trostaft

?! When did that happen. Great!

There's (at least) 2 of us! Well, mine is a little minisforum unit, but the idea is all the same.

Well, if the thesis is that we're running the heavy work on a remote server that's less of an issue.

That algorithm won't converge anytime soon. A pre-conditioner (i.e. at least some apprentice wizardy) helps.

It was, but the comments on that post were insanely depressing to read. I'm a mathematician, and find myself largely hopeful and excited for its future, but being online this last week has been incredibly brutal due to the surrounding vitriol. It's an avalanche of negativity, and I'm largely not talking about that of my peers (though indeed, some of my peers). Most mathematicians I've spoken have been reasonable / positive, perhaps an effect of my field.

I really need to just stay off social media for like a month and keep talking IRL. Longer, if I know what's good for me.

MCMC = Markov Chain Monte Carlo

It's a way to approximately draw samples from a probability distribution. Crucially, it applies even when we only know the distribution up to a multiplicative constant which is a common ailment of many distributions in the computational uncertainty quantification field (not that we don't know the constant, but that it's usually computationally catastrophic to estimate it well).

Not much computational mathematics here, but I do see some of interest in the MCMC community. In particular, 139, 93, and 101 are very interesting. Also (I'm not too familiar), I remember attending some talks on the Crouzeix conjecture 325 attacks, should sharpen some rates for Krylov methods. Obviously need to read deeper, some of these don't have corresponding Lean formalizations.

Cool!

> Why aren't mathematicians excited about the frontiers being unlocked by AI?

Well, I wouldn't generalize based off of the thread OP (and people on social media, including me). I think a lot of us are very excited! Most of my collaborators are, including myself.

There's a lot of simultaneous social change that's accompanying these tools, not all of which is positive. Agonized screaming is pretty loud, relatively speaking to the rest of the conversation.

Indeed, I've switched over to model similar to this in the course I teach. Weekly optional problem set + in-class quiz every week with one homework problem verbatim + an extension. The homeworks are completely open resource, LLM, friend, sorcery, w/e. I figure that, even if they're given an oracle, they have to internalize the ideas to the point where they can generalize on the follow-up. I'll only really know when the course finishes, but I think this will work out. I want to pair this with an oral midterm and final, but that'll have to wait a semester.

You're talking about this article? I don't see how you could get that, fundamentally I read it the other way around. Unless you're talking about the base assumption of capabilities.

> As an ex-mathematician I assure you this is very wrong, and every working mathematician I know right now is completely miserable, and/or trying to flee the field as fast as possible.

To throw a counterpoint to this into the writhing cesspit of HN, I'm active in academic mathematics (postdoc) and every one of my collaborators is deeply in love with the field and their jobs. Perhaps the grass is greener on the applied mathematics side of the fence.

Am I doing my napkin math correct? The post says it's using a model comparable to Fable 5.1, which is $50 per million output tokens. So this is ~$300K? Surely an over-estimate due to caching.

Ah, interesting! I've been toying around with a formalizing of a paper I'm closing to submitting; maybe I'll try to clean it up into a format for this.

I'm teaching a university math course next semester and I've been messing around with a solution to this that my old analysis professor used to employ. Every week, they take home a handful of theory questions. These aren't turned in for credit. But, every week, there'll be a short quiz where one of those questions are asked verbatim, along with a small follow up.

That felt like a very natural solution to me. Of course, worst case scenario, they just blindly generate and memorize the proofs without thinking. But I think the follow ups will catch them in that case. Moreover, I choose to believe that >90% of my students are willing to at least put in the work of scrutinizing those proofs which is honestly not bad exercise in its own right.

I would love to do oral exams directly, the logistics with the number of students I have just make that hard. I know we're trialing it with the less populated special topics courses.

I've been curious of other publication quality figure generation libraries for python, if you have a moment I'd be interested as to why you chose Altair over plotnine? I'm well and truly entrenched in the Matplotlib style of plotting, so I appreciate any opinions here.