+1 on the twothumbsup! That's an excellent guide to swarming, thanks Ron.
The video brings up a couple of thoughts. First, it seems that the plan for Grok is to build up a portfolio of concrete applications of the technology to real use cases, and to generate revenue from those uses in industry and commerce. The instances of these applications are in-situ, working bits of CLA whose parameters are optimised for just those use cases. Thus, swarming is a part of an industrial process which manufactures an installation for a customer. This is great for a number of reasons. First, Grok gets some return on its large investment in developing the theory and technology, and also allows this community to get access to the technology via NuPIC. Second, Grok is gathering a portfolio of institutional knowledge which informs both the business and the research. On the other hand, the technique of swarming is a business artefact in the context of the theory of neocortical function, as it bypasses the thorny question of how we configure our brains to process incoming data. Swarming is clearly an artificial genetic algorithm (or set of algorithms) for choosing models based on performance relative to a portion of the data. The natural process similarly has a genetic component which mirrors this in a way, but it also contains an on-line improvement algorithm which fine-tunes models over the entire lifespan of the organism. For example, Pinker's Language Instinct (a must-read, by the way) describes how we can perceive all possible phonemes (ie from all known languages) in early infancy, but then, over just a few months, we lose the ability to even hear some phonemes if they are not contained in our mother tongue. The classic example is Japanese speakers losing the ability to hear "l" and "r" as different sounds. We do this both by noting which phonemes we actually hear, and also by connecting those phonemes with our developing motor control over the larynx, tongue, and lips as we learn to replicate the speech we hear. Pinker's understanding is that this (among many other similar phase-changes) is a genetically-triggered event which culls unused functionality and reallocates the recycled resources for some new linguistic purpose. In terms of swarming, it's like repeating the swarming process after a set amount of real data, and reconfiguring the models based on actual experience. In our discussions of adaptive encoders, I was positing that the encoders could be self-improving, in that they perform like a single particle in the swarm to migrate the encoding in the direction of better global performance of the overlying region. The same could be the basis for inter-region encoding and connection. Anyway, thanks again Ron. It's always great to see a detailed exposition of concrete, working systems - it provides a basis for further thinking on the biological as well as the algorithmic possibilities. Regards, Fergal Byrne On Sun, Sep 8, 2013 at 10:34 PM, Marek Otahal <[email protected]> wrote: > Yay!! (twothumbsup) > just watching... > PS: Maybe later post the slides Ron's showing? > > Thanks for the edu-vid Ron! > > > On Sun, Sep 8, 2013 at 11:26 PM, Matthew Taylor <[email protected]> wrote: > >> Swarming in NuPIC, with Grok engineer Ron Marrianetti: >> >> http://youtu.be/xYPKjKQ4YZ0 >> >> Enjoy! >> >> Matt >> >> _______________________________________________ >> nupic mailing list >> [email protected] >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >> > > > > -- > Marek Otahal :o) > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > > -- Fergal Byrne ExamSupport/StudyHub [email protected] http://www.examsupport.ie Dublin in Bits [email protected] http://www.inbits.com +353 83 4214179 Formerly of Adnet [email protected] http://www.adnet.ie
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