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Cake day: May 16th, 2025

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  • Every time I hear a moderate AI argument (e.g. AI will be an aid for searching literature or writing code), it’s like, “Look, it’s impressive that the AI managed to do this. Sure, it took about three dozen prompts over five hours, made me waste another five hours because it generated some completely incorrect nonsense that I had to verify, produced an answer that was much lower quality than if I had just searched it up myself, and boiled two lakes in the process. You should acknowledge that there is something there, even if it did take a trillion dollars of hardware and power to grind the entire internet and all books and scientific papers into a viscous paste. Your objections are invalid because I’m sure things are gonna improve because Progress.”

    I am doubly annoyed when I turn my back and they switch back to spouting nonsense about exponential curves and how AI is gonna be smarter than humans at literally everything.



  • More AI bullshit hype in math. I only saw this just now so this is my hot take. So far, I’m trusting this r/math thread the most as there are some opinions from actual mathematicians: https://www.reddit.com/r/math/comments/1o8xz7t/terence_tao_literature_review_is_the_most/

    Context: Paul Erdős was a prolific mathematician who had more of a problem-solving style of math (as opposed to a theory-building style). As you would expect, he proposed over a thousand problems for the math community that he couldn’t solve himself, and several hundred of them remain unsolved. With the rise of the internet, someone had the idea to compile and maintain the status of all known Erdős problems in a single website (https://www.erdosproblems.com/). This site is still maintained by this one person, which will be an important fact later.

    Terence Tao is a present-day prolific mathematician, and in the past few years, he has really tried to take AI with as much good faith as possible. Recently, some people used AI to search up papers with solutions to some problems listed as unsolved on the Erdős problems website, and Tao points this out as one possible use of AI. (I personally think there should be better algorithms for searching literature. I also think conflating this with general LLM claims and the marketing term of AI is bad-faith argumentation.)

    You can see what the reasonable explanation is. Math is such a large field now that no one can keep tabs on all the progress happening at once. The single person maintaining the website missed a few problems that got solved (he didn’t see the solutions, and/or the authors never bothered to inform him). But of course, the AI hype machine got going real quick. GPT5 managed to solve 10 unsolved problems in mathematics! (https://xcancel.com/Yuchenj_UW/status/1979422127905476778#m, original is now deleted due to public embarrassment) Turns out GPT5 just searched the web/training data for solutions that have already been found by humans. The math community gets a discussion about how to make literature more accessible, and the rest of the world gets a scary story about how AI is going to be smarter than all of us.

    There are a few promising signs that this is getting shut down quickly (even Demis Hassabis, CEO of DeepMind, thought that this hype was blatantly obvious). I hope this is a bigger sign for the AI bubble in general.

    EDIT: Turns out it was not some rando spreading the hype, but an employee of OpenAI. He has taken his original claim back, but not without trying to defend what he can by saying AI is still great at literature review. At this point, I am skeptical that this even proves AI is great at that. After all, the issue was that a website maintained by a single person had not updated the status of 10 problems inside a list of over 1000 problems. Do we have any control experiments showing that a conventional literature review would have been much worse?




  • Most restaurant origin stories involve someone sharing their favorite taco recipe or whatever. These guys start off with a bad pop-history explanation of the battle of Alesia. That’s how you know their food is great.

    There’s more where the founder of the company talks about how he really hated working at his family’s restaurant while growing up (good sign). Knowing that his family came from China adds another layer of weirdness, in my opinion. The characters where the company name comes from (改革) can be read in both Chinese (gǎigé) and Japanese (kaikaku) and mean the same thing (reform) in both languages. It just feels so weird that he talks so much fluff about Julius Caesar, mentions his family from China and then, out of the blue, uses a Japanese name for the company. What is with these people fetishizing ancient Rome and Japan so much?





  • 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.



  • I don’t know any quantum physics and I’ve only taken one class on quantum computing, but the part about real vs complex numbers is quite funny to me. The very first homework exercise in that class was showing that, in quantum computation, there is no difference in using real or complex amplitudes (you can simulate any pure state with complex amplitudes using real amplitudes and only one extra qubit). The real reason to use complex amplitudes is “Why not, real numbers are complex numbers anyway.” It does help that the quantum Fourier transform is far more convenient with complex amplitudes.


  • 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.



  • On one side, we have a trolley problem thought experiment involving hypothetical children tied to hypothetical train tracks and some people sending him rude emails. On the other side, we have actual dead children and actual hospitals and apartments reduced to rubble. I wonder which side is more convincing to me?

    It’s the same pattern of thought as rationalists with AI, trying to fit everything they see into their apocalypse narrative while ignoring the real harms. Rationalists talk a good game about evidence, but what I see them do in practice is very different. First, use mental masturbation (excuse me, “first principles”) to arrive at some predetermined edgy narrative, and then cherry pick and misinterpret all evidence to support it. It is very important that the narratives are edgy, otherwise what are we even writing 10,000 word blog posts for?