidance from user" and "only useful after substantial detailed user guidance", with the most powerful models having a greater proportion of answers in the former category.  They seem to work particularly well for questions that are so standard that their answers can basically be found in existing sources such as Wikipedia or StackOverflow; but as one moves into increasingly obscure types of questions, the success rate tapers off (though in a somewhat gradual fashion), and the more user guidance (or higher compute resources) one needs to get the LLM output to a usable form. (2/2)

There is a LOT of money on this message board trying to convince us of the utility of these machines and yes, people talk about it ad nauseum, in vague terms that are unlike anything I see in the real world, with few examples.

Show me the code. Show me your finished product.

Increasing productivity is nice and commendable, but it is NOT an LLM making a breakthrough on its own, which is the topic of the Gwern's article.

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