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But 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 monopole slows down Progress and Technology.

The GNU project should do a bit more background research before starting new projects. Here are some links to open source deep learning tools. These are the same tools and libraries used by those "money driven companies" in their projects, including AlphaGo:

Caffe, widely used C++ deep learning framework.

Theano, widely used Python deep learning framework.

Torch, the software used by Google, AlphaGo and Facebook.

TensorFlow, Google's large scale machine learning framework.

CNTK, Microsoft's deep learning toolkit.

The idea that "the fact that only companies and labs have access to this technology can represent a threat" is patently absurd. Theano, Caffe and Torch are all open source and even Google has open-sourced its Tensor Flow platform which makes it easy to build new tools and run then, fast, on all the GPUs you can find. If you need to do this at scale and you're not the size of Google or IBM you can use Amazon's Machine Learning for AWS. There are many, many higher level toolkits out there that are available under licenses that are much less restrictive than GPLv3.

"However, the GNU project sees that the fact that only companies and labs have access to this technology can represent a threat"
That is not a fact at all. There are tons of open source neural network libraries and tools and even tons of open source neural network libraries that provide recurrent network and deep learning features. Just a 30 second search gives me this list:
http://deeplearning.net/softwa...

"a very simple feedforward network which can learn very simple tasks such as curve fitting"
This is NN101 stuff and I'm sure hundreds if not thousands of college students have made something similar.

Nothing to see here. Move along.

The most powerful chips out there are still far below the capacity of a human brain.

I don't want just to play games, I want to retire and leave my computer to do my work for me.

At this point, we already have better software models for the brain than hardware to run it.

There's probably no need to reinvent the wheel. A number of high-level api's are available for this purpose.
OpenCV does image processing and has GPU support.

A more general tool is Theano which is a meta-programming tool. You state your computations symbolically and theano generates a computation graph. The graph gets simplified and the theano generates cpu/gpu code for your equations.

--Beau

Hey, we're definitely thinking about this! In fact, the Java version can run on a GPU. And we're in the process of making a fast Python version based on theano. Unfortunately, even with all of these speedups, we're still talking about lots of neurons and lots of computation.

However, there are plenty of smaller scale models that you can run in Nengo to get a sense of what's going on in the larger Spaun model! The tutorials are a good place to start.

A lot of vague marketing-speak in this article. "Deep learning"? The article basically talks about neural networks, just one of the techniques in machine learning. Neural networks were hyped for a long time, perhaps because of the catchy name.

You could have answered your own questions with a quick search, rather than assume that that which you are ignorant about is mere "marketing-speak."

deeplearning.net

Deep learning (Wikipedia)

Unsupervised Feature Learning and Deep Learning

Its definitely a subset of AI, but if you are interested in Machine Learning then you should check out the Deep Learning Tutorials. They cover most of the building blocks of "Deep Learning", which you can think of as the new wave of Artificial Neural Networks. The tutorials include complete theoretical (and mathematical) descriptions of the model, as well as Python/Theano implementations. Pre-requisites would be a good math background (first year calculus should suffice), basic probability theory and coding in Python/numpy. You can learn Theano as you go along.