hodgehog11

I think a lot of people would like to move on from SPSS, but try telling that to the people that have been using it for the last 25 years.

Ah, are you a statistical consultant? That would explain a lot, but you should then know why these people need you. Or maybe you're in a department where the statistical standards are much higher than in mine.

Sorry, but this really comes across as demonstrating a substantial degree of entitlement. What is the purpose? Easy. A student is trained to use SPSS for their thesis work. They may be given a sitewise license from their university, but only for one device. Then after their training, they lose access to SPSS, but want to do that analysis in their workplace.

Proprietary software, especially at these prices, cause all kinds of issues. SPSS is one of the software packages that has a sizeable audience of people crying out for a true open source alternative. Just because you have the money to afford these things, does not mean that others do.

Also, if you are referring to the similarly named ArtCraft suite as a "pointless activity" then you are really out of touch.

Segmentation faults are super easy to get from AI agents writing C. If you want an agent vibecoding in a compiled language, you have to use Rust. That doesn't mean that the software does what is intended (that requires a review agent and then an orchestrator at minimum) but it makes things a lot easier.

There are tons of people in the social sciences that still, unfortunately, use SPSS to this day. If you think that R/tidyverse/Excel is a true alternative to SPSS, you definitely haven't interacted with any of these people.

Rust is the prime language for vibecoding because the compiler checks most of the issues for you. This is meant to be part of the ArtCraft suite, so it is intended to be developed and managed with AI agents.

This greatly depends on the type of coding you are doing. For easy webdev, sure. For me and my colleagues, LLMs have been quite unhelpful for programming until about five months ago. Mistakes can be devastating, especially at scale. If the simpler stuff works for you, then great. But don't pretend to speak for everyone.

I think this is an unfortunate reality that many miss. For simple and procedural tasks, yes, there are awesome alternatives. DeepSeek 4.1 Flash 1017 is my favourite by far. But for the headlining complex applications, and for consistently reliable reviewing, the gap is genuinely so remarkably far that it is hard to imagine having any alternative in the near future. The closest is the (very expensive) Kimi K3.1 Pro, which is, in my experience, roughly where Anthropic and OpenAI were near the start of the year. DeepSeek is a year behind in this domain. All open models have still yet to cross that monumental improvement that happened around Fable / Sol mid-year.

Because rigour is typically tied to math performance, my own experience has closely matched the FrontierMath Tier 4 benchmark. That should give you an idea of the difference.

I'm not foolish enough to say that AI is not economically viable. But these two companies specifically, OpenAI and Anthropic, just seem totally screwed. They're making great revenue, but their expenditure is unbelievably irresponsible (actually, their behaviour across the board is irresponsible). Training, testing, and marketing should not be this expensive.

I'm looking forward to the new wave of companies that will likely pick up after them, and engage in much more responsible business practices. These two have got to go.

And unfortunately, anyone who uses DeepSeek for serious math applications is extremely foolish, at least right now anyway. Disappointingly, the Chinese models are so far behind on math capability that it is a genuine moat at this point.

Totally different story for coding of course.

Yes, that's about right. It is much easier for it to roughly copy other code that it has seen, than to understand the code it has been given enough to make targeted fixes.

You can definitely still get PCSX2 working on it though, and it's fantastic. PS2 and PS3 games are among my favourite uses for handheld PCs.

I'm normally not the cynical type, and I am appreciative of Karpathy's efforts to educate people on developing language models, but this is exactly how it reads.

Every once in a while, everyone needs to touch grass.

Agreed, every behavior of this particular dev supports the hypothesis that they are producing mass slop to a "good enough" level to receive massive hype, without the intention to put in the thousands of GPU-hours it would require to polish (actually, the estimated roadmap is growing rapidly in expected timeframe as the AI understands the difficulty of the task). I expect there to be more and more of these crappy surface-level implementations before people lose interest and the dev drops all of it.

Indeed! Almost none of these most math folks are likely to read. The only thing to read is the statement, which is only a handful of lines in both cases. The point of Lean is that if that statement compiles and is validated by hand to be equivalent to the natural language statement, then it is true. That was what I was trying to say here.

That's fair, but I would argue that reading the specification is very easy by comparison. A quick one hour tutorial is usually enough judging from my students' experiences.

Why do you believe that learning Lean is a better use of time when the AI is clearly better at writing and interpreting Lean than it is writing quality papers? At this point, Lean is for autoformalization, no one is really supposed to read it.

Do you care to share then? Because anyone I know with any sense right now is aware that "planning for the next forty or so years" is genuinely impossible at this point, especially in mathematics, and the right approach is to remain on your toes, ride the waves, and diversify as much as possible. Heck, even planning for the next five years seems absurd. This is a time of immense uncertainty, and to think otherwise is foolish.

Yes. Most probably do not understand the notation involved in, and the statement of, the Lindeberg-Levy Central Limit Theorem. But every scientist uses this theorem in one way or another. These ideas have a way of trickling down because to people who work thanklessly to do so.

"Plausible" text was preferred over rational text when we trained LLMs using RLHF. It's rapidly shifting the other way now with RLVR, which enforces correctness by default.

No, this is different, and this is coming from someone who has been studying deep learning for the last decade. We are talking about the difference between RLHF and RLVR strategies. The former benefits clarity and explanation, while the latter concerns only correctness. AI was moving in a particularly damaging direction by pushing on the first path, so it was natural to move to the second. But the second will come at the cost of clarity of explanation. It will likely get better at its explanations, but not fast enough to render its most advanced accomplishments readily understandable to the user. The chess example is a pretty good one (that is an RLVR approach).

The first mistake that you are making is assuming that OpenAI is a singular conglomerate with the same set of values across its members, and that its researchers bear the same set of behaviors and opinions as its scummy executives. It isn't. The OpenAI researchers are probably a group of their mates, and they have every belief that they are being just as honest as any of their other colleagues. These antagonistic relationships do not really exist between research institutions; that is an invention of outsiders and marketing departments. Everyone is from the same group.

I don't see the report as absolving the industry of wrongdoing. In fact, I believe it is particularly damning, and many of my colleagues feel the same. No one is on the side of OpenAI here, not even its own members.

As for submitting the report back to OpenAI for comment and working with them, given that there is currently no government mandate or requirement for any AI company to disclose wrongdoing, this seems like an unfortunate necessity. Independent bodies and governing agencies have to be created. At the moment, it is the industry regulating itself, which means no regulation at all.

So no, you do not need to believe that the document is truth. But it is all that we will likely get, because nobody with any power will do a damn thing. That is just as frustrating to the researchers as it is to you. The solution is not to defund the institutes doing needed research. It is to put laws or incentives or something in place to punish inappropriate behavior and lack of disclosure for the companies.

Many of the most prominent people in the AI industry all know each other or have deep social ties. Why? Because they all came from the same places. Neural network research was niche circa 2012 and early adopters post AlexNet were few in number, especially compared to today. This by itself does not infer a conflict of interest. If you believe so, then you might be surprised to find that all research is like this in every field. The most prominent players were part of the initial niche. Sub-optimal, yes, but this is the reality.

Now, I do not know METR, and have not interacted with its staff. $120m is pretty normal for these types of groups as I understand. METR clearly has even tighter ties to the AI companies than any of the groups I do know, so I agree that calling them independent is a bit of a joke. Even without the explicit labels from their website, there are shockingly few degrees of separation in this entire field. The conferences are big, workshops are designed to be highly social, and the stakes feel high.

Regarding the timeframe and limited material, that's pretty standard now. You do what you can.

Now, none of this necessarily invalidates what they did. Yes, there is big money in AI, and that money hires people, but there quite a lot of geeky researchers in these groups that genuinely only care about the science. That level of neurodivergence might be difficult for most people to comprehend, especially if you're focusing on the politics, but these are the types of people attracted to this line of work.

Put yourself in the shoes of these researchers. You are really interested in AI and how it works, and you convince yourself that this is potentially moral to work on because of a possible threat to humanity. You apply to work at one of these groups. You find similarly-minded people and work together on trying to figure things out. Maybe your boss is a sleazebag in bed with the industry, or maybe they're not. You do not care. You work on this, you publish notes and papers, you comment on LessWrong or X, and you leave the company if they don't let you do this stuff, because there are dozens of others.

Most of the public don't know these guys or this work, because they only focus on the crappy and irresponsible companies leading the AI development charge, and alignment research is so unbelievably hard and slow. I can go into the why if you're interested, but the reason is that it is extraordinarily difficult to make progress if your objective is only vaguely defined. This is the same problem with the Yang-Mills mass gap, for example, although I would personally bet that Yang-Mills has a much better chance of being solved than alignment. Just a different tier of difficulty altogether.

I also disagree that p(doom) work (I don't think this describes more than 1% of the work out there, closest I can think of is Tegmark's group, which put it pretty high from recollection) advances the pace of AI. I don't believe it is doing anything. Nobody externally believes it anyway (why would they?). These companies are run by narcissists who believe that they should be the ones to reach "superintelligence".

I think most supervisors try to discourage students from writing in this way because there is a delicate art to it that you are unlikely to be able to meet at that career stage. When I read some of the texts from Martin Hairer and Cedric Villani in my field (or even Riemann's famous paper on the zeta function), you can see that they are motivational and informative but still concise at the same time. Full of personality too, especially Villani. That's a hard balance and it requires exceptional understanding of the topic and the reader.

At the same time, we really should be encouraging it more. I have found that in newer machine learning theory papers (strictly theory, not empirical work), there is something closer to a good balance that is expected even of students.

Including a motivating example is key to this, and should be considered mandatory.

> The outcome of this 'safety' is restricting public access to AI and giving a monopoly of access to the industry.

This is unbelievably ignorant speech. I have not received a dime of any of this funding, but I do know many excellent researchers that have, and they do fantastic work. There is an unbelievable gap between theory and practice regarding the capacity of deep learning, and while great strides have been made to develop the surrounding theory, there is a long way to go. Many believe that without a concrete understanding of how neural networks properly learn concepts, we have little hope of molding them to be reliably useful. It costs money to hire researchers and develop fundamental theory.

Just because you don't understand any of that work, does not mean that it is pointless. This is fundamental research that is 20 years behind schedule.

Maybe just nostalgia talking, but the height of Newgrounds really seemed like a golden age for light gaming. The shift to mobile games has just resulted in a large number of gambling simulators, which I'm sure has been a key reason for the popularity of Kalshi, Polymarket, etc.

This is a fantasy I remember hearing in grad school. Get a little more senior and the whole perception shatters. You can put whatever rubbish you want out there. But if you are getting attention, or want attention, you have to play by the rules. Those rules are whatever the community dictates. Otherwise, why would Nick Polson be at risk of being discredited? It's no different from any other field, math is not special. Correctness is a necessity. It is not sufficient.

This was my point though. Not sure why you are disagreeing? The original argument was that there was no criteria for publication other than replication, or that is how I read it. I was refuting that.

Perelman was already working on an interesting problem, and wrote in ways that were less formal, but still academically sound, so of course he did not need to publish it. What about the people working on problems that are not interesting? You don't think they encounter significant difficulties getting their work out there?

Absolutely not. Otherwise, if I prove something original, and it is correct and replicable, this would mean I can publish in Annals of Mathematics. Now if we narrow that to publishing somewhere, then sure, but that's irrelevant. This is about values for prominent results (e.g. Annals) for mathematicians.

Care to explain? If a paper is not interesting, it would not get past reviewers. Unless "interesting" is not part of the criteria for entry to the journal? That sounds interesting if true, but that certainly has never been my experience.

I feel like I am going crazy, since this is taught in most academic writing courses. Being replicable or persuasive or correct is not enough. It needs to be perceived as valuable, or it will not get through. I learned that the hard way, and found others that taught this message only later.