Want to wade into the snowy surf of the abyss? Have a sneer percolating in your system but not enough time/energy to make a whole post about it? Go forth and be mid.
Welcome to the Stubsack, your first port of call for learning fresh Awful you’ll near-instantly regret.
Any awful.systems sub may be subsneered in this subthread, techtakes or no.
If your sneer seems higher quality than you thought, feel free to cut’n’paste it into its own post — there’s no quota for posting and the bar really isn’t that high.
The post Xitter web has spawned so many “esoteric” right wing freaks, but there’s no appropriate sneer-space for them. I’m talking redscare-ish, reality challenged “culture critics” who write about everything but understand nothing. I’m talking about reply-guys who make the same 6 tweets about the same 3 subjects. They’re inescapable at this point, yet I don’t see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldn’t be surgeons because they didn’t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I can’t escape them, I would love to sneer at them.
(Credit and/or blame to David Gerard for starting this.)
Recently discovered Donald Knuth got oneshot by Claude recently (indirectly, through fedi) - feeling the itch to write about tech's vulnerability to LLMs because of it.
Even in Knuth's account it sounds like the LLM contribution was less in solving the problem and more in throwing out random BS that looked vaguely like different techniques were being applied until it spat out something that Knuth and his collaborator were able to recognize as a promising avenue for actual work.
I am not a mathematician or computer scientist and so will not claim to know exactly what this is describing and how it compares to the normal process for investigating this kind of problem. However, the fact that it produced 4 approaches over 31 attempts seems more consistent with randomly throwing out something that looks like a solution rather than actually thinking through the process of each one. In a creative exploration like this where you expect most approaches to be dead ends rather than produce a working structure maybe the LLM is providing something valuable by generating vaguely work-shaped outputs that can inspire an actual mind to create the actual answer.
The idea that it's ultimately spitting out random answer-shaped nonsense also follows from the amount of babysitting that was required from Filip to keep it actually producing anything useful. I don't doubt that it's more efficient than I would be at producing random sequences of work-shaped slop and redirecting or retrying in response to a new "please actually do this" prompt, but of the two of us only one is demonstrating actual intelligence and moving towards being able to work independently. Compared to an undergrad or myself I don't doubt that Claude has a faster iteration time for each of those attempts, but that's not even in the same zip code as actually thinking through the problem, and if anything serves as a strong counterexample to the doomer critihype about the expanding capabilities of these systems. This kind of high-level academic work may be a case where this kind of random slop is actually useful, but that's an incredibly niche area and does not do nearly as much as Knuth seems to think it does in terms of justifying the incredible cost of these systems. If anything the narrative that "AI solved the problem" is giving Anthropic credit for the work that Knuth and Stapprrs were putting into actually sifting through the stream of slop identifying anything useful. Maybe babysitting the slop sluice is more satisfying or faster than going down every blind alley on your own, but you're still the one sitting in the river with a pan, and pretending the river is somehow pulling the gold out of itself is just damn foolish.
I am a computer science PhD so I can give some opinion on exactly what is being solved.
First of all, the problem is very contrived. I cannot think of what the motivation or significance of this problem is, and Knuth literally says that it is a planned homework exercise. It's not a problem that many people have thought about before.
Second, I think this problem is easy (by research standards). The problem is of the form: "Within this object X of size m, find any example of Y." The problem is very limited (the only thing that varies is how large m is), and you only need to find one example of Y for each m, even if there are many such examples. In fact, Filip found that for small values of m, there were tons of examples for Y. In this scenario, my strategy would be "random bullshit go": there are likely so many ways to solve the problem that a good idea is literally just trying stuff and seeing what sticks. Knuth did say the problem was open for several weeks, but:
I guess "random bullshit go" is served well by a random bullshit machine, but you still need an expert who actually understands the problem to read the tea leaves and evaluate if you got something useful. Knuth's narrative is not very transparent about how much Filip handheld for the AI as well.
I think the main danger of this (putting aside the severe societal costs of AI) is not that doing this is faster or slower than just thinking through the problem yourself. It's that relying on AI atrophies your ability to think, and eventually even your ability to guard against the AI bullshitting you. The only way to retain a deep understanding is to constantly be in the weeds thinking things through. We've seen this story play out in software before.
Thank you for providing some actual domain experience to ground my idle ramblings.
I wonder if part of the reason why so many high profile intellectuals in some of these fields are so prone to getting sniped by the confabulatron is an unwillingness to acknowledge (either publicly or in their own heart) that "random bullshit go" is actually a very useful strategy. It reminds me of the way that writers will talk about the value of just getting words on the page because it's easier to replace them with better words than to create perfection ex nihilo, or the rubber duck method of troubleshooting where just stepping through the problem out loud forces you to organize your thoughts in a way that can make the solution more readily apparent. It seems like at least some kinds of research are also this kind of process of analysis and iteration as much as if not more than raw creation and insight.
I have never met Donald Knuth, and don't mean to impugn his character here, even as I'm basically asking if he's too conceited to properly understand what an LLM is, but I think of how people talk about science and scientists and the way it gets romanticized (see also Iris Merideth's excellent piece on "warrior culture" in software development) and it just doesn't fit a field that can see meaningful progress from throwing shit at the wall to see what sticks. A lot of the discourse around art and artists is more willing to acknowledge this element of the creative process, and that might explain their greater ability and willingness to see the bullshit faucet for what it is. Maybe because science and engineering have a stricter and more objective pass/fail criteria (you can argue about code quality just as much as the quality of a painting, but unlike a painting either the program runs or it doesn't. Visual art doesn't generally have to worry about a BSOD) there isn't the same openness to acknowledge that the affirmative results you get from an LLM are still just random bullshit. I can imagine the argument being: "The things we're doing are very prestigious and require great intelligence and other things that offer prestige and cultural capital. If 'random bullshit go' is often a key part of the process then maybe it doesn't need as much intelligence and doesn't deserve as much prestige. Therefore if this new tool can be at all useful in supplementing or replicating part of our process it must be using intelligence and maybe it deserves some of the same prestige that we have."
Baldur Bjarnason's essay remains evergreen.
Even when looking at Knuth's account of what happened, you can already tell that the AI is receiving far more credit than what it actually did. There is something about a nondeterministic slot machine that makes it feel far more miraculous when it succeeds, while reliable tools that always do their job are boring and stupid. The downsides of the slot machine never register in comparison to the rewards. Does it feel so miraculous when I get an idea after experimenting in Mathematica?
I feel like math research is particularly susceptible to this, because it is the default that almost all of one's attempts do not succeed. So what if most of the AI's attempts do not succeed? But if it is to be evaluated as a tool, we have to check if the benefits outweigh the costs. Did it give me more productive ideas, or did it actually waste more of my time leading me down blind alleys? More importantly, is the cognitive decline caused by relying on slot machines going to destroy my progress in the long term? I don't think anyone is going to do proper experiments for this in math research, but we have already seen this story play out in software. So many people were impressed by superficial performances, and now we are seeing the dumpster fire of bloat, bugs, and security holes. No, I don't think I want that.
And then there is the narrative of not evaluating AI as an objective tool based on what it can actually do, but instead as a tidal wave of Unending Progress that will one day sweep away those elitists with actual skills. Random lemmas today mean the Millennium Prize problems tomorrow! This is where the AI hype comes from, and why people avoid, say, comparing AI with Mathematica. To them I say good luck. We have dumped hundreds of billions of dollars into this, and there are only so many more hundreds of billions of dollars left. Were these small positive results (and significant negatives) worth hundreds of billions of dollars, or perhaps were there better things that these resources could have been used for?
ooh gooods nooo now all the Claude slurpers are going to refer to this forever as definitive proof of how legitimately useful LLMs have got, it "solved" a math problem for Donald Knuth! :<
A lobster invokes classic argument from authority
yeah b/c I'm a professional computer scientist ...
I was pissed when my (non-academic) friends saw this and immediately started talking about how mathematicians and computer scientists need to use AI from now on.
But... why?
Always remember that Nobel disease is a thing.
The one I often think about is the person who invented PCR and then later claimed to have had an encounter with a fluorescent talking raccoon of possibly extraterrestrial origin.
oh hey I remember reading that Donald Knuth paper earlier today, when it got posted by an AI youtube channel as 'proof' AI is on the path to AGI