I think the parent comment meant professional, high-level chess. The kind people get played to play, not just do for a hobby. That's absolutely on life support.
I'm not sure what the equivalent would look like in the math field, but it probably involves a lot of mathematicians losing their jobs and the quality of human-produced math decreasing overall.
The quality of the math in general would be fine, since in this scenario cpus will keep producing it. The quality of cpu-cpu chess games is quite high, beyond human understanding in many cases.
Chess is a weird example because it doesn't really have any utility beyond itself. Even pure math sometimes ends up having use in the strangest places. Although if no one understands the frontier math (because no one is getting paid to), I'm not sure it even matters what the quality of the cpu math is?
It's a bit like a tree falling in a forest. If an LLM proves a theorem but no one understands it, did it make a sound?
"Although if no one understands the frontier math (because no one is getting paid to), I'm not sure it even matters what the quality of the cpu math is?"
Presumably AI will be connect the dots to the applications. As the declaration says, this isn't just about math. Human understanding is losing economic value. You can understand stuff on your own time, I guess.
The standard justification for pure math to holders of purse-strings is something like "it might lead to a useful application down the road, like crypto, who knows". That looks pretty inefficient now. We have to entertain the possibility that AI can develop the math needed for any application we put to it. Eg if number theory didn't exist, we could have asked AI for a way to transit messages securely and it would maybe come up with fermats little theorem as part of its solution or maybe come up with an approach we can't conceive of right now seeing as most of us are constrained to available number theory. Like how in the last year when I give an LLM a programming project I see it doesnt even bother with of the many software libraries I and others have written and just codes up the calls it needs on the fly or finds some other ad hoc solution.
I would argue people getting paid to play chess was a short lived phenomenon anyway if you put it in context. The transition there is less related to the introduction of chess engines and more related to the shift in the media landscape.
> It's a bit like a tree falling in a forest. If an LLM proves a theorem but no one understands it, did it make a sound?
But in future most proofs will be for consumption by other AI models in the pursuit of yet other proofs.
It's kind of surprising so many mathematicians act surprised by this given this was clearly where automated proof assistants would lead. I guess they assumed they'd always be the ones guiding them.
It's going to be hard to compete with something that has access to all of math at once and can find connections between elements that appear unrelated to humans.
And at some point AI will start suggesting - or doing - physical experiments.
Presumably some of the proofs will have applications beneficial to humans beyond impressing other mathematicians, and AI will surface them, or use them directly.
Well if it proves useless presumably they'd stop doing it.
But if AI is to recursively self improve understanding and evolving its own foundations, which are clearly mathematical, is essential. There is no need for humans to grasp what is going on in that loop.
I mean, was the point of math ever just because some humans enjoyed doing it? Even though a lot of it is theoretical, there's been all sorts of useful things that have come out of it as well due to an improved understanding of the universe through new ways of thinking about it. If it got to the point where no human could understand it and there were no ways to actually use it, I don't think anyone would bother having their computers doing it at all.
When there's a billion people playing something, money will never be an issue for those at the top. Even things like chess.com was able to sponsor a tournament with a million dollar prize pool.
Also I'd argue that chess's utility is ultimately the same as pure math, particularly in esoteric fields. These things are highly unlikely to ever lead to any sort of real world breakthrough or application. The main benefit is an outlet for human logic, creativity, and exploration - which significant self improvement possible along the journey for players.
Though I think even that's probably too socially utilitarian. I think ultimately the 'real' drive is the same in both fields - it's fun and personally rewarding.
> Though I think even that's probably too socially utilitarian. I think ultimately the 'real' drive is the same in both fields - it's fun and personally rewarding.
That's fine, and no amount of machine excellence will keep you from enjoying recreational chess or recreational math.
Ah but it's enjoyable for billionaire and pauper alike. And the former tends to enjoy sponsoring it for the sake of seeing and promoting the bounds of human capability.
Mathematics is more than establishing arbitrary facts (although some look like curiosities), it's also defining what interesting research directions are and establishing common language/notation. I think that will stay relevant?
In theory you can automate finding interesting research directions by identifying conjectures with many dependencies. And notation has never been mathematicians' forte, with them trying to cram the entirety of universe into single letters.
Why? How do you define interesting research directions? That used to be defined by testing the limits of human understanding i.e. some people can't figure something out. AI might have very different ideas about what is interesting and I am not sure what humans would get out of putting years into understanding AI proofs for what? What are we doing at that point? Like if you spend years understanding some AI proof of theorem 123456, why is that meaningful? I am actually asking why you think defining interesting research directions will stay relevant. In my opinion, people spend years acquiring knowledge so they can work on problems which is separate.
There's two points about this I am assuming 1) Mathematics actually has a significant subjectivity to it and is community oriented and not just climbing a never ending list of theorems that exists in the universe 2) A lot of mathematical research work is inside of a subfield and isn't directly motivated by applications. Sometimes it is but e.g. people don't work on obscure theorems about elliptic curves because of a dire need for that but more because the community found it interesting.
People become interested in things when they become invested in it personally, because they've contributed to it. So I don't think it will stay relevant...
Does your "one" only contain humans or does it also contain other AI systems. AI math is not a single monolithic thing, but a distributed one. I see value in sharing proofs even among just AI.
I don't really care what Zitron says or pay attention to him because it seems clear to me he has some kind of agenda.
But this article is not nearly as impartial as it claims to be. It interprets all Zitron's claims in a narrow and overly-literal way. Missing the point and refuting a technicality.
For example, looking at the last 3 year's revenue/profit growth. This tells us nothing without looking at a larger context. Has revenue growth slowed down? Have profit margins compressed? What makes up the income and has that changed? Etc.
I think anyone being intellectually honest understands that these tables alone don't refute Zitron's claims that big tech are "dying and thrashing around" which is something that could take many years and easily hide under these kind of top line / bottom line numbers. (Maybe further analysis would refute the claim, but that analysis is not present here.)
Or Zitron's claims that AI capabilities are "reaching the upper limits" back in 2024. I don't think even Zitron would disagree that AI capabilities have grown since then, but that doesn't refute his point. How long the "reaching" takes and how wide the "upper limits" are is completely up to a subjective interpretation, which this post makes no attempt to even explore.
By all means dunk on futurists. But at least steelman their positions or you look just as biased as them. (Although maybe I am off base and this bias is meant to be clear from the start by admitting to pro-AI predictions all the way back in 2015.)
It seems to me that if they'd just about reached their upper limits in early 2024 agentic coding wouldn't be eating software development like it is now, and wasn't then.
LLMs are broader than just coding. The article states this particular Zitron claim was wrong, even at the time, because you can put an LLM on a loop until it generates code that compiles. Mitigating hallucinations (in the subset of applications with verifiable output) is not really the same as solving that category of problems outright. I could quibble about a number of things in this example alone, but that's not really my point.
I don't care if the claims are correct or incorrect. I just think the tone of the article is dishonest about its own impartiality and fairness, because it doesn't even attempt to interpret the claims in any way except the least favorable. If you want to be persuasive, you should refute a claim using the most favorable interpretation of that claim possible -- this does the opposite.
Yeah, I think this is fair criticism, and I definitely felt the pointedness in the tone as well.
On the overly-literal / narrow thing, I think thats the culture around evaluating predictions overall. Like, all those posts around christmas where people make predictions and evaluate how last year went. The rigor is the norm.
I have a whole theory on this I call design innovation through technical innovation.
If you want to do something no one has done artistically, it helps if you're also doing something no one has done technically.
And novel designs stand out. It's no coincidence how many of those wildly successful games introduce totally new game designs, only made possible through their totally new game engines.
I really appreciate this comment, so I'll try to return the favor (in small part) by sharing one pretty special to me: WYCE, https://grcmc.org/wyce. Hope you enjoy.
What makes another couple big steps like that inevitable in a short time frame?
Before the recent floodgates cracked open, AI research made only slow incremental progress for decades. Why couldn't we already be back near that rate of progress?
Funny, I put parachutes on my airplanes in kerbal space program (as a safety feature) but never considered what the real-life analogue to that would be. Turns out it's very similar!
False. Analogue does not mean having the same function.
Analogue, in this sense, means some action or thing seen as comparable to another.
If something is analogous of, or to something, it is proportional to something else.
The proportionality describes a primary/secondary relationship. If two actions/things are entirely comparable - i.e. orthagonality approximating 0.0 - then they are not analogues, but rather equivalents.
Something may be a poor comparison to another thing, but still be an analogue - albeit, a poor analogue in comparison to some other, original, instance.
I'm not sure what the equivalent would look like in the math field, but it probably involves a lot of mathematicians losing their jobs and the quality of human-produced math decreasing overall.
The quality of the math in general would be fine, since in this scenario cpus will keep producing it. The quality of cpu-cpu chess games is quite high, beyond human understanding in many cases.
Chess is a weird example because it doesn't really have any utility beyond itself. Even pure math sometimes ends up having use in the strangest places. Although if no one understands the frontier math (because no one is getting paid to), I'm not sure it even matters what the quality of the cpu math is?
It's a bit like a tree falling in a forest. If an LLM proves a theorem but no one understands it, did it make a sound?
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