Hi Neal, I think I understand your frustration. I hope my perspective will help this conversation a bit and help you and others decide whether NuPIC and the CLA are worth your time.
The NuPIC open source project and the HTM/CLA technology are unique. You should care about NuPIC if the following two criteria apply to you. 1) You are interested in Machine Intelligence 2) You believe the best way to achieve machine intelligence is by employing the principles used by the neocortex Our goal is to make intelligent machines, machines that learn, that interact with their world, and have goals. Machine intelligence and machine learning are not the same thing. There are many problems that can be solved well with existing machine learning techniques and new ones are being created all the time. But most machine learning techniques are not on a path to machine intelligence. They are derived to solve particular problems and any technique that solves the problem or does best on a benchmark is good. Machine intelligence is about building universal learning machines that can solve almost any problem via learning and behavior. I measure our progress in machine intelligence not just by what the CLA can do today but by how many of the learning/behavior principles in the neocortex we understand and employ. It is obvious to me that we must understand how the neocortex works to build intelligent machines. However this is not a majority view. I constantly run into researchers who dismiss the need to understand brains or say it is impossible. In fact I believe the biggest obstacle to achieving machine intelligence is getting more people to embrace brain theory as the path forward. I wrote On Intelligence to make this case; that a) we can understand how the neocortex works and b) this is the path to intelligent machines. The good news is this view is gaining momentum. DARPA, iARPA, IBM, and others are funding projects based largely on the arguments I have put forward and the progress we have made so far with the CLA. It is too early to say we have succeeded but we are definitely making progress getting people to embrace brain theory as a necessary component of a program to build intelligent machines. The CLA is the best, and arguably the only, example of a learning methodology based on cortical principles that fits within a framework for machine intelligence. Of course I too would like to have more success stories today. When we started Numenta I thought we could be where we are today in four years. It has been close to nine. Still, I am thrilled with our progress. The technology and theory are progressing and from my vantage point I see acceleration. This past month I made good theoretical progress on temporal pooling and vision invariance (I will write this up soon). Progress in theory doesnt translate quickly into products but I know it will eventually so it is exciting. Still, it is important to demonstrate progress on real world problems too. Today, Grok is the primary example of the CLA enabling a cool new product. We hope to make a big business based on Grok but that will take time so we cant yet point to it as a success. Another exciting application I see for the CLA is based on CEPTs work in word SDRs. I was blown away by Subutai Ahmads hackathon hack feeding three word sentences into the CLA. (There were other cool demos but this one struck me as the one with the most immediate business potential.) Subutais hack <http://numenta.org/blog/2013/11/06/2013-fall-hackathon-outcome.html#fox> looked simple but what was happening under the covers was impressive. There are several great businesses lurking there. But as always, it takes time, money, and foresight to seize those business opportunities. I dont know if anyone in the NuPIC community has what it takes to do that, but CEPTs work and the CLA has enabled fundamentally new possibilities in language understanding. There are no doubt other applications NuPICers are working on that we dont know about. Again, achieving business success with the CLA is important but it has to play a supporting role in the overall mission. Your point about the code not exactly matching the theory is understandable. Implementing the theory in SW is challenging. We constantly struggle with how to make the code work in real time within the constraints of our computing environments and how to test it to know that it works, etc. All I can say is doing significant things is hard. The first computers were far from Turing's "universal machine". In summary, NuPIC isnt for everyone. The CLA isnt the most amazing machine learning method ever invented and we have many theoretical problems yet to solve. But the CLA is the best learning model that fits within a framework for biological and machine intelligence and even today it has many unique capabilities. If you subscribe to the two principles above I dont know of a better place to be. Jeff From: nupic [mailto:[email protected]] On Behalf Of Pedro Tabacof Sent: Friday, January 10, 2014 4:24 AM To: NuPIC general mailing list. Subject: Re: [nupic-discuss] Any results, anywhere? 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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