popcorncolonel

I really thought this article was going to be about the state of combinatorics in the year 2048.

I wish there were an easy way to browse the most recent papers by category. Or get an email of the daily digest of what's added today for my favorite categories.

I know arXiv does this, but it's in such an unreadable format that it makes me just want to read it on the site.

And they're from 4 days ago... What about the arXiv papers that were posted yesterday?

It's for sure different for different people.

I disagree that you need a solid founding in information theory. Almost all that I've seen about IT in ML is minimizing the KL divergence, which can be learned by browsing the wiki page.

Damn, did not expect to see this paper on here. When I read it, I wasn't thinking it could be listed under the "all-time favorite CS papers", but the connections are quite interesting and (retrospectively) intuitive.

I'll be interested to see where/(if) it gets published.

The connection between these two has always been hazy to me. I often mixed up the two when talking about each of them independently.

This article was well-written, exactly precise enough, and cleared up the confusion. Thanks for sharing!

Very simple interface, but even the cherry-picked examples in the readme are quite poor in terms of meaning/grammatical correctness. It is even using pretty state-of-the-art techniques such as LSTMs and word embeddings, but the results just aren't there.

Can someone point me to some of the best examples of coherent and long (as in many words/sentences) automatic text generation? Or an explanation as to why we're so far from being able to tackle this problem? Because I haven't seen anything noteworthy in text generation to write home about.

I started reading a book with an eerily similar title to this article[1] and it's quite a good read so far, similarly discussing symmetry, as well as the history behind the development of this theory.

[1] https://www.amazon.com/Symmetry-Monster-Greatest-Quests-Math...

Very interesting! Thanks for sharing -- I'll look more into this.

It is OpenAI, after all.

It would be pretty much impossible for the programmers to "build the behavior in" to the neural network, unless you mean training on supervised data or something.

As a ML researcher and an avid dota fan, I'm jealous! And it must have been great with Dendi the legend there too.

Icefrog also balances the game for fun, not just for the theoretical limit of what's equally fair.

Why would popularity be a detriment to this API?

Why not first allow the AI to have unlimited APM and beat humans, then restrict it later? Because I don't think we're even close to the easier problem.

I'd be interested to see if this is actually true.

And here I was thinking the exact opposite would have happened due to the Internet.

Internet?

Does Google tend to get a lot of bad programmers?

Bing rewards baby

What other operation? If we don't know what it is, how do you know it needs just one?

That's just because we know very little about them. It very well could be the case that factoring semiprimes might be ridiculously easy, but we are just too dumb right now to figure out how to do it quickly (even if P != NP -- we haven't even shown that factorization is NP complete).

No, that's what NP means. Given a witness to a solution of a problem, verifying that the solution is correct in polynomial time means the problem is in NP.

I don't really see the point in prime classification, I feel like you're never gonna get better than doing a deterministic sieve algorithm. Even if it did do quite well in terms of accuracy, probabilistic models aren't 100% correct so you still have to check for primality deterministically.

However the twin prime factorizer idea was interesting and could potentially lead to some factorization speedups (based on heuristics) if done correctly.

Either way though, prime number distribution is quite tricky - we should probably be looking in other number bases as well rather than just base 10.

The people whose parents are paying for college already have more of an advantage so they would be more likely to have an ROI from college.

If you're fine with the on-screen keyboard (which is fine for browsing, once you get used to it), flipping the keyboard so the keys are on your lap and the back rest lies on the back of the keycover is pretty good for lap usage.

And LTE.

It would be pretty damn surprising if you got any more than an incremental improvement this late in the game.

Interesting that the more laptop-y (and more expensive!) device is the more restricted one than the tablet.

And if you had an eGPU?