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I 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.

I 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://torch.ch/, http://www.deeplearning.net/software/theano/ http://www.deeplearning.net/software/theano/, http://pybrain.org/ http://pybrain.org/, https://www.tensorflow.org/ https://www.tensorflow.org/, http://leenissen.dk/fann/wp/ http://leenissen.dk/fann/wp/, http://chainer.org/ http://chainer.org/

simondedalus 11y ago
I think the idea is to use awareness of GNU and also to focus the attention of people with the skill to contribute on neural-networks. What I mean is, you're right of course, there's much better neural-network free software already available, but GNU endorsing an official package 1) could get people whose concerns are more strongly geared toward free software ethics to start paying attention to neural networks, and 2) get people, regardless of their ideological commitment to anything, to be more aware both of the ethical issues and of neural-network software--just in virtue of GNU's mild fame. and of course, unless the maintainer of this package makes weird choices and alienates other projects, there's the other benefit of projects learning from each other and poaching code and strategies for the greater good.
- capitalsigma 11y ago
  Right. I don't think this is meant to be immediately competitive against other, more mature neural net implementations. It's just GNU getting some skin in the game and giving people another option to use/contribute to. I think this might be a fun project to submit some changes to, since there's probably plenty of easy problems left to solve with such a new implementation.
semisight 11y ago
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.
- albertzeyer 11y ago
  Wrong, most do support OpenCL or at least have partial support. It's just much less supported because not so much people see too much benefit from it. Btw, it's all open source. If you miss some functionality, it's really easy to add.
- mindcrime 11y ago
  and relies on non-free components (CUDA). Yeah, this is one reason I'm really hoping some of the stuff AMD is pushing, in regards to openness around GPUs, gains traction. And why I am hoping OpenCL continues to improve so that it can be a viable option. Being dependent on nVidia for all time would blow.
- spdustin 11y ago
  I'd really like to understand the reasons behind the focus on CUDA and not OpenCL. My understanding is that nVidia and AMD made sure their hardware and software would make the GPU accessible for non-graphics tasks, but AMD's version is not functionally or legally locked to their hardware. Why hasn't OpenCL taken off and run on nVidia hardware? It seems like there must be more at play, but I'll admit a lack of insight and imagination on this one.
  - revelation 11y ago
    Because it requires nVidias cooperation in implementing OpenCL. And of course they are not about to do so in a useful manner when they are leading with CUDA. Also, the premise of OpenCL is somewhat faulty. You end up optimizing for particular architectures regardless.
  - danieldk 11y ago
    It seems like there must be more at play, but I'll admit a lack of insight and imagination on this one. I think the reasons are twofold: 1. CUDA had a big headstart over OpenCL. 2. NVIDIA has invested a lot in great libraries for scientific computing. E.g. for neural nets, they have made a library of primitives on top of CUDA for neural nets (cuDNN), which has been adopted by all the major packages.
  - 11y ago
deleted 11y ago
[deleted]
mindcrime 11y ago
I 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? Not only is there a lot out there, a lot of it was released by companies like IBM[1], Google[2], Yahoo[3], Baidu[4], Microsoft[5], etc. So while I'm generally sympathetic to the FSF's position, this case almost seems like a bit of a reversal of things: there doesn't seem to be a problem with for-profit companies taking the fruits of the labors of volunteers and building on top of it... instead, we have a surplus of riches, released as Open Source by a bunch of big companies. It just happens that most of it is under a permissive license like the ALv2. Of course, one could suggest that that state of affairs isn't natural and/or sustainable, and that this doesn't negate the issues the FSF is dedicated to. So I support this effort, even if it seems redundant on some level at the moment. [1]: http://systemml.incubator.apache.org http://systemml.incubator.apache.org [2]: http://tensorflow.org http://tensorflow.org [3]: http://yahoohadoop.tumblr.com/post/139916563586/caffeonspark-open-sourced-for-distributed-deep http://yahoohadoop.tumblr.com/post/139916563586/caffeonspark... [4]: https://github.com/baidu-research/warp-ctc https://github.com/baidu-research/warp-ctc [5]: https://github.com/Microsoft/CNTK https://github.com/Microsoft/CNTK
- IshKebab 11y ago
  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".
  - RealityVoid 11y ago
    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 training data. It won't be the victory of the smartest scientist but of the one with the most employees. And that does not seem to be the case.
DigitalJack 11y ago
Not only that, but GNU has deliberately obfuscated code (GCC) to prevent people from using it in ways GNU doesn't approve (but are allowed by license).
pmarreck 11y ago
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?
- argonaut 11y ago
  For image data, Caffe (http://caffe.berkeleyvision.org/ http://caffe.berkeleyvision.org/) is dominant (most deep learning computer vision research I see is done with Caffe). However, that's the chief problem with Caffe - it's very difficult to extend because it revolves so much around "just specify attributes" and train.

I 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://torch.ch/, http://www.deeplearning.net/software/theano/ http://www.deeplearning.net/software/theano/, http://pybrain.org/ http://pybrain.org/, https://www.tensorflow.org/ https://www.tensorflow.org/, http://leenissen.dk/fann/wp/ http://leenissen.dk/fann/wp/, http://chainer.org/ http://chainer.org/

simondedalus 11y ago

I think the idea is to use awareness of GNU and also to focus the attention of people with the skill to contribute on neural-networks. What I mean is, you're right of course, there's much better neural-network free software already available, but GNU endorsing an official package 1) could get people whose concerns are more strongly geared toward free software ethics to start paying attention to neural networks, and 2) get people, regardless of their ideological commitment to anything, to be more aware both of the ethical issues and of neural-network software--just in virtue of GNU's mild fame. and of course, unless the maintainer of this package makes weird choices and alienates other projects, there's the other benefit of projects learning from each other and poaching code and strategies for the greater good.

capitalsigma 11y ago
Right. I don't think this is meant to be immediately competitive against other, more mature neural net implementations. It's just GNU getting some skin in the game and giving people another option to use/contribute to. I think this might be a fun project to submit some changes to, since there's probably plenty of easy problems left to solve with such a new implementation.

semisight 11y ago

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.

albertzeyer 11y ago
Wrong, most do support OpenCL or at least have partial support. It's just much less supported because not so much people see too much benefit from it. Btw, it's all open source. If you miss some functionality, it's really easy to add.
mindcrime 11y ago
and relies on non-free components (CUDA). Yeah, this is one reason I'm really hoping some of the stuff AMD is pushing, in regards to openness around GPUs, gains traction. And why I am hoping OpenCL continues to improve so that it can be a viable option. Being dependent on nVidia for all time would blow.
spdustin 11y ago
I'd really like to understand the reasons behind the focus on CUDA and not OpenCL. My understanding is that nVidia and AMD made sure their hardware and software would make the GPU accessible for non-graphics tasks, but AMD's version is not functionally or legally locked to their hardware. Why hasn't OpenCL taken off and run on nVidia hardware? It seems like there must be more at play, but I'll admit a lack of insight and imagination on this one.
- revelation 11y ago
  Because it requires nVidias cooperation in implementing OpenCL. And of course they are not about to do so in a useful manner when they are leading with CUDA. Also, the premise of OpenCL is somewhat faulty. You end up optimizing for particular architectures regardless.
- danieldk 11y ago
  It seems like there must be more at play, but I'll admit a lack of insight and imagination on this one. I think the reasons are twofold: 1. CUDA had a big headstart over OpenCL. 2. NVIDIA has invested a lot in great libraries for scientific computing. E.g. for neural nets, they have made a library of primitives on top of CUDA for neural nets (cuDNN), which has been adopted by all the major packages.
- 11y ago

deleted 11y ago

[deleted]

mindcrime 11y ago

I 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? Not only is there a lot out there, a lot of it was released by companies like IBM[1], Google[2], Yahoo[3], Baidu[4], Microsoft[5], etc. So while I'm generally sympathetic to the FSF's position, this case almost seems like a bit of a reversal of things: there doesn't seem to be a problem with for-profit companies taking the fruits of the labors of volunteers and building on top of it... instead, we have a surplus of riches, released as Open Source by a bunch of big companies. It just happens that most of it is under a permissive license like the ALv2. Of course, one could suggest that that state of affairs isn't natural and/or sustainable, and that this doesn't negate the issues the FSF is dedicated to. So I support this effort, even if it seems redundant on some level at the moment. [1]: http://systemml.incubator.apache.org http://systemml.incubator.apache.org [2]: http://tensorflow.org http://tensorflow.org [3]: http://yahoohadoop.tumblr.com/post/139916563586/caffeonspark-open-sourced-for-distributed-deep http://yahoohadoop.tumblr.com/post/139916563586/caffeonspark... [4]: https://github.com/baidu-research/warp-ctc https://github.com/baidu-research/warp-ctc [5]: https://github.com/Microsoft/CNTK https://github.com/Microsoft/CNTK

IshKebab 11y ago
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".
- RealityVoid 11y ago
  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 training data. It won't be the victory of the smartest scientist but of the one with the most employees. And that does not seem to be the case.

DigitalJack 11y ago

Not only that, but GNU has deliberately obfuscated code (GCC) to prevent people from using it in ways GNU doesn't approve (but are allowed by license).

pmarreck 11y ago

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?

argonaut 11y ago
For image data, Caffe (http://caffe.berkeleyvision.org/ http://caffe.berkeleyvision.org/) is dominant (most deep learning computer vision research I see is done with Caffe). However, that's the chief problem with Caffe - it's very difficult to extend because it revolves so much around "just specify attributes" and train.