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On HN it's very common to see a blog post along the lines of "I found this old piece of equipment with no brand name, I used some network traffic inspection to figure out what it does, I hacked around a bit, I got it working and turned it into a self-ringing doorbell with wifi" (or whatever). All of that is anecdotal, N=1, "I did what worked for me, I hope it's interesting to you". And those posts are highly prized and rightly so.


That's great yes, and I'd like us to give even more weight to the lived experience of others even in other contexts and regarding other subject matters.


In contrast I like the fact that there is often an indication of what is not substantiated by strong/provable/scientific evidence and what is not. In fact I quite love both these subjective experience reporting and the more sceptical perspectives. A lot depends on the specific subject and whether evidence exists or is feasible to gather. I would hate to not hear about cool sounding ideas that MIGHT work, but is just not endorsed by rigorous chains of evidence. As long as the discussions are honest and in good spirit. People can point out risks and likihoods and alternative explanations. I really like those too.


(a) no it's not

(b) your comment is miles off-topic, as he is not addressing doom in any sense


But they're submissions to ICML.


It could certainly replace the author of this article.


Modern LLMs can one-shot code in a totally new language, if you provide the language manual. And you have to provide the language manual, because otherwise how can the students learn the language.


> The fact that AI can do your homework should tell you how much your homework is worth.

A lot of people who say this kind of thing have, frankly, a very shallow view of what homework is. A lot of homework can be easily done by AI, or by a calculator, or by Wikipedia, or by looking up the textbook. That doesn't invalidate it as homework at all. We're trying to scaffold skills in your brain. It also didn't invalidate it as assessment in the past, because (eg) small kids don't have calculators, and (eg) kids who learn to look up the textbook are learning multiple skills in addition to the knowledge they're looking up. But things have changed now.


Completely agree - I always thought the framing of "exercises" is the right one, the point is that your brain grows by doing. It's been possible for a long time to e.g. google a similar algebra problem and find a very relevant math stackexchange post, doesn't mean the exercises were useless.

"The fact that forklift truck can lift over 500kg should tell you how worthwhile it is for me to go to a gym and lift 100kg." - complete non-sequitur.


If the judge of your fitness took into account only the amount you could lift, then sure, why not use a forklift.

This is what's happened in education. The education system is not concerned with teaching you to learn, or even teaching you, it's concerned with grades.

As many have stated here, homework is practice for the student, and can be helpful in learning. However, it's given out as something to grade without any collaboration on the learning experience.

By the way, I'm ranting about all this as someone who did very well in both high-school and university, so I feel somewhat justified in calling bullshit on the whole thing.


> A lot of homework can be easily done by AI

Then maybe the homework assignment has been poorly chosen. I like how the article's author has decided to focus on the process and not the product and I think that's probably a good move.

I remember one of my kids' math teachers talked about wanting to switch to in inverted classroom. The kids would be asked to read a some part of their textbook as homework and then they would work through exercise sheets in class. To me, that seemed like a better way to teach math.

> But things have changed now.

Yep. Students are using AIs to do their homework and teachers are using AIs to grade.


Yep, making time to sit down to do homework, forming an understanding of planning the doing part, forming good habits of doing them, knowing how to look up stuff, in a book index or on Wikipedia or by searching or asking AI. The expectation is still that some kind of text output needs to be found and then read, digested.


Absolutely devastating for the credibility of FAIR.


I thought the latest llama were not from FAIR but from the genai team


Link [1] doesn't seem to mention svg or vector graphics at all.


I think the point was that like SVG, MNG has the ability to transform existing objects in order to compose an image.

See this example[1] for illustration.

[1]: http://www.libpng.org/pub/mng/spec/#example4


> Link [1] doesn't seem to mention svg or vector graphics at all.

They specifically called out that it doesn't support vector graphics, so the latter shouldn't be a surprise.

And if you want to check if it's like SVG but worse, you need to compare that spec with the SVG spec, not look for the word SVG.


This important paper from Anthropic includes evidence that part (but only part) of reasoning is cross-lingual:

https://www.anthropic.com/research/tracing-thoughts-language...


No, you're confusing GA with GP.


You're right! My bad. Thank you for pointing it out! Leaving my comment up for info on bayesopt.


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