more than anything it's valid enough as a frame of reference for what many people want to do. if they went and invented a new term for it i'd reckon a lot of people just wouldn't even click on it
yes, this is tricky and something we’ve struggled with too. We chose IDE because the closest comp is probably VSCode - lots of folks seem to just use it for code browsing / review these days!
regardless, we ended up renaming “ide” -> “canvas” in on gh + website. hope that’s less confusing. will mull it over in the meantime.
I think it’s particularly infuriating when you sit down, and decide to read something off the reputation of an author (or their institution), only to find out that their writing has a complete disregard for your attention and time…
My problem was more that it seemed to start with one thesis, and then throughout the article took long detours to market other achievements and then managed to not say very much at the end...
I liked the monolith. If I didn't want any AI on my device, I liked having the ability to switch that off and be done with it like Firefox offers (and what Apple offered in the last major release).
While I broadly agree with the premise of re-directing the “purpose” of math, I quite detest the idea that judgement might be primarily based upon some in person discussion, or
oral presentation, and the claim that written mathematics that is not orally communicated might be less worthwhile in some sense (i know this isn’t the exact statement of the authors proposition).
There are a good deal of people, whom, falter much more in oral discussions, whether this be for a psychological thing, stage fright, or difficulty explaining things on the spot. There are also certainly brilliant people, who can’t give an informative, discussion inviting talk to save their lives, but given enough time, can formalize their thoughts in writing at the highest levels of their field, and that writing is likewise very enlightening (sometimes).
It’s not clear to me, that, AI as is, could not pose successfully in an oral discussion of a topic. I mention this because it seems that one implication of the article is that AI might write things that are logically correct, but devoid of understanding. I suggest rather that 1) it is not extremely improbably that AI is incapable of generating mathematics that furthers human understanding and if 2) it is indeed highly likely that they cannot generate mathematics that furthers human understanding in a textual format, then surely one could also differentiate between human and AI on a textual level, and judge the contribution of a human, without the need of oral discussion?
I suppose another aside is, one might claim that the existence of AI means people have much much more text to filter for, and so, it becomes difficult to find one person’s good writing amidst a sea of, logically correct, yet understanding devoid textual content. But by and large much or mathematical academia certainly operates off of some reputation/vouching system presently anyways, that already serves as a “filter” in some sense. Perhaps the existence of such a system/culture is not a good thing, but oral discussions/seminars certainly aren’t immune from such predilections.
Perhaps I’m babbling like an idiot, but the entire and sole purpose of this comment is just to say: for the love of god please don’t let the standard be judged by oral presentation
One thing I've always been curious about is, often times it seems that models don't seem to have these addendums like "dont do excessive/random shit" by default? Or I suppose, if it did have something like that, and still screws up like in your example, it clearly isn't working, so assume that it doesn't.
But the phrase "don't do random shit", semantically, from a monkey's paw perspective, could imply curtailing the model's creativity and 'thinking out of the box' capacity, that might have existed in its 'reasoning' process. So I'm always concerned if it's possible that, adding these phrases might be part of the reason why a model performs dumber than it should.
I don't have empirical evidence to support that supposition though.
>could imply curtailing the model's creativity and 'thinking out of the box' capacity
Imo a feature. I can handle the creativity and out-of-box thinking just fine. I want the model to do the grunt work but be smart enough to do the grunt work correctly.
I think of it as a tradeoff between creativity and specificity. Every instruction you give reduces creativity, and at best, increases specificity (I imagine a lot of prompts like "make no mistakes" do literally nothing but pollute context, but I haven't evaled them)
So if you're doing something very ordinary, fewer instructions result in better results. If you're doing something fairly off-piste, you have to give instructions to that effect and accept less creativity. For situations where you want it to do something extremely specific, tons of instructions and accept that you're going to get much closer output but much worse "intelligence"
Another way to think about it is Type 1 and Type 2 errors or sensitivity and specificity from statistical testing - do you want an agent that solves any problem but goes off the rails 10% of the time, or do you want an agent that can only solve 10% of the problems but nails them 100% of the time (sensitivity and specificity, respectively)
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