GNU Project Introduces Gneural Network AI Package (gnu.org)

jones_supa writes: The GNU free software project is introducing a new neural network computation package called Gneural Network. The GNU project has been impressed by the work of Google, IBM, AlphaGo and Watson on the field of artificial intelligence. However, the GNU project sees that the fact that only companies and labs have access to this technology can represent a threat: "First of all, we cannot know how money driven companies are going to use this novel technology. Second, this monopoly slows down Progress and Technology." This is why the author, Jean Michel Sellier, decided to create Gneural Network and release it under the GNU GPL license. In the current release (version number humbly set to 0.0.1), it is a very simple feedforward network which can learn very simple tasks such as curve fitting, but the development team plans to deliver more advanced features very soon. They are already spending efforts to implement a network of LSTM (long short term memory) neurons for recurrent networks and deep learning. Learning reinforcement techniques are also planned.

Comments

You're not making sense

[Anonymous Coward]

Until the BSD guys pick up on doing a project like this, I won't care.

The only major difference between BSD and GPL licenses is that BSD allows open software to be closed, so really you're arguing in favour of closed software. Whatever are you doing on Slashdot?

But the illogic of your position runs deeper still. The whole point of TFA and of Gneural is to provide an open neural net because closed ones are already plentiful , so the only perceived "benefit" of BSD (using the term loosely) is precisely what Gneural is trying to balance. This makes your desire for BSD licensing so that even more proprietary software can be made totally miss the point of the project.

Re:You're not making sense

[Megol]

Until the BSD guys pick up on doing a project like this, I won't care.

The only major difference between BSD and GPL licenses is that BSD allows open software to be closed, so really you're arguing in favour of closed software. Whatever are you doing on Slashdot?

Implying one have to be a GPL fanatic to see any value of /.? That's of course bullshit bordering on trolling.

But you are also wrong about the BSD licence, it doesn't allow open software to be closed - it allows open software to be _forked_. That property it shares with the GPL where the copyright owners can fork their code to be licensed however they want. So the difference is who can fork the code, all (BSD) or the copyright owners (GPL). So which is more free?

But the illogic of your position runs deeper still. The whole point of TFA and of Gneural is to provide an open neural net because closed ones are already plentiful , so the only perceived "benefit" of BSD (using the term loosely) is precisely what Gneural is trying to balance. This makes your desire for BSD licensing so that even more proprietary software can be made totally miss the point of the project.

And you plainly doesn't understand the BSD licence thus missing the point of the post.

Re:You're not making sense

[Wycliffe]

So paraphrasing:

BSD license = you are free to do with the software what you want.
GPL license = we are going to dictate what you may do and what you may not, which products it is acceptable to build, etc.

Sounds like one of those is much freer than the other...

BSD is freer in the first generation sense but when someone takes BSD and expands on it (for example MacOS), other people are then cut off from those added improvement. It would be like a free public library where no one was required to return the books. Yes, the books are more free for the first person that checks them out but if the first person puts them on their shelf at home, those books are now a lot less free for future users. In this case, they are obviously worried that as machine learning becomes more and more important they don't want the market cornered by a handful of commercial companies negating the last 20 years of their progress. If instead of paying a "microsoft tax" to use windows everyone has to start paying a "google tax" for the machine learning to make their computer usable then we are back where we have started from.

Re:You're not making sense

[ceoyoyo]

Except that the original project still exists. The BSD license is like an electronic library where you can check out the books for free. You don't get access to my margin notes unless I choose to return my copy of the book, but the original is still there and you can read it and make your own notes as you please.

Re:Vapour?

[jbolden]

NN have been around for 40 years. Lots of people have built stuff already.

Mainly FSF is a political organization not a software shop. They did a lot of good work, and they failed on some projects. Lots of top quality people couldn't keep up with the Linux kernel no embarrassment in that. The person who gets the bronze in the olympics is not a failure.

Re:An open source NN library. How novel!

[Dutch Gun]

Yep, nothing novel at all, even in conception. The real trick with deep learning software is figuring out how to integrate them into knowledge bases and provide useful training and feedback mechanisms. Honestly, creating the neural network software is the easy part, because there's a ton of academic research that tells you *exactly* how to do it. I actually downloaded and examined the code, and while it looks reasonably clean and functional, we're not exactly talking about a huge amount of work to replicate it - it's just a few hundred lines in total.

Moreover, when any software package starts with a section on "The Ethical Motivations" for its existence, it strikes me as the wrong sort of motivation altogether. The real motivation should be "I want to solve some interesting problems", and THAT will drive the design. This sounds like a pretty typical academic exercise, and as such, probably is not going to amount to much, other than as a starting point for some student projects here or there. But even that is dicey, as naturally, there's no documentation at all - just a readme file telling you to look at the source code to figure out how it works. Odds are pretty good that documentation is never actually written for it, because that's a hell of a lot less fun than writing the code.

Sorry, I really do love this sort of stuff, but it's a little hard to get excited about the project when its exactly the same sort of code I was tinkering with as an undergrad student decades ago. That anyone is actually comparing a few hundred lines of relatively simplistic C code to IBM or Google's machine learning projects is disingenuous at best, borderline insulting at worst.