That was my main question. How does this compare (either experimentally or theoretically) to already well established approaches.

No references in their paper.

The smells like Wolfram's "New Kind of Science" type work. And they are welcome to prove me wrong, I like AI topics. But it is isolated from other peer work, when AI field is not new. So kind of suspicious.

I've studied Numenta's CLA's, previously HTM's, as well as established ML approaches fairly in-depth over the past couple of years. Hawkins definitely takes a new approach to neural networks and focuses more on modeling the brain's biology rather than building on known artificial neural network concepts. HTM's did incorporate bayesian nets, but have been abandoned, probably with the departure of Dileep George, who now runs http://vicarious.com/.

However, Hawkins work is similar to Deep Neural Nets in that both are unsupervised neural nets (unlike perceptrons and back-prop) and both aim to represent data as sparse representations. Also, here's a similar question I answered on Quora: http://www.quora.com/Machine-Learning/Does-Numenta-build-on-...

In addition, Numenta does use CLA's to solve real problems for customers: https://www.groksolutions.com/solutions.html

Finally, Hawkins' book, On Intelligence, has at least inspired researchers like Stanford's Andrew Ng at a philosophical level which he talks about IIRC in his online ML course. https://class.coursera.org/ml/class

Here's an independent review of the older version of the algorithm: http://bias.csr.unibo.it/maltoni/HTM_TR_v1.0.pdf

"Systematic experiments on three line-drawing datasets have been carried out to better understand HTM peculiarities and to extensively compare it against other well-know pattern recognition approaches. Our results prove the effectiveness of the new algorithms introduced and that HTM, even if still in its infancy, compares favorably with other existing technologies."

Basically none. Numenta has yet to do anything that has impressed any researchers I know. Perhaps someday they will, but I am not counting on it.

NuPIC was released as free software, in part, because there has been a strong interest in the academic research community. I encourage you to check out the mailing list (http://lists.numenta.org/mailman/listinfo/nupic_lists.nument...) for some recent examples.

Generally, the most relevant academic community would be the NIPS community and I have not noticed any Numenta papers at NIPS, but please point me towards any I have missed if you are aware of any. I expect a lot of people have an opinion along these lines: http://developers.slashdot.org/comments.pl?sid=225476&cid=18... Don't get me wrong, I would love to see numenta produce something of value for the ML community, but it doesn't look good so far.

Have those researchers released any source code?

Yes, plenty. Releasing source code is quite common in the ML community.

I'm a neuroscientist and I'm also not clear on exactly what the anticipated use case for this is. The connection to principles in neuroscience is somewhat loose, it appears to be more grounded in machine learning.