It's ironic that ML researchers don't fully understand why their models behave the way they do, yet continue to make progress using benchmarks to guide the efforts. Human understanding is important but whether and to what extent it's necessary seems to be a separate question, and the answer may depend on the nature of the field.

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caaqil an hour ago | parent [-]

> Human understanding is important but whether and to what extent it's necessary seems to be a separate question

We are about to find out the answer soon enough, probably from the in vitro results of the mathematicians currently on the chopping block.

I submit that human understanding is overrated, and many attempts to elevate it in the wake of AI is mediated by protectionism masquerading as virtue and "deep".

	▲	esalman 35 minutes ago \| parent [-]
		What matters more is that what will happen to us when we find the answer. That's what Tao et al. are more worried about. Traditional way of doing science means we (governments, NIH, NSF, and even private corporations) fund activities like asking seemingly unimportant questions, spending years running experiments on such hypothesis, publishing, reviewing, talking about results, reproducing results and such. We all agree that these are beneficial to us as a whole (Hacker news crowd might disagree). When we understand a process, we can apply it to a different problem and produce something useful. Euler developed a process to answer a whimsical question about walking in a town crossing 7 bridges only once. Now graph theory is applied everywhere. We obviously have failed to stop OpenAI from dumping "solutions" to hundreds of problems. So going forward, instead of testing hypothesis and talking about results, mathematicians will be forced to read through AI slop and detect what's useful and what's wrong. Maybe it will improve our understanding, but someone has to fund that activity. Will NSF, NIH, or OpenAI for that matter, do that?