Oh yeah, he wrote an update saying that the LLM is still great, even if the result is already known, because it saves him time. We have come full circle back to the exact same value proposition as the vibe coders.

After seeing this, I reminded myself that I’ve seen this type of thing happen before. Over the past half year, so many programmers enthusiastically embraced vibe coding after seeing one or two impressive results when trying it out for themselves. We all know how that is going right now. Baldur Bjarnason had some great essays (1, 2) about the dangers of relying on self-experimentation when judging something, especially if you’re already predisposed into believing it. It’s like a mark believing in a psychic after he throws out a couple dozen vague statements and the last one happens to match with something meaningful, after the mark interprets it for him.

Edit: Accidentally hit reply too early.

Pigs will be a true method of space exploration if they can fly to Mars in 1 hour.

Not sure if analog turing machines provide any new capabilities that digital TMs do, but I leave that question for the smarter people in the subject of theorethical computer science

The general idea among computer scientists is that analog TMs are not more powerful than digital TMs. The supposed advantage of an analog machine is that it can store real numbers that vary continuously while digital machines can only store discrete values, and a real number would require an infinite number of discrete values to simulate. However, each real number “stored” by an analog machine can only be measured up to a certain precision, due to noise, quantum effects, or just the fact that nothing is infinitely precise in real life. So, in any reasonable model of analog machines, a digital machine can simulate an analog value just fine by using enough precision.

There aren’t many formal proofs that digital and analog are equivalent, since any such proof would depend on exactly how you model an analog machine. Here is one example.

Quantum computers are in fact (believed to be) more powerful than classical digital TMs in terms of efficiency, but the reasons for why they are more powerful are not easy to explain without a fair bit of math. This causes techbros to get some interesting ideas on what they think quantum computers are capable of. I’ve seen enough nonsense about quantum machine learning for a lifetime. Also, there is the issue of when practical quantum computers will be built.

From the ChatGPT subreddit: Gemini offers to pay me for a developer to fix its mess

Who exactly pays for it? Google? Or does Google send one of their interns to fix the code? Maybe Gemini does have its own bank account. Wow, I really haven’t been keeping up with these advances in agentic AI.

OpenAI claims that their AI can get a gold medal on the International Mathematical Olympiad. The public models still do poorly even after spending hundreds of dollars in computing costs, but we’ve got a super secret scary internal model! No, you cannot see it, it lives in Canada, but we’re gonna release it in a few months, along with GPT5 and Half-Life 3. The solutions are also written in an atrociously unreadable manner, which just shows how our model is so advanced and experimental, and definitely not to let a generous grader give a high score. (It would be real interesting if OpenAI had a tool that could rewrite something with better grammar, hmmm…) I definitely trust OpenAI’s major announcements here, they haven’t lied about anything involving math before and certainly wouldn’t have every incentive in the world to continue lying!

It does feel a little unfortunate that some critics like Gary Marcus are somewhat taking OpenAI’s claims at face value, when in my opinion, the entire problem is that nobody can independently verify any of their claims. If a tobacco company released a study about the effects of smoking on lung cancer and neglected to provide any experimental methodology, my main concern would not be the results of that study.

Hmm, should I be more worried and outraged about genocides that are happening at this very moment, or some imaginary scifi scenario dreamed up by people who really like drawing charts?

One of the ways the rationalists try to rebut this is through the idiotic dust specks argument. Deep down, they want to smuggle in the argument that their fanciful scenarios are actually far more important than real life issues, because what if their scenarios are just so bad that their weight overcomes the low probability that they occur?

(I don’t know much philosophy, so I am curious about philosophical counterarguments to this. Mathematically, I can say that the more they add scifi nonsense to their scenarios, the more that reduces the probability that they occur.)

There’s really no good way to make any statements about what problems LLMs can solve in terms of complexity theory. To this day, LLMs, even the newfangled “reasoning” models, have not demonstrated that they can reliably solve computational problems in the first place. For example, LLMs cannot reliably make legal moves in chess and cannot reliably solve puzzles even when given the algorithm. LLM hypesters are in no position to make any claims about complexity theory.

Even if we have AIs that can reliably solve computational tasks (or, you know, just use computers properly), it still doesn’t change anything in terms of complexity theory, because complexity theory concerns itself with all possible algorithms, and any AI is just another algorithm in the end. If P != NP, it doesn’t matter how “intelligent” your AI is, it’s not solving NP-hard problems in polynomial time. And if some particularly bold hypester wants to claim that AI can efficiently solve all problems in NP, let’s just say that extraordinary claims require extraordinary evidence.

Koppelman is only saying “complexity theory” because he likes dropping buzzwords that sound good and doesn’t realize that some of them have actual meanings.

I study complexity theory and I’d like to know what circuit lower bound assumption he uses to prove that the AI layoffs make sense. Seriously, it is sad that the people in the VC techbro sphere are thought to have technical competence. At the same time, they do their best to erode scientific institutions.

Username called “The Dao of Bayes”. Bayes’s theorem is when you pull the probabilities out of your posterior.

知者不言，言者不知。 He who knows (the Dao) does not (care to) speak (about it); he who is (ever ready to) speak about it does not know it.

AI research is going great. Researchers leave instructions in their papers to any LLM giving a review, telling them to only talk about the positives. These instructions are hidden using white text or a very small font. The point is that this exploits any human reviewer who decides to punt their job to ChatGPT.

My personal opinion is that ML research has become an extreme form of the publish or perish game. The most prestigious conference in ML (NeurIPS) accepted a whopping 4497 papers in 2024. But this is still very competitive, considering there were over 17000 submissions that year. The game for most ML researchers is to get as many publications as possible in these prestigious conferences in order to snag a high paying industry job.

Normally, you’d expect the process of reviewing a scientific paper to be careful, with editors assigning papers to people who are the most qualified to review them. However, with ML being such a swollen field, this isn’t really practical. Instead, anyone who submits a paper is also required to review other people’s submissions. You can imagine the conflicts of interest that can occur (and lazy reviewers who just make ChatGPT do it).

I know r/singularity is like shooting fish in a barrel but it really pissed me off seeing them misinterpret the significance of a result in matrix multiplication: https://old.reddit.com/r/singularity/comments/1knem3r/i_dont_think_people_realize_just_how_insane_the/

Yeah, the record has stood for “FIFTY-SIX YEARS” if you don’t count all the times the record has been beaten since then. Indeed, “countless brilliant mathematicians and computer scientists have worked on this problem for over half a century without success” if you don’t count all the successes that have happened since then. The really annoying part about all this is that the original announcement didn’t have to lie: if you look at just 4x4 matrices, you could say there technically hasn’t been an improvement since Strassen’s algorithm. Wow! It’s really funny how these promptfans ignore all the enormous number of human achievements in an area when they decide to comment about how AI is totally gonna beat humans there.

How much does this actually improve upon Strassen’s algorithm? The matrix multiplication exponent given by Strassen’s algorithm is log₄(49) (i.e. log₂(7)), and this result would improve it to log₄(48). In other words, it improves from 2.81 to 2.79. Truly revolutionary, AGI is gonna make mathematicians obsolete now. Ignore the handy dandy Wikipedia chart which shows that this exponent was … beaten in 1979.

I know far less about how matrix multiplication is done in practice, but from what I’ve seen, even Strassen’s algorithm isn’t useful in applications because memory locality and parallelism are far more important. This AlphaEvolve result would represent a far smaller improvement (and I hope you enjoy the pain of dealing with a 4x4 block matrix instead of 2x2). If anyone does have knowledge about how this works, I’d be interested to know.