Aw. It should have been named the GNU Gneural Gnetwork, gno?
My first thought also
GNU Gneural Network
61–70 of 111 posts
Re: GNU Gneural Network
#62Earlier quoted context omitted.
Yeah they've really missed the fact that it isn't the algorithms or code that we're missing out on. Companies are usually pretty open about these because they know it isn't bit that is hard to compete on. The hard bit is the training data. Good luck collecting 10k hours of transcribed speech, or 10k recordings of "Okay Google".
Huh, I wonder how much truth there is to your words. As an outsider to Machine learning and neural networks, it seems to me that algorithms can be very valuable and big companies do not lack training data. Of course, training data is expensive and very important, but if training data were that important, their most important resource would not be machine learning scientists, but an army of do-monkeys that provide tra…
It absolutely is the case. Why do you think Google has been providing such services as Google Voice, ReCaptcha, Street View, heck... Maps... and basically everything that's awesome and free? (other than the stuff that puts advertisements in your peripheral vision)
Andrew Ng told a story about one of his first robots that was supposed to roam around the lab and collect coffee cups to deposit in the sink. He ran out of varieties of coffee cups to train the robot's vision well before the robot learned how to detect a coffee cup.
The key to being a successful AI company is to figure out how to get the world to send you data.
Edit: That or figure out how real brains work and how to scale them. Which is probably almost, but not quite, entirely unlike a convolutional neural net.
Edit2: This also leads us to a twist of the dogmatic refrain: If you're not the customer, you're the employee.
Re: GNU Gneural Network
#63Earlier quoted context omitted.
> Good luck collecting 10k hours of transcribed speech I'm sure that nearly every DVD theatrical release has subtitles available. Speech against a wide range of background noise too, e.g. music, explosions, traffic, normal ambient noise, etc. Seems a good start for acquiring a large corpus of labelled speech.
Models trained on DVD audio are considered derived works. You certainly couldn't release such a model under the GPL. You also have to solve the (very difficult) subtitle alignment problem before you could begin training.
That actually depends on if the audio is under copyright.
And even so, that is no reason why we as open source collaborators cannot create a million or billion or so samples of "Hello" in foo language as training data as a corpus for all to use.
Re: GNU Gneural Network
#64Earlier quoted context omitted.
> Good luck collecting 10k hours of transcribed speech I'm sure that nearly every DVD theatrical release has subtitles available. Speech against a wide range of background noise too, e.g. music, explosions, traffic, normal ambient noise, etc. Seems a good start for acquiring a large corpus of labelled speech.
Models trained on DVD audio are considered derived works. You certainly couldn't release such a model under the GPL. You also have to solve the (very difficult) subtitle alignment problem before you could begin training.
What about the countless audiobooks available on archive.org [1]? Sure, you may be limited to just books in the public domain, but that's still plenty of books.
Re: GNU Gneural Network
#65freebase?
Re: GNU Gneural Network
#66Earlier quoted context omitted.
> Good luck collecting 10k hours of transcribed speech I'm sure that nearly every DVD theatrical release has subtitles available. Speech against a wide range of background noise too, e.g. music, explosions, traffic, normal ambient noise, etc. Seems a good start for acquiring a large corpus of labelled speech.
Models trained on DVD audio are considered derived works. You certainly couldn't release such a model under the GPL. You also have to solve the (very difficult) subtitle alignment problem before you could begin training.
There are tons of freely available models based on copyrighted works. Are you sure this is true?
But in order to use the movies for training you would need to buy the thousand and thousands of DVD's
Re: GNU Gneural Network
#67Earlier quoted context omitted.
Yeah they've really missed the fact that it isn't the algorithms or code that we're missing out on. Companies are usually pretty open about these because they know it isn't bit that is hard to compete on. The hard bit is the training data. Good luck collecting 10k hours of transcribed speech, or 10k recordings of "Okay Google".
> Good luck collecting 10k hours of transcribed speech I'm sure that nearly every DVD theatrical release has subtitles available. Speech against a wide range of background noise too, e.g. music, explosions, traffic, normal ambient noise, etc. Seems a good start for acquiring a large corpus of labelled speech.
Aside from the potential problem with regards to copyright, it should also be noted that subtitles in general are not transcripts of dialogue. The subtitlers often have to shorten down sentences of speech so that viewers have time to read before the next couple of subtitles appear on screen.
Re: GNU Gneural Network
#68Earlier quoted context omitted.
Yeah they've really missed the fact that it isn't the algorithms or code that we're missing out on. Companies are usually pretty open about these because they know it isn't bit that is hard to compete on. The hard bit is the training data. Good luck collecting 10k hours of transcribed speech, or 10k recordings of "Okay Google".
Huh, I wonder how much truth there is to your words. As an outsider to Machine learning and neural networks, it seems to me that algorithms can be very valuable and big companies do not lack training data. Of course, training data is expensive and very important, but if training data were that important, their most important resource would not be machine learning scientists, but an army of do-monkeys that provide tra…
It's mix between who has the most data, who can pose the problem best, who can wield the largest computers and who can handle the most complex algorithms
Re: GNU Gneural Network
#69I agree with the general motivation that having too much AI research in the hands of software companies who keep it proprietary harms transparency and progress. But there is already a lot of neural-network free software, so why another package? For example, these widely used packages are free software, and seemingly more featureful: http://torch.ch/ , http://www.deeplearning.net/software/theano/ , http://pybrain.org/…
Almost all of the open source software in the area is permissive-licensed, and relies on non-free components (CUDA). To be honest, I'm not sure how Gneural plans to compete with those packages without support from CUDA or cuDNN, all of which are distinctly not open source.
The use of the gplv3 allows gnueral to have, as a dependency, any of the apache or permissive licensed tools like TF, torch, etc, and then through those tools 'export' their dependence on non-free components from nvidia and others.
I don't think this is wrong, per se, but it is ...funny when the fsf portrays their work as morally superior to us horrible corporate permissive license lovers, while inexorably depending on non-free components.
In an ideal world this project will be popular and will lead to someone on gnueral writing nvidia compatible drivers that will allow them to reject nvidia's, but I'm not optimistic. Not because of some incompetency on the Gnueral team, but nvidia's long history of making life very difficult for open driver writers.
Re: GNU Gneural Network
#70I agree with the general motivation that having too much AI research in the hands of software companies who keep it proprietary harms transparency and progress. But there is already a lot of neural-network free software, so why another package? For example, these widely used packages are free software, and seemingly more featureful: http://torch.ch/ , http://www.deeplearning.net/software/theano/ , http://pybrain.org/…
Do you know of anything besides Microsoft's GUI tool that lets someone simply specify the various attributes of the network they want, and it simply creates it, ready to be trained and tested and consuming data?