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Comment by iandanforth | Hacker News Reader

It's valid to say that CLA is somewhat separate from mainline research, and some of the terms are a product of that. The specific ones you refer to arn't misnomers however.

CLA - Closest 'mainline' term would be a recurrent neural network, but the neurons and network organization are very different.

OPF - This is more of a runtime environment or a set of tools to work with.

Encoders - These are actually a step below what you think of as an autoencoder. They translate many data types into useable inputs for the network. Then the spatial pooler takes over and tries to find efficient representations.

While it's possible to describe Numenta's work with existing frameworks, and unifying nomenclature is always useful, I hope you'll take the time to learn the system so that we can apply the correct shared terminology!

Full Disclosure: I used to work for Numenta.

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It would be nice if people would compare their stuff a bit more. Hinton gets a lot of attention with deep belief networks. Grossberg tried to do a lot with adaptive resonance theory. Schmidhuber has something going on with long-short--term memory. Perlovsky has yet another neural network model. All intelligent people and I love reading their work, but they should sit together and MAKE the mainstream. :-)

Just describe the basic mathematical formulations and define what is different between you and the other. I wish they would do this more and write less verbose papers.

Unfortunately the incentive structures for researchers tend to reward big claims, both on terms of results and novelty, over careful comparisons. It's always better to sell your work as a conceptual leap toward than a slight variation in a mathematical model

Are there any concise (non-book-length) tutorials or online courses about the Hawkins Way?

The most recent HTM paper is here:

https://www.groksolutions.com/technology.html#cla-whitepaper

There were some earlier ones that used to be on the Numenta website (before the name-change), but I don't see those posted now.

With the exception of being able to handle on-line learning, I have a slight preference for their previous, more abstract approach. Their current approach seems to model the details of the neural connections at a lower level than what seems necessary to me. This is not, by the way, any kind of valid scientific opinion, just the views of a widely-read amateur.

A good place to start is his lecture at Berkely : http://www.youtube.com/watch?v=qZM9JREjnp4

Then Chapter 6 of "On Intelligence" From there you should be and to tackle his whitepapers which can be found here: http://blog.mohammadzadeh.info/index.php/hierarchical-tempor...

If you are interested in generalized strong AI, this is some of the best work out there.

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