lagrangeinterpolator

Don't forget the other comment saying that if you hate AI, you're just "vice-signalling" and "telegraphing your incuruosity (sic) far and wide". AI is just like computer graphics in the 1960s, apparently. We're still in early days guys, we've only invested trillions of dollars into this and stolen the collective works of everyone on the internet, and we don't have any better ideas than throwing more ~~money~~ compute at the problem! The scaling is still working guys, look at these benchmarks that we totally didn't pay for. Look at these models doing mathematical reasoning. Actually don't look at those, you can't see them because they're proprietary and live in Canada.

In other news, I drew a chart the other day, and I can confidently predict that my newborn baby is on track to weigh 10 trillion pounds by age 10.

EDIT: Rich Hickey has now disabled comments. Fair enough, arguing with promptfondlers is a waste of time and sanity.

I went deep into the Yud lore once. A single fluke SAT score served as the basis for Yud's belief in his own world-changing importance. In middle school, he took an SAT with a score of 670 verbal and 740 math (maximum 800 each) and the Midwest Talent Search contacted him to tell him that his scores were very high for a middle schooler. Despite his great pains to talk about how he tried to be humble about it, he also says that he was in the "99.9998th percentile" and "not only bright but waayy out of the ordinary."

I was in the math contest scene. I have good friends who did well on AP Calculus in middle school, and were skilled enough at contests that they would have easily gotten an 800 on the math SAT if they took it. Even so, there were middle schoolers who were far more skilled than them, and I have seen other people who were far less "talented" in middle school rise to great heights later in life. As it turns out, skills can be developed through practice.

Yud's performance would not even be considered impressive in the math contest community, let alone justify calling him one of the most important people in the world. Perhaps at the time, he didn't know better. But he decided to make this a core part of his self-identity. His life quickly spiraled out of control, starting with him refusing to attend high school.

It is how professors talk to each other in ... debate halls? What the fuck? Yud really doesn't have any clue how universities work.

I am a PhD student right now so I have a far better idea of how professors talk to each other. The way most professors (in math/CS at least) communicate in a spoken setting is through giving talks at conferences. The cool professors use chalkboards, but most people these days use slides. As it turns out, debates are really fucking stupid for scientific research for so many reasons.

1. Science assumes good faith out of everyone, and debates are needlessly adversarial. This is why everyone just presents and listens to talks.
2. Debates are actually really bad for the kind of deep analysis and thought needed to understand new research. If you want to seriously consider novel ideas, it's not so easy when you're expected to come up with a response in the next few minutes.
3. Debates generally favor people who use good rhetoric and can package their ideas more neatly, not the people who really have more interesting ideas.
4. If you want to justify a scientific claim, you do it with experiments and evidence (or a mathematical proof when applicable). What purpose does a debate serve?

I think Yud's fixation on debates and "winning" reflects what he thinks of intellectualism. For him, it is merely a means to an end. The real goal is to be superior and beat up other people.

Choice quote from Dave Karpf:

Policy moderation can never fail. It can only be failed.

Yeah, it's not like reviewers can just write "This paper is utter trash. Score: 2" unless ML is somehow an even worse field than I previously thought.

They referenced someone who had a paper get rejected from conferences six times, which to me is an indication that their idea just isn't that good. I don't mean this as a personal attack; everyone has bad ideas. It's just that at some point, you just have to cut your losses with a bad idea and instead use your time to develop better ideas.

So I am suspicious that when they say "constructive feedback", they don't mean "how do I make this idea good" but instead "what are the magic words that will get my paper accepted into a conference". ML has become a cutthroat publish-or-perish field, after all. It certainly won't help that LLMs are effectively trained to glaze the user at all times.

AI researchers are rapidly embracing AI reviews, with the new Stanford Agentic Reviewer. Surely nothing could possibly go wrong!

Here's the "tech overview" for their website.

Our agentic reviewer provides rapid feedback to researchers on their work to help them to rapidly iterate and improve their research.

The inspiration for this project was a conversation that one of us had with a student (not from Stanford) that had their research paper rejected 6 times over 3 years. They got a round of feedback roughly every 6 months from the peer review process, and this commentary formed the basis for their next round of revisions. The 6 month iteration cycle was painfully slow, and the noisy reviews — which were more focused on judging a paper's worth than providing constructive feedback — gave only a weak signal for where to go next.

How is it, when people try to argue about the magical benefits of AI on a task, it always comes down to arguing "well actually, humans suck at the task too! Look, humans make mistakes!" That seems to be the only way they can justify the fact that AI sucks. At least it spews garbage fast!

(Also, this is a little mean, but if someone's paper got rejected 6 times in a row, perhaps it's time to throw in the towel, accept that the project was never that good in the first place, and try better ideas. Not every idea works out, especially in research.)

When modified to output a 1-10 score by training to mimic ICLR 2025 reviews (which are public), we found that the Spearman correlation (higher is better) between one human reviewer and another is 0.41, whereas the correlation between AI and one human reviewer is 0.42. This suggests the agentic reviewer is approaching human-level performance.

Actually, now all my concerns are now completely gone. They found that one number is bigger than another number, so I take back all of my counterarguments. I now have full faith that this is going to work out.

Reviews are AI generated, and may contain errors.

We had built this for researchers seeking feedback on their work. If you are a reviewer for a conference, we discourage using this in any way that violates the policies of that conference.

Of course, we need the mandatory disclaimers that will definitely be enforced. No reviewer will ever be a lazy bum and use this AI for their actual conference reviews.

I'm a nerd and even I want to shove this guy in a locker.

Referencing the telephone game does not prove anything here. The telephone game is shows that humans are not good at copying something exactly without changes, which computers are better at. But the question here is if AI can achieve deeper understanding of a work, which is needed to produce a good summary. This is something humans are far better at. The AI screws up the summary here in ways that no reasonable person who has watched the TV series (or played the games) would ever screw up.

A game/sneer where you are a venture capitalist with billions invested in generative AI: https://woe-industries.itch.io/you-have-billions-invested-in-generative-ai

Although I never use LLMs for any serious purpose, I do sometimes give LLMs test questions in order to get firsthand experience on what their responses are like. This guide tracks quite well with what I see. The language is flowery and full of unnecessary metaphors, and the formatting has excessive bullet points, boldface, and emoji. (Seeing emoji in what is supposed to be a serious text really pisses me off for some reason.) When I read the text carefully, I can almost always find mistakes or severe omissions, even when the mistake could easily be remedied by searching the internet.

This is perfectly in line with the fact that LLMs do not have deep understanding, or the understanding is only in the mind of the user, such as with rubber duck debugging. I agree with the "Barnum-effect" comment (see this essay for what that refers to).

The most obvious indication of AI I can see is the countless paragraphs that start with a boldfaced "header" with a colon. I consider this to be terrible writing practice, even for technical/explanatory writing. When a writer does this, it feels as if they don't even respect their own writing. Maybe their paragraphs are so incomprehensible that they need to spoonfeed the reader. Or, perhaps they have so little to say that the bullet points already get it across, and their writing is little more than extraneous fluff. Yeah, much larger things like sections or chapters should have titles, but putting a header on every single paragraph is, frankly, insulting the reader's intelligence.

I see AI output use this format very frequently though. Honestly, this goes to show how AI appeals to people who only care about shortcuts and bullshitting instead of thinking things through. Putting a bold header on every single paragraph really does appeal to that type.

Chicken penne is now rivaling spaghetti Bolognese as a core layer of pasta infrastructure.