Hello Neal, I understand your frustration. With so much buzz around deep learning, big data, etc, on the mainstream machine learning community, it's hard to work with such a different approach. I see this a real scientific research, and there is always big risks with projects as such, but if we succeed it will be a real AI progress. Note also that there are many interesting brain features that are yet to be incorporated into the CLA (such as motor action, hierarchy, feedback).
I don't know if you read this thread, but I applied the CLA to a real-world dataset that was used on a serious competition, achieving an error that would put me around the third place (note that it's a somewhat unfair comparison since I had the test data at my disposal): http://comments.gmane.org/gmane.comp.ai.nupic/1047 Considering it was my first time using the new NuPIC and I didn't spend a lot of time on it, I consider it to be a good, even surprising, result. I believe we will see much more interesting applications later on, we either have to be patient or actually work on them. This NuPIC version is really new, I even had difficulties getting it running, so we're not talking about a mature technology (even though the algorithm is a few years old), but rather a very young one. Pedro. On Fri, Jan 10, 2014 at 4:52 AM, Ian Danforth <[email protected]>wrote: > Neal, > > Which application domain are you most interested in? Remember that NuPIC > has been used almost exclusively internally by Grok/Numenta until very > recently, and so on a limited set of tasks around prediction and anomaly > detection the technology are much more mature. In most other areas there > haven't been more than tech demos implemented. The kind of extensive use, > methodology and rigor it sounds like you're looking for probably won't > happen until the code base is a bit cleaner and easier to use. It would be > useful to know what you expected NuPIC to be 'doing' so the community can > provide entry points and demonstrations that serve the audience you > represent. > > Ian > > > On Thu, Jan 9, 2014 at 7:50 PM, Neal Donnelly <[email protected]> wrote: > >> Hey everyone - >> >> I got involved with NuPIC with starry eyes after reading the CLA >> whitepaper. The abstractions that it describes are exciting and the level >> of work that was obvious in the codebase were very compelling. I was mildly >> concerned that there were no results presented, but I was told that was >> because there are no established metrics for measuring its performance >> since it's so novel. >> >> Now, after mucking around in the network engine codebase for a while, >> I've realized that the abstractions presented in the CLA whitepaper seem to >> have little bearing on the implementation. Spatial and temporal pooling are >> accomplished as separate types of regions, which are composed not of cells >> but of Nodes, which are themselves comprised of elements...? >> >> This confusion has left me wondering why I believe in this project if >> neither theory nor results back up the implementation. This line of >> thinking has left me frustrated that I can't find a single result of NuPIC >> actually *doing* anything. When none of the existing benchmarks are >> fitting, researchers invent a new one. I realize I've seen no learning >> curves, no applications to real data, no demonstrations of performance of >> any kind. >> >> My hope is that this will provoke a flood of links and papers that I >> missed. My fear is that I've been terribly naive to assume that NuPIC would >> work when there aren't results out front and center. >> >> Thanks. >> Neal Donnelly >> >> _______________________________________________ >> nupic mailing list >> [email protected] >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >> >> > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > > -- Pedro Tabacof, Unicamp - Eng. de Computação 08.
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