lagrangeinterpolator

Mathematicians are not entirely happy about OpenAI’s ten mathematical results. As it turns out, the proofs did not cite many of the existing techniques in the literature. The plagiarism bot is still a plagiarism bot. scientificamerican.com/…/openais-latest-math-brea… “They are running roughshod over the work of others who came before them in a deliberate way,” says Steven Miller, a mathematician at Yeshiva University, who argues that OpenAI has effectively plagiarized his own research. “It seems completely systematic to me, and it points to research misconduct.” The LLM-generated proof hinges on a particular mathematical argument that it presented as its own but that actually first appeared in a 2016 paper by Miller and a collaborator. The discovery stunned Francesco Fournier-Facio, a mathematician at the University of Cambridge, who studies group theory—at least until he “engaged with this breakthrough as I would if a human had written it,” he says. The result, he and some of his colleagues found, wasn’t as novel as it first appeared. Like a number of recent AI breakthroughs, it pasted together ideas from the mathematical literature to build a new theorem. Once again, the LLM’s trick is its superhuman patience for assembling puzzle pieces, not the ability to make some profound intellectual leap. In particular, Astra’s key mathematical step combined ideas first found in two papers from 2016 and 2019. Andreas Thom, a mathematician at the Dresden University of Technology, who co-authored the 2019 paper, summarized the result on MathOverflow.com, calling it “creative and at the same time elementary.”

He is spending the equivalent of $1 million a year on tokens just to develop this game with 6 concurrent players on a good day. Of course, he isn’t actually paying that much. Instead, he just has a dozen different $200/mo Max subscriptions to get the same amount of tokens for $33k a year. AI economics, baby!

Clammy Sammy acknowledges Ed Zitron for the first time! xcancel.com/sama/status/2084663673570971990 His response is the Live Laugh Love of 2015 Silicon Valley platitudes: i would rather be an optimist and work hard than a pessimist posting about why things won’t work. it’s much more difficult and the most likely path is failure, but society fails if people don’t try. no amount of “it will never work” essays will drive society forward. Of course, building data centers that use untold amounts of rare earth metals, electricity, and water while spewing out tons of pollution and scraping every last bit of human content in order to remove people from their jobs by doing them badly is what drives society forward. Profits fail if oligarchs don’t try.

The OpenAI hacking incident has put everyone on edge. Anthropic, not to be outdone, decides to publicly announce that they have committed not one, not two, but three crimes! Come on! I was told this wasn’t a marketing campaign!

Before the days of AI 2027, he became famous in 2024 for posting one of the original pieces of writing in the line-go-up genre: Situational Awareness. It seems that like any good grifter, he used this opportunity to make money (in this case by starting a hedge fund).

There is something about this picture, a warehouse full of books to be destroyed by Anthropic for training data, that just makes me sick. Knowing that they are destroying books in the abstract is bad enough, but seeing the sheer scale of it makes me viscerally angry. Karen Hao called her book Empire of AI for a very good reason. The empire has all these noble narratives of bringing civilization and progress to the colonies, but in reality the empire only sees resources to be extracted, repackaged, and sold back to their subjects at a fee. Kudos to those who saw this immediately and fought against it. Shame on those who believed in the narrative of progress, to the point where many of them are collaborators with the empire. Whenever a smarmy fellow on Linkedin posts about how AI is making so much progress in coding or math, I want them to see this picture. I want them to see the videos of families in tears after a data center takes away their home with eminent domain, and the other families who have to deal with the resulting pollution. Remember that it all came at these costs. I hope it was worth it.

It gets worse. I found another article about the same podcast: Sam Altman says AI won’t shorten the workweek because humans secretly enjoy being busy. “Technology, for a long time, has been promising people that they’re going to work less and they’re going to have all this leisure,” Altman said. “But somehow we never get the promise of the four-hour workweek at mass scale in society,” he added. “And I don’t expect AI to change that.” “It’s like a relative game. People are very focused on how they’re doing relative to other people,” the CEO said. “I think we’re all going to be much busier than we thought we were supposed to be in a post-superintelligence world. We’re still going to complain about it, but secretly we’re going to be happy,” Altman concluded.

The chuds are all at r/accelerate now. “We’re the unapologetically Pro-AI alternative to r/singularity, r/futurology, r/artificial & r/technology which are becoming filled with Luddites, Techno-Decelerationists, Techno-Reactionaries, & Anti-AIs.”

Clamuel did another podcast. I don’t want to waste my time watching it, but I want to sneer at some choice quotes from news articles breathlessly reporting this stuff as gospel 1, 2 “We are now, like, in the singularity” Is the singularity in the room with us right now? Even the people on X the Everything (Including CSAM) App aren’t so convinced. Altman said on the podcast that just a decade ago, the so-called singularity still felt like a distant and improbable dream — something he and his colleagues would discuss casually over lunch. “Now we’re actually in the moment that we used to talk about at the lunch table in a very not-serious way,” he said. “I’ve been waiting for this my whole life, and I think it’s going to be incredible, hugely positive, awesome for the world.” Do you feel it? Do you feel incredible? Do you feel hugely positive? Do you feel awesome, now that the singularity is here? Oh wait, he’s speaking in the future tense again, never mind. It really is a force of habit. It’s also funny how he talks about how positive things are gonna be immediately after the super scary hack done on Hugging Face (really due to incompetence on both sides). Oh no, the AI is gonna kill us all! It’s going to be incredible, hugely positive, awesome for the world. Later in the podcast, Altman criticizes AI leaders who have repeatedly warned that AI is dangerous. He didn’t name Anthropic, his primary competition, but its CEO, Dario Amodei, is well-known for making dire predictions about the future in his calls for greater attention to safety. “I also think some of the alternative visions painted by other companies are quite terrifying,” Altman said. “I’m going to make sure that gets pushed against and is not what happens.” At least Clammy realizes that all the doom trolling about how AI will replace and destroy all humans turns out, surprisingly, to lead to overwhelming negative public opinion. But this statement truly reassures me that Scammy has good intentions about AI safety. He will make sure this is pushed against and is not what happens. He wouldn’t want another super scary, dangerous hack that just so conveniently turns out to be excellent marketing. OpenAI CEO Sam Altman says one of his biggest concerns about AI isn’t whether it becomes too powerful in and of itself, but that a single company could control that power. Yeah, it would be really bad for Anthropic to control all the power of AI. It would be a disaster to hand everything over to Anthropic. “Every time that humanity has traded off its liberty for safety, it’s been a long-term net loss,” Altman said. “We are going to put this in the hands of people. We’re going to empower them. We are going to let society express its ideas and use this technology in the way they want.” Society is expressing its ideas about AI, all right. But I feel like they aren’t what he has in mind. Altman, meanwhile, said preventing such power from becoming overly concentrated has long been central to his vision for AI, even as his company continues to keep its own models closed. “For so long now, I have felt focused on this singular goal of abundant intelligence and a belief that incredible human prosperity will come from that, as long as we don’t have a weird power concentration and kind of a new authoritarianism,” he said. Well, we seem to have a weird power concentration and “kind of” a new authoritarianism, but I don’t see any abundant intelligence and incredible human prosperity. Lots more sneers on the Better Offline subreddit.

It is surprising how many exceptionally strong mathematicians have started working for OpenAI and Anthropic. These people would have easily become professors at top universities if they stayed in academia. I think many mathematicians, especially the competitive ones at the top, have a “progress at any cost” attitude (and I’m sure the paychecks helped). As for the results, you still need good mathematicians to sift through all the output to identify that the proofs are valid. I would honestly be positive about universities developing their own AI to help mathematicians get these kinds of results, but right now, AI is inseparable from these evil companies. Thankfully, I believe this is a likely outcome in the future because the AI companies will one day implode. From what I’ve seen, most prompts are in plain English. I suppose the part where the AI parses the statement correctly is much easier than the part where it boils a couple lakes in the process of bashing its head against the wall trying millions of different combinations of random shit from the literature to slap together a proof. For one of the big results (cycle double cover), the prompt specified that the AI could use 64 subagents and was required to not give up for at least 8 hours. The tokenmaxxers would be proud, we didn’t need that forest anyway. Thank god math doesn’t have a CTO to look at the expense reports.

Among the three big results I’ve looked at (unit distance problem, cycle double cover, Jacobian), two were counterexamples and one of them had a short 3 page proof using ideas from the 1970s. The Jacobian conjecture is an extreme case because a single counterexample is enough (for unit distance, you technically need a family of counterexamples), and it is easy to check with very basic computations. It is telling that all of these announcements came from OpenAI or Anthropic employees, who presumably have unlimited resources. Nobody really knows how many resources they spent on this, or what else they tried. Nobody really seems to care about this question, either. What I’m more worried about is many people starting to use AI to try and prove small lemmas for them in their projects. Of course, a $200/mo subscription is absolutely necessary to them. This honestly feels like a repeat of Claude Code back in February. The software engineers eventually realized that AI is absurdly expensive after the companies realized that spending $14000/mo to service a $200/mo subscription is a bad idea. If the AI vendors couldn’t squeeze money out of rich software companies, what exactly are they gonna get out of poor mathematicians and universities? Also, there is the cognitive decline caused by overuse of LLMs that has yet to set in.

I think a serious possibility is that AI generated papers flood the zone with uninteresting incremental results that are eventually meaningless and full of mistakes. Right now, math is full of smart, dedicated people, so at least major results are reviewed carefully. But as AI alarmism drives away many honest people from the field, the remaining mathematicians will be burdened with far more work to review, and their cognitive faculties will be eroded by LLM use. Despite 4 years of development, $3 trillion of debt, mountains of stolen data, all the agents and harnesses and loops and other expensive tricks, as well as the advantages of Lean in math research, LLMs still hallucinate. I believe this is happening with software, but at least there are objective consequences for screwing up there (guy gets his home directory deleted, email is sent on a guy’s behalf without permission, small business gets every customer subscription cancelled). But nothing bad happens if there is a mathematical mistake in a paper and nobody catches it. One could say to just provide a Lean proof, but there is still the issue of making sure the Lean code actually matches the content of the paper. Exactly what force will correct things? Still, I don’t think this is the most likely possibility. The AI companies are extremely unsustainable financially, and it’s not like they’re very popular. Once they collapse, I believe there will be a re-evaluation of how LLMs should be used in research. If they are used (let alone trained), someone is going to have to pay the bills. In the end, we have to ask ourselves the question of why one does math. To me, math is not really a field where you memorize trivia. The real value comes from being able to think abstractly and rigorously from first principles, and from understanding why something is true rather than just knowing it is true. It is another aspect of your ability to reason as a free human. A few dedicated people go into math research, but your skills can easily go to many places. If you’re starting undergrad, you have plenty of time to see how this all pans out before making a decision.

long rant about math The recent big AI results in math have left me in quite a bad mood. I believe the main ingredient is Lean, which is a formal language resembling a programming language. Math proofs written in Lean can be verified deterministically with a computer, which really helps mitigate the hallucination problems of LLMs. Back in the days of pure scaling LLMs and Sam Altman talking about Dyson spheres, I was skeptical that LLMs would do math, but I did think that perhaps in the future, techniques using these formal languages could contribute to math. Well, it seems like OpenAI and Anthropic had the same idea and I underestimated their limitless checkbooks. Many of the biggest results were announced by mathematicians directly working for them (and presumably being paid a handsome amount). For what it’s worth, after the last of these big announcements, I decided to try one of these AIs on one of my small problems that I couldn’t figure out. The AI did give a solution. That is, until I checked it thoroughly and realized that the it had a subtle but severe mistake that made it useless. I reprompted it, it failed again, and I ran out of tokens. I’m sure someone will tell me to shell out $200/mo for a pro subscription. In the math and computer science research community, this is all anyone can really talk about right now. Honestly, after watching this whole AI bubble starting from the very beginning, I think the AI companies want to use marketing to stoke fear that all mathematicians will be replaced. But now, I am just too tired to argue. The amount of alarm and the extraordinary social pressure to use LLMs has soured me to this whole research thing. If becoming a researcher will one day require supporting these evil AI companies, I would rather just not. My dream job now is Factorio developer. A lot of annoying people in technical areas view the world in terms of an intelligence hierarchy: the smartest people do math and physics, the slightly less smart people do coding, and the dumb people do everything else. So if AI can do math then it can do anything else. But, as an example, it is abundantly obvious now that AI is not replacing filmmaking. The techbros might be moved by arguments about how hilariously expensive video generation is, and how all these videos are 2 second clips stitched together so you won’t feel the uncanny valley. But the real reason is that nobody wants to watch slop made with no intention or feeling. Also, nobody wants to support the AI companies, which could not act more evil even if they tried. The mania in math right now quite resembles the mania in software engineering back in December-February, when Claude Code definitely solved all coding. I don’t think the boosters expected that by April, everyone would be complaining about how expensive it all was while seeing an endless parade of vibe coding disasters (and no increase in productivity). Even if math research works out perfectly well (which is a still big if), it’s not going to pay the bills. They would need to find a use case in the real world, where hallucinations can cause serious damage and cannot be formally prevented. And they have certainly tried. Math will not change the fact that all of this will collapse.

Here’s A Taxonomy of Omnicidal Futures Involving Artificial Intelligence by Critch and Tsimerman, which approvingly cites our favorite AI 2027. Nobel disease is still a thing (I think the rationalist beliefs are quite pervasive among mathematicians).

I find it really funny how after he gets booed he says, “If you don’t care about science, that’s okay, because AI is going to touch everything else as well. Whatever path you choose, AI will become part of how work is done.” Yeah, if you’re worried that AI is only going to fuck up science, don’t worry, it’s going to fuck up everything else as well. Was he trying to stick to a (terrible) script, or is he genuinely this incapable of reading a room? “When someone offers you a seat on the rocket ship, you do not ask which seat. You just get on.” No, my mom taught me about stranger danger. I know what to do when a sketchy old man named Eric Schmidt pulls up with a rocket ship that says FREE ICE CREAM. “The rocket ship is here. Let me give you some advice. First, find a way to say yes. Listen.” Thanks for revealing how AI adoption is really about coercion. It doesn’t matter what you think, AI is inevitable and you ignorant Luddites are gonna have to find a way to like it. Truly a masterclass in public speaking by Eric Schmidt. When the audience reacts negatively to what you said, just double down and shove it down their throats. You’re a billionaire, so you know better than them.

How many people, if they were given $1.3 million just once in their lifetime, would figure out far better uses for that money than this guy?

The last several years have been the monkey’s paw moment for rationalists, where they keep getting what they want and realizing it’s actually bad. As for why they keep getting what they want, just look at who’s funding them. (Also featuring a “Chinese curse” that isn’t actually a phrase in Chinese. At least it’s not “may you live in interesting times”.)

I attended a town hall hosted by the department at my university supposedly for general discussion about department affairs. Considering the university had recently made moves such as adding “AI” into the very name of the department, I had suspicions that much of the discussion would be about AI. (I realize I’m doxxing myself but whatever.) I mostly came for the free food, but I was also interested in seeing what people thought about AI. The event started with a talk by a prominent professor with major administrative power in the department, and indeed the talk was mostly about AI. His views were that he personally didn’t like AI, but he believed that it had changed the world (particularly in programming), and that it was going to stay. One of his justifications for pivoting the department to AI was ensuring universities had some say in AI and not letting all the control go to unaccountable corporations. The reaction from the audience was a pleasant surprise to me. He asked everyone how much they were excited about AI (hardly anyone) and how much they were worried (most of the audience). By far the most amusing moment was when someone asked, “What if the assumption that AI is inevitable is wrong? What if AI does not live up to its promises?” (Sadly, I don’t remember the exact words that the person said.) The professor’s response was that by this point, there are so many trustworthy, smart, prominent people who definitely wouldn’t fall for scams, and they have adopted AI. He trusts those people, so he trusts that AI is genuine. I don’t know if the audience member accepted this explanation, but I hope not. Our modus operandi is FOMO. The pizza was only ok, not really worth a 90 minute event.

This really goes to show how much they need to rely on the LLMentalist effect, despite the AI boosters insisting that the AI is totally different now, everything changed in the last few months. They do not care about creating a useful, reliable tool. That concept doesn’t even occur to them, since why do that when AI is magic? In any case, they are incapable of creating a useful, reliable tool. Deep down, the only thing the AI companies have at their disposal is the ELIZA effect. OpenAI has every incentive not to truly eliminate AI psychosis, because they need engagement. They only want to mitigate the extreme cases where people go insane and cause bad PR for them. But mild AI psychosis is totally fine, it’s great when people are addicted to your product and make the numbers go up!

Somehow this is no worse than his usual fare, such as a thumbnail that is just a bunch of colored lines resembling a line chart but without representing any actual data, with some random marked points labeled “Dark Farms” and “Human Zoo”. No, I’m not kidding.

Unfortunately, our problem right now is not Donna the below-average Democrat but Donald the fascist. And when it comes to fascists I do not ask if they are above or below average.

The fire code thing really is an excellent example of LessWrong Brain. Fire truck drivers insist on needlessly large trucks (no citation) which makes roads 30% wider than they would otherwise be (no citation) which has “probably” “non-trivially” contributed to larger cars (no citation) leading to enough additional road fatalities to cancel out the lives saved by stricter fire codes (no citation). The LessWrong Brain argument starts with a deliberately contrarian conclusion and proves it with a Rube Goldberg chain of logical syllogisms. Of course, citations are strictly optional, and they are free to misinterpret them as they see fit. The only real standard of each claim is “looks good to me”, but you are supposed to be impressed that they managed to string a dozen of them together to reveal some shocking, deep truth of the world that nobody else knows about. The AI 2027 nonsense is an infamous example of this. He uses the word “fermi” which is cult jargon based on Fermi estimation, a.k.a. guessing shit with back-of-the-envelope calculations. Not exactly what you want if you want to convince people to reform fire codes, especially if you have zero citations for anything. I guess people just aren’t rational enough, and the only reason the fire codes are so irrational is because people are emotional about fire codes. Firefighters are apparently revered as heroes, when it is really the case that the LWers who should be the heroes. After all, firefighters merely save people from fires, while LWers rent multimillion dollar mansions to save quadrillions of hypothetical people from hypothetical basilisks!

It’s fine, spyware is only a risk when it’s bad people’s spyware. It’s totally fine when it’s Anthropic™-approved spyware! As for Mythos catching things, maybe they should have used Mythos on their very own Claude Code considering that it has hilariously obvious security exploits, such as this one which inserts an arbitrary string into a shell command. Actually, never mind I don’t see anything wrong here, maybe we should burn another $20k in electricity running Mythos on it again to find out.

In basically every case in history where people decided to kill a bad king, there was a period of chaos and violence that followed it. The killing of Charles I happened during the English Civil War, and the killing of Louis XVI happened during the French Revolution. This has happened many times in Chinese history, with the fall of an imperial dynasty leading to several decades of civil war (most recently in the early 1900s). But I guess if you have a big clever brain with big clever thoughts, you don’t need to look at history. If the only way to get rid of a bad king is to kill him, he will do anything he can to defend his power, including abusing it however he can. Even if you successfully get rid of him, good luck establishing a proper government afterwards with all the violence you’ve caused. And who knows if the new king is gonna be better or worse? A better system would instead have a mechanism that replaces officials on a regular basis, say every few years, and ensure that these replacements are peaceful. Oh wait, that’s liberal democracy. If we do something boring like support democracy, how will people ever think of us as special, clever thinkers with bold, contrarian thoughts?

I've seen this story play out in software engineering: people were very impressed when the AI does unexpectedly well in one out of 50 attempts on an easy task, and so people decided to trust it for everything and turn their codebases into disasters. There was no great wave of new high-quality software. Instead, the only real result was that existing software has become far more buggy and insecure. Now we have people using AI in science and math because it was impressive in random demonstrations of solving math problems. I now have friends asking me why I'm not using AI, and also saying that AI will be better than all mathematicians in 30 years or whatever. Do you really think I refuse to use AI out of ignorance? No, I know too much about it! I have seen the same story play out in software engineering, and what makes this any different?

This is what happens when your worldview is based on anime. (A lot of anime has heavy themes, but most people understand that it's not real life, just like all such art. Unlike Yud, most people's worldviews on coding and math are based on actual coding and math.)

We can see that one 9 of availability is 90% = 0.9, two 9s is 99% = 0.99, three 9s is 99.9% = 0.999, etc. In general, for positive integers n, n 9s of availability is 1 - (1/10)^n, and we can extrapolate that to non-integer values of n. The value γ needed for 87.5% availability is the solution to 1 - (1/10)^γ = 7/8, or γ = log_10(8) = 0.903089987. γ is transcendental by Gelfond-Schneider (see this for a reference proof).

Right now, Sora is at zero 9s of availability.

By far the dumbest "feature" in the codebase is this thing called "Buddy" (described in a few places such as here). Honestly, I don't really know what it's for or what the point is.

BUDDY - A Tamagotchi Inside Your Terminal

I am not making this up.

Claude Code has a full Tamagotchi-style companion pet system called "Buddy." A deterministic gacha system with species rarity, shiny variants, procedurally generated stats, and a soul description written by Claude on first hatch like OpenClaw.

...

On top of that, there's a 1% shiny chance completely independent of rarity. So a Shiny Legendary Nebulynx has a 0.01% chance of being rolled. Dang.

Great, so they were planning on a gacha system where you can get an ASCII virtual pet that, uhh, occasionally makes comments? Truly a serious feature for a serious tool for the serious discipline of software engineering. Imagine if IntelliJ decided to pull this bullshit.

But also, Claude Code is leaning hard into gambling addiction — the “Hooked” model. You reward the user with an intermittent, variable reward. This keeps them coming back in the hope of the big win. And it turns them into gambling addicts.

The Onion could not have come up with a better way to illustrate this very point.

Good luck telling the promptfondlers that LLMs are only useful for entertainment and not for any useful work.

I'm sure these English instructions work because they feel like they work. Look, these LLMs feel really great for coding. If they don't work, that's because you didn't pay $200/month for the pro version and you didn't put enough boldface and all-caps words in the prompt. Also, I really feel like these homeopathic sugar pills cured my cold. I got better after I started taking them! No joke, I watched a talk once where some people used an LLM to model how certain users would behave in their scenario given their socioeconomic backgrounds. But they had a slight problem, which was that LLMs are nondeterministic and would of course often give different answers when prompted twice. Their solution was to literally use an automated tool that would try a bunch of different prompts until they happened to get one that would give consistent answers (at least on their dataset). I would call this the xkcd green jelly bean effect, but I guess if you call it "finetuning" then suddenly it sounds very proper and serious. (The cherry on top was that they never actually evaluated the output of the LLM, e.g. by seeing how consistent it was with actual user responses. They just had an LLM generate fiction and called it a day.)