Hi Matt, Thanks for getting those up in such good order. Well done Chetan, that looks even more inventive than the RDSE!
The discussions with Jeff and other Numenta staffers is really intriguing, and I think that there are some really important implications for both NuPIC and HTM in general from this step forward. I'm writing up my ideas on this and will post a link once that's done. A quick teaser: 1. This is a very HTM-compatible idea. You could view each square's "order" as a normalised "activation potential" and the process of choosing the top w squares as analogous to local inhibition among neurons (with all virtual neurons outside the search square forced to zero). So, the square forms a "virtual SDR" which is expanded into a full SDR by the fact that it projects randomly into the larger bit field of the encoder. 2. The general "coordinate" paradigm operates on the same principle, in that it populates an n-dimensional space with a grid of hash-based (pseudorandom) "signature neurons" which can be sampled using this local inhibition scheme to derive a location fingerprint algorithm. This seems to me to be a genuine invention in the field of high-performance semantic encoding of high-dimensional spatial position (which is either treated as a total mystery or abstracted away using fragile tricks). 3. It is clear how you could combine this encoding scheme with data which varies by location, to create a richer idea of "order" in feeding the SDR generation algorithm. For example, you could combine random "order" with altitude or temperature data to choose the top w squares. 4. This is a wonderful model for how we "zoom in" or "zoom out" and perceive a continuously but smoothly varying model of the world. It also models how we can perceive gracefully degrading levels of detail depending on how much time or attention we pay for a perception. In this case, the "encoder" detailed here would be a subcortical structure or a thalamus-gated (attention controlled) input or relay between regions. 5. This method solves the presentation-order dependence issue of the original RDSE, where the encoding of a value depends on which values were presented before it, unless you precalculated values from an initial "centre" (which could have significant memory or compute costs). The coordinate encoder has a deterministic, O(1), order-independent algorithm for computing both "order" and bit choice. The only issue I see is that the pseudorandom number is Python-specific, and so a C++ encoder (or a Java/Clojure encoder) will likely produce completely different answers (but see next point). 6. I believe it would be worth exploring using Perlin noise (or some similar cross-platform pseudorandom generator) to generate the order and bit choice values. This would give you a) identical encodings across platforms, b) pseudorandom, uncorrelated values when the noise samples are far enough apart (eg when the inputs are integers as in this case), and c) smoothly changing values if you use very small step sizes. Just one point about changing radius and its effect on the encoding. I'm almost certain (but will have some tests to prove it one way or the other) that the SDR is very robust to changes in radius, due to the sparsity of the SDRs. In other words, the overlap in an SDR at radius r with that at radius r' (at the same GPS position) will be high, because you are only adding or removing an annulus around the same position (this will be similar to adding or removing a strip of squares when a small position change occurs). Regards, Fergal Byrne On Fri, Aug 8, 2014 at 2:52 AM, Matthew Taylor <[email protected]> wrote: > In this video, Numenta engineer Chetan Surpur explains NuPIC's > Geospatial Coordinate Encoder, which is used to generate predictions > and anomaly indications from temporal geospatial data (like you get > from GPS). > > https://www.youtube.com/watch?v=KxxHo-FtKRo > > This video describes the NuPIC Geospatial Tracking application and > shows how it can be used to analyze GPS data. > > https://www.youtube.com/watch?v=M4dD9wCQLkA > --------- > Matt Taylor > OS Community Flag-Bearer > Numenta > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > -- Fergal Byrne, Brenter IT Author, Real Machine Intelligence with Clortex and NuPIC https://leanpub.com/realsmartmachines Speaking on Clortex and HTM/CLA at euroClojure Krakow, June 2014: http://euroclojure.com/2014/ and at LambdaJam Chicago, July 2014: http://www.lambdajam.com http://inbits.com - Better Living through Thoughtful Technology http://ie.linkedin.com/in/fergbyrne/ - https://github.com/fergalbyrne e:[email protected] t:+353 83 4214179 Join the quest for Machine Intelligence at http://numenta.org Formerly of Adnet [email protected] http://www.adnet.ie
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