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Nice one lol (this should get filtered too)

The original prompts did significantly rewrite the original and drift into different meaning. Through some tuning, they are now mostly faithful to the original without unnecessary snark.

Isn’t it the actual number?

This is an amazing and unexpected result. To be clear though, it does not imply that NP is subexponential: "Note that runtime bounds of the form 2^o(n) do not transfer under general polynomial-time reductions (2^n^o(1) would be needed for this), so our result does not imply that general NP problems admit subexponential time algorithms."

Edit: To explain more, it is guaranteed that you can reduce any NP problem to 3SAT in polynomial time with a polynomial factor in input size. You still have to solve it.

If 3SAT were solvable in polynomial time, then polynomial(polynomial(x)) = polynomial(x) and you're implied to have an overall polynomial time solution.

But what we have here is that 3SAT is solveable in subexponential time. subexponential(polynomial(x)) is not guaranteed to be sub-exponential.

Further edit: The subexponential solution does carry over to any (?) problems that have can be reduced to 3SAT in a linear number of variables. They list: Counting independent sets, Counting proper q-colorings, Counting perfect matchings.

See this from the paper: "Note that runtime bounds of the form 2^o(n) do not transfer under general polynomial-time reductions (2^n^o(1) would be needed for this), so our result does not imply that general NP problems admit subexponential time algorithms."

It doesn't imply NP is sub-exponential, only that 3SAT is. Conversions to other problems can still end up exponential.

>With BERT, you need a large, labeled dataset, and you have to train/fine-tune the model.

I think they were responding to this. You can use BERT to provide zero shot classification predictions.

Are you sure it wouldn’t suggest such a thing? Stoic thinkers (such as the one your repo is named after) are not necessarily anti-suicide. And I think if you prompted a real stoic with the question and the right context, it would certainly suggest it without safe guards.

The public “funded” these models in the sense of contributing to their training data.

Wouldn’t it be 1/(number of days since 1888) for a given day? Or 1/(number of years) for a given year? So less than 1/100 if static and independent

I started using an AI calorie tracking app. For non-branded foods, the calorie counting is pretty inaccurate and sometimes very off. However, having an app that can track pictures and create short descriptions that someone else (my trainer) can easily review has been pretty helpful.

I'm in your boat. I tried out TikTok out a few times, including making a new account, but it never showed me good content. I had maybe one or two longer sessions, but never felt the need to go back, like I (unfortunately) do with Reddit or Youtube. I could never understand why it was so popular, but maybe I'm just a curmudgeon.

There is no thresholding, you delete edges randomly based on the probability assigned.

>It gives me hope that teenagers are watching his videos and becoming inspired to go into infrastructure.

Sadly, that was me ~10 years ago, but the lure of FAANG money was too strong and I went into EE/CS after 1 year as a civil engineering major. I wonder if one day we will really start feeling the affects of this talent reallocation, and civil engineering will become a higher paying profession.

I think they are correct, do you have a source? From my knowledge the only other components are the fully connected networks which are not big contributors.

I hardly think it's fair to say that they were 'taking advantage'. If Google says unlimited, should the typical person really expect that to be taken away? That's a really bad look for Google. "If we offer something that's good value to you, expect it to be taken away suddenly in the future." Those are not the actions of a company I would want to rely on.

I never really got how proofs are supposed to solve this issue. I think that would just move the bugs from the code into the proof definition. Your code may do what the proof says, but how do you know what the proof says is what you actually want to happen?

NP hard problems aren’t all equivalent, that’s NP complete.

I don’t think that would necessarily favor small artists. That would just favor artists who are listened to by people who don’t use Spotify a lot.

Right now someone who only streams a few songs gets a very small “vote” (assuming pay is per stream). That would make it so that everyone had the same “voting” power. But I doubt there’s much correlation between people who use Spotify less and small artists. In fact that’s probably a negative correlation if anything, and this could end up hurting local artists.

Another way to look at it is that you are now paying less for the base ticket which used to include those things. This allows consumers more choice in what part of the experience they want to pay for.

If someone wants to build a computer and they don't need a graphics card, it's better to give them the option not to get one for less money, or to get one for more money.

For those who are unaware 'engineering' in Swiss German is "Ingenieurwissenschaften"

The thing about Kelly is that it assumes risk of ruin is zero because you never bet your entire bankroll. It assumes you can always bet a fraction of a fraction of what you have remaining.

You can certainly write a program which can determine if a program would halt. It just won't be able to output a yes or no for _every_ program. In some cases it would have to output 'undecidable'. Most of the practical programs you might want to feed into this program will have an answer.

> Not every piece of code is an abstraction of course. To me, an abstraction is a piece of code that’s expressed in high-level language so that the distracting details are abstracted away. If I were to see a confusing piece of code littered with conditional logic, I wouldn’t see it and think “oh, there’s an incorrect abstraction”, I would just think, “oh, there’s a piece of crappy code”. It’s neither an abstraction nor wrong, it’s just bad code.

The wrong abstraction isn't crappy code itself. It is a reasonable looking piece of code that will force the next person into writing crappy code to accommodate it.

Edit: I think the entire project of TensorFlow is a good example of this. They built the library around a "graph" entity, and anything you did had to be shoehorned to fit that. That worked OK for some straightforward neural networks and situations for a while. As the area evolved though, it proved very burdensome. They tried to evolve it into TensorFlow 2.0 which was more forgiving, but by that point it was too late, the ecosystem became a mess. PyTorch stole the thunder because they didn't make the wrong abstraction (though I'm not sure if "duplicating" is what helped them do that)

So people polluting the atmosphere will actually have to pay for that? Oh the humanity!

> I'm new to the codebase and I just want to know how it (the codebase) works.

You should want to know _why_ it works that way too if you want to do any meaningful work with it. Context matters. I’ve seen many cases where the way something works seems dumb, only to learn later they had already tried the “smart” way but ran into some obscure problem which the “dumb” way solves.

I can’t say I know the truth, but the sentiment is that Welch divested from R&D, focused on “financialization” I.e. investing in financial instruments over adding real value, made big short term gains, and doomed the future of the company.

What is unscientific about the article?

What substantial features have they been pushing? To me it looks like just a bunch of ways to limit and monetize their existing userbase.

What precisely is your criticism of the post?