Bert, The SDRs coming out of CEPT are already semantically encoded using their proprietary algorithms [1]. Those encodings do not include POS. The sequence memory inside NuPIC will learn sequences between words based on those SDRs, but since they don't include POS or syntactic information, I doubt it can learn anything about syntax. But because the CEPT SDRs have other things encoded within them, you could feed Fluent <animal> <vegetable> <mineral> over and over and it will start predicting SDRs that match the types of SDR patterns it's seen. This hackathon demo [2] from last year might help explain.
[1] http://www.youtube.com/watch?v=hjMjhhmYKhI [2] http://www.youtube.com/watch?v=X4XjYXFRIAQ#t=3240 (54 minutes in) --------- Matt Taylor OS Community Flag-Bearer Numenta On Fri, Mar 7, 2014 at 5:21 AM, Bert Frederiks <[email protected]> wrote: >>> The word SDRs in CEPT that Fluent is using have no concept of part of >>> speech, so I doubt you would get the right types of words in the right > > > Trying to understand what you mean by this... Don't the SDRs automatically > become part of (hopefully) something language-like inside Fluents' neural > network? In other words... they should become part of speech/language by > using them in speech, not (here that is through > feeding it books)? Call this ("social") process structuration. > > >>> places. I have done some experiments with parts of speech tagging >>> using the POS tags in NLTK as categories for NuPIC [1], and it does >>> pretty well at guessing what POS is coming next in a sentence, but >>> this is a very hard problem that can't be done by most humans well >>> either, because of the possibility of so many branches in human >>> speech. > > > I do not mean Fluent should be able to tag. I am interested in how many > hierarchical neural levels are needed to get a syntactically correct output, > even though the content may be absurd, like: "I was going to the ball and > the ball rolled down the stairs walking to the moon." > > If you can make this then yo have, I think, one prerequisite for speech, and > maybe this would not be the most difficult. Linguists now think we have > syntactical rules in our heads. It would be smashing to be able to show that > this is just the outcome of how HTM works! > > If HTM is not enough then we may need to add something that has the function > of what psychologists call our short term memory (STM). This can hold up to > 7 items for 30 seconds. I am sure STM is needed for speech, but it would > make things a lot easier if it is not needed for a correct syntax. I guess > such an STM will itself be controlled by (part of) a HTM? > > Bert > > >>> On Thu, Mar 6, 2014 at 9:35 AM, Bert Frederiks <[email protected]> wrote: >>>> >>>> What would happen if one would feed Fluent with, say, books for children >>>> (to >>>> keep the task easy enough)? And then to have Fluent auto-associate from >>>> one >>>> word to the next? Would be very interesting. I would predict it shows >>>> psychotic sentences, but probably with correct syntax -- if true then >>>> this >>>> in itself (w/sh)ould be enough to end or change the jobs of most >>>> linguists, >>>> I guess. HTM is necessary but not enough for speech IMHO (if I >>>> understand >>>> well Jeff Hawkins thinks otherwise about this). >>>> >>>> Bert >>>> >>>> op 28-02-14 06:08, Chetan Surpur schreef: >>>>> >>>>> >>>>> Hi everyone, >>>>> >>>>> I'm happy to introduce a project I've been working on this week. It's a >>>>> platform for language prediction, using NuPIC together with CEPT [1]. >>>>> The >>>>> goal is to make it easy for anyone to build a language-based demo of >>>>> NuPIC >>>>> without having to know any of the internals of the CLA or CEPT. >>>>> >>>>> In fact, I have not one, but /two/ little projects to open up to you. >>>>> >>>>> >>>>> The first is nupic.fluent [2], a python library. It builds off of >>>>> Subutai's and Matt's hackathon demos [3]. With it, you can create a >>>>> model, >>>>> feed it a word (also called a "term"), and get a prediction for the >>>>> next >>>>> one. It's very simple - and that's the point. >>>>> >>>>> The second is nupic.fluent.server [4], a server-based API and sample >>>>> web >>>>> app using nupic.fluent at its core. You can use it to build a web-based >>>>> demo >>>>> of language prediction with NuPIC, something we invited the community >>>>> to >>>>> participate in during the last office hour [5]. >>>>> >>>>> But wait, there's more! I've hosted the Fluent server on an EC2 >>>>> instance, >>>>> so you all can play with the Fluent web app right now. Enjoy: >>>>> >>>>> http://bit.ly/nupic-fluent >>>>> >>>>> Note that it's far from production-ready, and it may go down at any >>>>> time. >>>>> That link is just a little taste for now; I aim to host it in a more >>>>> permanent place soon. >>>>> >>>>> Here is a screenshot of it in action: >>>>> >>>>> Inline image 1 >>>>> >>>>> Lastly, I invite everyone in the community to come hack on this with >>>>> me; >>>>> it's under the same license as NuPIC. And of course, feel free to use >>>>> it in >>>>> your demos (but be wary, it's still very early and the API might/will >>>>> change). >>>>> >>>>> Thanks, >>>>> Chetan >>>>> >>>>> [1] http://www.cept.at/ >>>>> [2] https://github.com/numenta/nupic.fluent >>>>> [3] http://numenta.org/blog/#demos >>>>> [4] https://github.com/numenta/nupic.fluent.server >>>>> [5] http://www.youtube.com/watch?v=67q75RnU58A&feature=share&t=37m16s > > > > _______________________________________________ > 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
