I think Ted Chiang's story "The Evolution of Human Science" may be a significantly better metaphor here. What AI is dropping is (allegedly) just answers, not mysteries. In both cases, we don't really understand it, but in "Picnic", most of the things left behind aren't even useful, merely mysterious or more often dangerous. In "Evolution", answers (and only answers) and technologies arrive pre-packaged, with zero understanding.

- https://en.wikipedia.org/wiki/The_Evolution_of_Human_Science

- https://gwern.net/doc/fiction/science-fiction/2000-chiang.pd...

I like this metaphor better: the mathematics community is like a group of ancient astronomers that are unexpectedly getting the output from a modern telescope. "Why even look at the sky anymore," some say. "And we don't even own the telescope!"

Meanwhile, the Rubin observatory is creating an archive that grows by 20 terabytes each night, and nobody really worries about this. Dealing with that amount of data is definitely a problem, but also a nice problem to have, provided that there's a scientific community that can deal with it. Mathematics has unexpectedly become Big Science and there needs to be a better relationship between the funders of the scientific instruments and the scientists themselves.

It's a bit confusing that the output from the scientific instrument looks like badly-written academic papers. Maybe it should be treated more like raw data? The format is not ideal, but historians (for example) often have to deal with far worse.

Usefulness relies on understanding, even if that means understanding what is possible to ask for. What is being generated right now is really just a translation from one inscrutable phenomenon into another. In neither case do we have power over the data, because we don’t know what it means, truly where it comes from, and even the beginning of the limits of what the discovery allows. We’ve traded one black box for another, and still need to unpack it.

> All this new knowledge is arriving at a pace much greater than the field’s ability to process it

That’s the whole issue: it’s not knowledge, it’s data. AI can not provide an account for the proofs, or be questioned about them, or organize a talk exposing the idea because there’s no understanding behind any of it.

This is what Tao has been saying; that the results, in the strictest sense, were never the goal. It is ultimately the understanding (that previously was required to form a proof) that is valuable.

I like that metaphor. But what it calls to my mind is an "alien artifact" which was found by a road, and "how did these refined metals get placed inside of a rock, in such an unnatural formation?" and there was much debate about this artifact. Years later it was determined that it was in fact a spark plug which had been remineralized by the effluent from a mine upstream: an unusual process acting on a discarded object, and people who want to believe. Nobody thought that the remineralization process could take less than hundreds of years, which ruled out "spark plug".

Unrelated to the article: Roadside Picnic inspired a movie called Stalker, which in turn inspired the S.T.A.L.K.E.R. game series.

I highly recommend also reading the “Inhabited Island” as well as “Hard to be a God” by these authors. These books are brilliant as well.

It also reminds me, in a very similar way, of William Gibson's "Hinterlands". Some people are just hell bent on flying into the Alien Coordinates That Make You Crazy or Kill You but Sometimes Give You Cool Stuff you Don't Understand, from William Gibson's short story Don't Fly Into the...

I can only recommend Strougastky brothers books’, I enjoyed ‘Hard to Be a God’ and wish to read more from them.

That is why I find the prospect of spoiling the plot without warning to be bad taste and didn’t finish the article.

A new analogy for the HN audience that is susceptible to reinforcement learning applied to humans (daily)!

This rests on the (completely false) premise that AI is doing anything other than simply parroting our human knowledge back at us

It has no capacity to think, argue, demonstrate, or certainly do anything even remotely close to “intelligence”

I really dislike this analogy. Trash and apple cores versus potential, objective discoveries?

I'm not saying that OpenAI's results are good or bad. But I can say that at least humans could try to understand them and get not only the discoveries themselves but some techniques that could help other progress down the road.

It's fair to ask whether we'll be better off as a result of AI Math. But many people are failing to see any sort of positive outcome and I think this is close minded and borderline irrational.

I don't blame Mathematicians for trying to protect their careers. Anyone would to the same in their place. But the rest of society should take a hard look at what's happening and, as the financiers of mathematical research, make an independent assessment of whether humanity needs "old school" mathematical research at all versus a model with a lot more AI.

> It is ultimately the understanding

But understanding to what end? I'm if it's for the pure aesthetic experience of engaging in the beauty of math than that's fine, but that's typically not the view from my experience.

The goal of understanding is to advance the field, since historically human understanding was the only way to do this.

For the vast majority of human history, the world we experienced was a strange and unintelligible space. Humans survived at the by the grace of forces beyond their control. For a brief window of time human existed in a world where they increasingly saw a relationship between their understanding of the world and there ability to steer their environment towards a desired outcome.

I believe now we're seeing that that was a historical anomaly. The world is once again entering a state where it becomes increasingly unintelligible to us. Nick Land already famously pointed out, many decades ago, that the global financial system is already a form of "artificial intelligence" we don't really control. We're seeing that spread wider and wider.

The world making sense peaked around 1960s and has continued to breakdown ever since.

Why wouldn’t the AI be able to answer questions about the proofs or organize a talk? Those are pretty much the things it excels at, exploring and summarizing previous knowledge. The problem is the volume of questions and data a human needs to step through to arrive at the same understanding.

I guess the issue with that is that the understanding is important to mathematicians. The majority of people outside mathematics don’t understand it whether it’s humans doing the work or AI. If AI can generate useful results more quickly, then to the general public it is more valuable.

Does that mean AI is not doing something useful? If it provides a proof, it gives direction to human mathematicians who can then develop an understanding, give talks, be questioned about the topic, and teach. Is that not a useful service?

Fair, “answers” is probably more precise than “knowledge.” That gap between answers and understanding is the whole point of the post, so I think we mostly agree.