All of the latest big proofs were driven by professional human mathematicians steering and priming the models, yes.

All of the best AI-made software projects are also driven by experienced human software developers steering and priming the models. Does that mean the projects "aren't made by AI"?

No, it just means AI is not quite good enough yet to fully replace humans, and, so, unsurprisingly, the best results will be obtained from people who are already great at a field and who take the time to squeeze as much force multiplication out of LLMs as possible. The AI is still doing well over 95% of the significant work.

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TomGarden 18 hours ago | parent | next [-]

While I agree with you, I think we also have to concede that this is not how these accomplishments have been presented. I'd argue most people I've seen talk about this online are unaware of the mathematicians steering the models.

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basch 18 hours ago | parent | prev | next [-]

“Good enough to replace humans” isn’t necessarily the benchmark.

The question is is a computer with a human stronger than a computer without a human. At what point does the hybrid go from being stronger, to the human getting in the way, or steering the computer in more wrong directions that right ones, or the human not being able to keep up. Does the human add enough extra randomness to be of value for a while, even as a minor co-processor.

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meowface 24 minutes ago | parent | next [-]

I have little doubt that for many years, AI + human will be better, and then eventually AI will be so good that humans mostly won't offer anything. That latter state will probably take at least 10 more years, but will likely happen within our lifetime.

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TheOtherHobbes 17 hours ago | parent | prev [-]

Underappreciated point. The point of inflection is where humans switch from being a driver to a liability.

But I don't think it's randomness, because that would be easy to add. It's more like a different perspective on the training data, a different set of perception categories, and a different set of skills used to work with all of the above.

Those skills aren't very efficient, but they're the best we can do. We're used to their strengths but we don't like to think about their limitations.

It's completely plausible that AI will replace some of them, and not implausible it could replace and improve on all of them.

	▲	basch 17 hours ago \| parent [-]
		It's also not necessarily implausible that AI/we decide that performance is better with humans in the loop somewhere, even if its reduced to something like mechanical turk.

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agileAlligator 18 hours ago | parent | prev | next [-]

https://chatgpt.com/share/6a5fdc7a-d6f8-83e8-bbea-8deb42cfed...

Terrence Tao's conversation with ChatGPT is very illuminating.

HN Discussion: https://news.ycombinator.com/item?id=49010345

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chrisjj 11 hours ago | parent | prev | next [-]

> All of the best AI-made software projects are also driven by experienced human software developers steering and priming the models. Does that mean the projects "aren't made by AI"? No

Software devs steer and prime compilers too. Those tools don't "make the project" and nor does your so-called AI.

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simianwords 17 hours ago | parent | prev [-]

> All of the latest big proofs were driven by professional human mathematicians steering and priming the models, yes.

False, navier stokes was solved in one shot without steering

	▲	rsfern 16 hours ago \| parent [-]
		According to OpenAI, but they haven’t exactly been transparent about what information the prompt entailed. The bigger question is to what extent did expert mathematicians metaprompt the model with fruitful solution strategies through their sessions finding their way into training data. Answering that question definitively is kind of important for understanding the models contribution/capability. But I feel like people want to turn this into a debate about priority and credit which is sort of secondary