birdsbolt

Still, veganism is much more than just a diet and this meaning is what was meant by the vegan-friendly natural latex.

I do assume that people who have never googled "veganism" might have negative opinion of these condoms, but I guess they also won't be aware of the struggles of third world countries and won't care about the goal of this company.

Not that I don't find the fact that veganism had to be mention entertaining.

This would provide some recurrence of melodies and would sound infinitely better. Problem with these kinds of models is the window based structure without the notion of the general theme. Theme should then be added by the human, and everything else can be filled using the model.

Was there any advantage of using a deep learning model instead of something more computationally simple?

Another interesting alternative. Prettier than QR and IMO meshtag.

One interesting things besides web that people have been doing with it is writing Atom editor plugins[1].

[0]: https://github.com/ghcjs/ghcjs

[1]: http://edsko.net/2015/02/14/atom-haskell/

[2]: http://markup.rocks/

There has been some generative modelling of CNNs but I'm not sure if there's generative ability in any of the existing frameworks.

You can get compile time errors instead of runtime for bunch of things.

Apple produces the hardware and the software.

Microsoft imposed software restrictions (or lets say defaults) on hardware sold by vendors which turned out undesirable for EU. If Apple offered iOS on other phones and restricted the browser application to Safari, they would obviously get sued.

Since they "own" the hardware, and bundle it with their own software - there's nothing illegal about it.

Not that I condone of that kind of behavior, just clearing up the reasoning that was not present in the article.

Not that I was being negative about it.

I believe one of the arguments was also that these couldn't be written in statically typed languages. Although, I do not know if this turned out to be true.

What is interesting is that these transducing operations can be written quite generically - code reuse is huge.

But, usage of transducers is similar to Java 8 Streams, or Haskell stream fusion, only that the implementation is fully generic, not dependent on the container/source.

For words with similar prefixes and suffixes directed acyclic word graph is a much better option (reuses prefixes and suffixes, not just the prefix as in trie), it's a little bit slower to build but fast to traverse if done right.

Any problem where there's a lot of suffix/prefix reusage benefits from a proper trie implementation (or suffix array/tree as alternative) - ex. lempel-ziv compression.

It's good to see that efficiency of producing corn has increased enormously.

Author - at the end - proposes a very extreme version of veganism -- cultivating proteins using bacteria feeding them hydrogen, carbon dioxide and oxygen:

"Researchers for decades have been producing food conceived for astronauts on the way to Mars by cultivating hydrogenomonas on a diet of hydrogen, carbon dioxide, and a little oxygen. They make proteins that taste like hazelnut. ... A single spherical fermenter of 100 yards diameter could produce the primary food for the 30 million inhabitants of Mexico City. The foods would, of course, be formatted before arriving at the consumer. Grimacing gourmets should observe that our most sophisticated foods, such as cheese and wine, are the product of sophisticated elaboration by microorganisms of simple feedstocks such as milk and grape juice. Globally, such a food system would allow humanity to release 90 percent of the land and sea now exploited for food."

It's definitely nice to see that all that meat production is getting more and more efficient. The meat industry is still the main producer of greenhouse gases but it's really getting astoundingly efficient, producing more food by producing less greenhouse gases.

I have a ten year formal training in music - piano (never went to college), I assumed we aren't really talking about composing Rachmaninoff-like pieces. You seem to be aiming at genius-level compositions, that is, currently, unrealistic, and I was surely not talking about that.

You're also going into philosophy of quality. What is quality? Are you doubting the ability of the model trained on thousands of classical compositions to reproduce a fully structured classical piece that sounds well and has a few leitmotifs? It's very easy to constrain the model with a leitmotif positioned at several places and ask of it to find you the most probable sequence (to fill the blanks). It's very easy to take a composition, decompose it into its constituent parts (chorus, verse, etc.) train this kind of sequence to a sequence model, and then do the same for the higher level stuff.

I mean, I agree with you that rule based systems wouldn't work. But statistical models could, if used in music with as much fervor as they are used in tasks in NLP, absolutely produce regular compositions that don't sound like you're randomly spitting out the notes.

Or are you aiming at profound genius compositions? Or maybe super-pop songs? Then I agree, that would be an AI-complete problem, equivalent to machine translation and 300 page novel production.

If you use CRFs you can condition on the whole piece and learn the model like that. Yes, you'll have to use a lot of data but models can be as global as you need them to be.

If you want to use a 'verse chorus verse slightly-different-chorus' way of composing, yes, you can use a first level of a chain to generate the probable musical sequence blocks, and generate each block separately, using at the same time features generated in each part (verse, chorus, slightly-different-chorus etc.) to keep the same feeling.

If you train your model in a way described above, you can then pick a tune in your head, put it in and ask the model to generate the most probable sequence for the whole song. Or, if you're using CRFs with Gibbs sampling, you start from the complete piece and iterate until the probability it fits is large enough. Same could be done, somewhat easily, with Hidden Markov Models (I just realised that Markov models might not be the thing I was referring to in the post above, I was talking about statistical variant of Markov chain).

Convolutional neural networks could do the same thing, probably even better than CRFs and HMMs. Music isn't more complex than language and people have been using these sequence modelling methods to do extraordinary things in natural language processing.

Oh, nostalgia is hitting me hard :D

Or, if you want to get even better performance, you can use conditional random fields with exactly the same global features, one has an advantage of not needing so much data because the distribution being modelled isn't a joint one + CRFs are excellent with custom features (features are observed variables and their distribution is implicitly present in the conditional distribution). Disadvantage would be that you couldn't as easily generate the sequence of notes (chords) because the model isn't generative (unlike Markov chains), but one can use Gibbs sampling (combined with CRFs) to search over the space of probable sequences.

Or, some nicely trained convnets could get you even closer to the brain of the composer :D