I don't understand why GP's comment is so controversial. GP is not denying that you should maybe think a little before a key hits the keyboard as many commenters seem to suppose. Both can be true.

I'm not familiar with Whisper in particular, but typically what happens in an ASR model is that the decoder, speaking loosely, sees "the future" (i.e. the audio after the chunk it's trying to decode) in a sentence like this, and also has the benefit of a language model guiding its decoding so that grammatical productions like "I like ice cream" are favored over "I like I scream".

Inference on a generic LLM may not be subject to these non-determinisms even on a GPU though, idk

On the one hand, I've caught some students red handed (ChatGPT generated their exact solution and they were utterly unable to explain the advanced Python that was in their solution) and had to award them 0s for assignments, which was heartbreaking. On the other, I was pleasantly surprised to find that most of my students are not using AI to generate wholesale their submissions for programming assignments--or at least, if they're doing so, they're putting in enough work to make it hard for me to tell, which is still something I'd count as work which gets them to think about code.

There is the more difficult matter, however, of using AI to work through small-scale problems, debug, or explain. On the view that it's kind of analogous to using StackOverflow, this semester I tried a generative AI policy where I give a high-level directive: you may use LLMs to debug or critique your code, but not to write new code. My motivation was that students are going to be using this tech anyway, so I might as well ask them to do it in a way that's as constructive for their learning process as possible. (And I explained exactly this motivation when introducing the policy, hoping that they would be invested enough in their own learning process to hear me.) While I still do end up getting code turned in that is "student-grade" enough that I'm fairly sure an LLM couldn't have generated it directly, I do wonder what the reality of how they really use these models is. And even if they followed the policy perfectly, it's unclear to me whether the learning experience was degraded by always having an easy and correct answer to any problem just a browser tab away.

Looking to the future, I admit I'm still a bit of an AI doomer when it comes to what it's going to do to the median person's cognitive faculties. The most able LLM users engage with them in a way that enhances rather than diminishes their unaided mind. But from what I've seen, the more average user tends to want to outsource thinking to the LLM in order to expend as little mental energy as possible. Will AI be so good in 10 years that most people won't need to really understand code with their unaided mind anymore? Maybe, I don't know. But in the short term I know it's very important, and I don't see how students can develop that skill if they're using LLMs as a constant crutch. I've often wondered if this is like what happened when writing was introduced, and capacity for memorization diminished as it became no longer necessary to memorize epic poetry and so on.

I typically have term projects as the centerpiece of the student's grade in my courses, but next year I think I'm going to start administering in-person midterms, as I fear that students might never internalize fundamentals otherwise.

My understanding as a hobbyist brewer and fermenter is that this is not true. Fermentation is greatly slowed at lower temperatures, but you should have things happening above freezing. Lager beers, for example, go from pure sugar to beer at 35F. And kimchi matures at fridge temperatures in ways that I'm pretty sure are caused by fermentation.

In an ideal world would second-language speakers of English proofread assiduously? Of course, yes. But time is finite, and in cases like this, so long as a threshold of comprehensibility is cleared, I always give the benefit of the doubt to the authors and surmise that they spent their limited resources focusing on what's more important. (I'd have a much different opinion if this were marketing copy instead of a research paper, of course.)

The first stab was basically correct, but then I needed to prompt him several times to correct an issue where the disk that's currently picked up was being rendered as a vertical strip instead of as a disk, and then I just told him to convert the React into pure JS and IIRC it worked first try.

First, saying a model "scours through its vast training data" is misleading at best: at inference time, LLMs no longer have direct access to their training data; they only have access to it insofar as it's been encoded in its parameters.

Second, saying "Every instance of an answer in the training data is like a vote" doesn't give the full picture. First of all, there are embedded contexts where "votes" can be negated: consider saying "the Earth is flat." vs. "We know it's false that the Earth is flat." or "Only a fool thinks the Earth is flat." All three contain the substring "the Earth is flat.", but both humans and LLMs are able to use context to understand that the latter two sentences are doing the opposite of endorsing the proposition that the Earth is flat. You could even imagine an extended satirical bit with "the Earth is flat" embedded within it where it is clear to a reader that all its content is intended to be taken as farcical, and I'd wager that an LLM would in many cases recognize this. So the voting metaphor breaks down here--it makes you think that the LLM is just keeping a tally of propositions, but really, it is doing something a bit more sophisticated.

I don't disagree with the premise, of course. LLM overhype is real. But we should be skeptical for the right reasons. Anna Rogers and Sasha Luccioni have a paper I really like: https://openreview.net/pdf?id=M2cwkGleRL