151–160 of 292 posts
Tao's Rule of Thumb (which applies very well to software): > My own suggested rule of thumb: if the authors cannot convincingly demonstrate that they are able to give a clear, expert-level talk on their results, one that is correct and properly attributed, then the result should not be published. A proof that no human can properly explain should be viewed as incomplete, even if it has been formally verified.
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I don't think this is a valid counterpoint at all. Math is cooperative, and comprehension is the point : the proof has value exactly because (and only to that extent) it empowers humans to understand an abstract truth. Chess is competitive: the memorized line has value because it makes you incrementally more likely to defeat your opponent.
Yes, this. Comprehension is the point. We could map this to something like physics. If a man on a horse can shoot another man with a bow, empirically he makes correct predictions on gravity, wind and relative motion. But he can’t explain it. It’s not any different if your model has some “embodied” or demonstrable understanding; the model is not part of the discourse.
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I don't think this is a valid counterpoint at all. Math is cooperative, and comprehension is the point : the proof has value exactly because (and only to that extent) it empowers humans to understand an abstract truth. Chess is competitive: the memorized line has value because it makes you incrementally more likely to defeat your opponent.
Math isn't "cooperative". Math is about truths. The length of circumference. The area of a triangle. The formulas for these are true in an objective sense irrespective of whether you understand them. That said, without understanding, Math can't evolve. Comprehension of a proof is very important, but not what Math is fundamentally about. Computer programs are Math. You can use them without understanding how they work.
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I don't think this is a valid counterpoint at all. Math is cooperative, and comprehension is the point : the proof has value exactly because (and only to that extent) it empowers humans to understand an abstract truth. Chess is competitive: the memorized line has value because it makes you incrementally more likely to defeat your opponent.
Math is also useful. If someone showed that p = np tomorrow in a formally verified proof I don't care if no one can understand it.
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We have thousands of years of precedent that suggests that breakthroughs in mathematics tend to accumulate into broader technology breakthroughs in other domains. That's a misconception. Only a tiny percentage of mathematics has seen any applications whatsoever. There are vast libraries full of mathematics no one (in this discussion, anyway) has ever heard of that no one reads anymore and has never been applied to an…
> There are vast libraries full of mathematics no one (in this discussion, anyway) has ever heard of that no one reads anymore and has never been applied to anything. And that's an issue why? It would seem to me that producing that also produced the mathematics that revolutionized the world repeatedly for centuries. I would go further and claim that, if you want the mathematics that revolutionizes the world, there's…
The issue is SNR: signal to noise ratio. Generating exponentially more mathematics, particularly if the process is indiscriminate or optimized for something other than usefulness or mathematical relevance (such as optimizing for machine-provability), does not imply that we get exponentially more applications. We may end up halting the progress of applications altogether as the entire capacity of the world's mathematical apparatus is consumed by the interpretation and investigation of machine-generated proofs.
You can already visit arXiv and find vast numbers of not-yet-published mathematical papers. Most should never be published. None of this junk is benefitting humanity in the slightest.
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First of all, mathematics is about so much more than the area of a triangle etc. that any analogy based on such simple things is overwhelmingly likely to be too simple to be of value. Secondly, there is no truly objective truth to the area of a triangle. At bottom, this “truth” is simply “everyone is convinced, and for good reason”. Without persuading other people of the “truths” that you discover, there is no real m…
> Without persuading other people of the “truths” that you discover, there is no real mathematics. Why? If I sat around and studied math by myself and discovered something true yet not yet known but didn't share it, it's still true. Are you saying I didn't "do math" because I didn't share the result? Math exists on another plane and it has 'truths' that we haven't discovered, yet are still 'true', no?
I don't know why anyone should care about understanding the results if the AI is better at math than us. It'd be like demanding that human mathematicians are banned from publishing until their cats understand the theorems. If Amazon uses AI math to come up with better routing, the cats can benefit from cheaper delivery fees just as much as humans can. No understanding needed. The human brain is being obsoleted, soon…
I’m not anti AI but thinking the human brain is obsolete and using it will become a hobby is a dystopian view of the future where no one has any agency anymore. By your logic since our brains provide no value why not just shoot ourselves in the head while we’re at?
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> There are vast libraries full of mathematics no one (in this discussion, anyway) has ever heard of that no one reads anymore and has never been applied to anything. And that's an issue why? It would seem to me that producing that also produced the mathematics that revolutionized the world repeatedly for centuries. I would go further and claim that, if you want the mathematics that revolutionizes the world, there's…
And that's an issue why? The issue is SNR: signal to noise ratio. Generating exponentially more mathematics, particularly if the process is indiscriminate or optimized for something other than usefulness or mathematical relevance (such as optimizing for machine-provability), does not imply that we get exponentially more applications. We may end up halting the progress of applications altogether as the entire capacity…
Moreover, the disdain you have for low-value output in mathematics is not unique to you. Talented mathematicians don't like it either. Your mistake is assuming that AI will cause math to be dominated by low-value outputs. In fact, the opposite is likely the case: the marginal value of proofs will fall so low that the bar for meaningful research will become dramatically higher, not lower. I expect the goals of research mathematics to become extremely ambitious relative to the past, organized around substantial and enormous goals, not mass-generated slop as you're imagining.
Of course, yes, there will still be lots of slop, just like GitHub is full of AI coding slop, LinkedIn is full of slop, etc. But that's a generalized issue of the AI era, not unique to math.
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Then it has always been the age of "AI"...So the article title is inaccurate!
We can meaningfully talk about (1) the existence of a field and (2) the said field hitting its stride. The title of Tao's article denotes the latter. And oh, what a stride it is: https://vibemathed.com/stats
Tao's Rule of Thumb (which applies very well to software): > My own suggested rule of thumb: if the authors cannot convincingly demonstrate that they are able to give a clear, expert-level talk on their results, one that is correct and properly attributed, then the result should not be published. A proof that no human can properly explain should be viewed as incomplete, even if it has been formally verified.
He's a typical person otherwise, politically aware of how he barters for food; until proven otherwise this can be seen as little more than social moat defense.
To paraphrase a quote attributed to Upton Sinclair; hard to get a worker to understand something when their paycheck relies on them not understanding it.
The only interesting thing here is the frogs high up admitting they feel the heat.