onurcel

The comments here lack so much nuance that if it wasn't HN, I would wonder if they were not made by a trolling farm.

A few years ago I created a SaaS product, and as a newcomer in the market, I was happy that (online) marketing existed so that people discovered what the advantages of my tool are compared to more established products that people already knew.

My question to you marketing-haters: what exactly are your suggestions here? Let people use only what they know? This doesn't incentivise innovation. Let an ideal unbiased search engine do the magic? One can argue that it is still marketing, but in the hands of someone else.

Here is the best PCA explanation I ever read on the web: https://stats.stackexchange.com/questions/2691/making-sense-...

that's funny. I have a project of recovering a game I wrote on Commodore 64 as a kid. It is saved on an audio tape. You can guess I have a strong motivation since it's for getting back something personal. I have the wav files ready, and regular conversion programs fail to recover it.

I have a few ideas, one of them being using a sequence to sequence NN. The intuition is that the output domain has a strong structure (C64 basic program tokens) so the decoder can learn to generate valid programs. It looks like it is going to be funnier than this game :)

thanks! yes it's a small custom script

That's really nice! I like it. I also built a minimalist ui tool a while ago [0] so I totally understand where you are coming from. Mine never really took off probably due the lack of interest of minimalism at that time, but there may be a wider audience for that approach nowadays.

Are you using it in a project of yours?

[0] http://celebio.github.io/Yama.js/

great idea! thanks for sharing this.

Recently I noticed I get better suggestions when I am logged out [0]. I am still convinced someone could implement a recommendation system that can suggest more interesting videos based on my history. And it would make me spend more time on the platform so it would be actually win-win if that could have been done by youtube but given their current priorities on monetization I don't know if there is hope. I will give that a try.

[0] https://news.ycombinator.com/item?id=25177807

Exactly. I work on Laser2, the approach is the same as Laser [0], Laser2 performs better on some low resource languages.

A more ELI5 explanation would be something like this: Laser is an encoder/decoder architecture trained as a translation task from language X to english/spanish, with the particularity of having only one vector between the encoder and decoder. Once this system is trained (with public translation datasets), the vector that the encoder gives you for an input sentence represents the "meaning" of that sentence, since the decoder relies only on that information to generate a translation. And this vector representation is the same for any language the system was trained on.

So we use that system to mine data from commoncrawl: giving any language pair (say romanian-nepali), having two vectors close to each other in that latent space means the sentences have the same meaning.

We use fastText's language classifier [1] to filter from commoncrawl, compute the vector representations with Laser, and find close vectors thanks to Faiss [2].

[0] https://engineering.fb.com/ai-research/laser-multilingual-se...

[1] https://fasttext.cc/docs/en/language-identification.html

[2] https://github.com/facebookresearch/faiss

I like the idea but I don't really get why they accept "statistically insignificant" results. What I expect from a scientific paper is to prove (even emprically) something. For example, if a paper claims something like "we show that using method X instead of Y doesn't improve the results", it won't get published on most journals... except this one, which is awesome, but the paper still has to prove that claim.

Thank you for the great article. I believe there is a typo in "we assign a value of 0 to p(x) log p(x) when x=0", it should be "when p(x) = 0".

Imagine that combined with this: https://onezero.medium.com/the-morning-paper-revisited-35b40...

awesome

The fact they put some disclaimer after bragging doesn't discredit the point of the author who says those companies brag with AI but still fail to fulfil basic queries.

The point of the OP is that they claim they understand everything. Example: https://www.blog.google/products/search/search-language-unde...

It looks nice. It would be nice to have the ability to fine-tune the model to your own source code. Is that on the roadmap?

If you want to experiment with question tag prediction on your laptop, you can also play with fastText : https://fasttext.cc/docs/en/supervised-tutorial.html

Deciding if feeding the media with fear was worth the attention you will get wasn't easy, ha. Let me tell you, you are the shame of the profession.

when the heat of "the moment" lasts 20 years, the least we can say is it's about time.

It's so unfortunate that the author didn't feel much the need of humility. He worked on some pretty cool stuff, it could have been great to present it differently.

And this is what inspired Rob Hubbard for the music of Delta game (related video https://www.youtube.com/watch?v=Jn2h6f1EO2k )

The Gompertz–Makeham law the author uses, actually allows also to modelize the accidental death rate. I don't know how hard it is to fit this additionnal parameter with real-world data though.

Is

This is awesome. Are there any video of the presentation at EC'16 ?

Making the Internet Archive’s blog faster

This kind of constant appears naturally when you approximate some computation, a famous example being Gaussian quadrature (look at x_i values depending on the "precision" you want : https://en.m.wikipedia.org/wiki/Gaussian_quadrature )

I don't know if this code is related to that but just pointing out that seemingly nonsensical constants may appear more than one would thing.

For further reading there is an excellent resource here http://web.mit.edu/15.053/www/ with a leitmotive example and both theoretical and practical approches. It also explains in detail the theory behind the real world implementation of simplex method (aka revised simplex) with inversed matrix factorization methods, etc. This is a quite old book but as far as I know even modern linear solvers use those techniques (at least open-source ones for sure).

This is a very specific and little subset of convex optimization and yet very powerful (and exciting).

Convex optimization methods can also be used in many non-convex situations, for example when you have quasi-convex or log-convex functions as objective or constraints. I recommend Boyd's excellent course https://lagunita.stanford.edu/courses/Engineering/CVX101/Win... and the online free book https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf (warning : very addictive)

Although they seem to be very interesting problems, they are merely NLP but AI problems. Quality is intrinsically subjective and what Quora needs is the community, or a very opinionated AI.

I remember seeing this kind of kits ads in magazines back then. Somehow it never appealed to me, I though they were too opinionated of what you can or cannot create with. But there is a good chance I was wrong and they may have been an excellent way to go further when you improve your skills

I've always wondered if he did this on purpose. I asked him recently but he doesn't remember, so there's a good chance that he just wanted to hack his stuff on his own :-) But as I mentioned he is a teacher, so let's say he did this unconsciously.

because he is 3 months old now :-)

Semi-conductors physics may be too much for a 9-year-old, but an introduction to transistors or doing a H bridge to reverse the rotation direction of a motor could be pretty impressive.