Of course — that is why I called it a pre-experiment. There are some subtle mechanics (distribution determination, system partitioning) that we need to work out in the actual calculations for phi (or phi*) and the approach I outline will allow us to concentrate on figuring these out. Once we know how to perform these calculations for this simple pre-experiment, then we can more easily add complexity to it by extending and modifying it to the AF and/or WA. And of course connecting IIT with strange attractor formation would be the “holy grail” of the entire pipeline of experiments,
—matt > On Jun 15, 2017, at 9:19 PM, Ben Goertzel <[email protected]> wrote: > > Yes, that would be a meaningful experiment to start with, though > obviously not a substitute for an experiment that gauges the > integrated information of the dynamics more broadly... > > On Fri, Jun 16, 2017 at 12:54 AM, Matt Ikle <[email protected]> wrote: >> Hi Ben, >> >> I've been thinking a lot about how to actually create experiments for >> connecting ECAN with Phi. >> >> It seems to me that a simple pre-experiment we could run builds upon the >> insecticide experiment as follows. We could load the insecticide experiment >> background knowledge and then stimulate the atoms for poison and insects >> just as before. We can track STI values for these three atoms over time and >> calculate IIT time series values from the STI time series values. To do this >> means making only some minor modifications to the practical_phi toolbox >> Matlab code to find IIT. >> >> We may even be able to, though I am more unsure about this, build upon this >> simple experiment to detect attractor formations within ECAN dynamics prior >> to looking at the larger ECAN dynamics using PLSI to reduce the space. >> >> Just trying to create a simple first set of experiments. Thoughts? >> >> --matt >> >> Sent from my iPhone >> >> On May 23, 2017, at 10:18 AM, Ben Goertzel <[email protected]> wrote: >> >> Yes, agreed... >> >> On May 24, 2017 00:15, "Matthew Ikle" <[email protected]> wrote: >>> >>> Yes all of this makes a lot of sense and could be exciting, especially the >>> connection to Phi. Could be an extremely interesting paper — but a lot will >>> depend upon parameter tuning as well as creating an appropriate experiment. >>> >>> Before we begin setting up the experiment to look for connections between >>> Phi and strange attractor structure, though, we obviously must first ensure >>> basic ECAN implementation as follows: >>> >>> Step 1: Ensure that ECAN works correctly and fulfills basic design >>> criteria; >>> Step 2: Run poison experiment and retune parameters. >>> >>> I feel confident that after Misgana makes the minor changes we discussed >>> in HK, ECAN should work correctly. Only after we have run through the two >>> steps above, though. should we proceed with the next (IIT) step and perform >>> additional parameter tuning. >>> >>> At some point (probably after all of the above), we should also enable >>> HebbianLink updating and run experiments testing the three updating >>> equations we have developed and setting the stage for yet another set of >>> parameter tuning. >>> >>> —matt >>> >>>> On May 23, 2017, at 3:52 AM, Ben Goertzel <[email protected]> wrote: >>>> >>>> Matt, >>>> >>>> Thinking about how to analyze time-series data from ECAN, I thought it >>>> might be cool to look for interactions between IIT (Phi) and strange >>>> attractor structure in the attentional focus... >>>> >>>> I found this code which lets us analyze data using Integrated >>>> Information Theory (Tononi's Phi) >>>> >>>> https://figshare.com/articles/phi_toolbox_zip/3203326 >>>> >>>> This has gotten some acceptance as a "measure of consciousness", so if >>>> we could show that some ECAN parameters or aspects correlate with >>>> "degree of consciousness" as measured by Phi, this would let us >>>> publish a wizzy and popular paper.... For instance, what if the >>>> system was more conscious (higher Phi) when it connected a sentence >>>> with background knowledge, than when it parsed a sentence but was >>>> unable to connect it with background knowledge... >>>> >>>> >>>> On the other hand, another interesting thing to do would be to look at >>>> a delay-embedding of the dynamics... >>>> >>>> Long ago I used the TISEAN toolkit for nonlinear time series analysis >>>> >>>> What I am thinking here is: If we are loading in Atoms from a bunch of >>>> texts, we could run PLSI or similar (latent semantic indexing) on the >>>> texts (Eyob could help with that, he's a master of PLSI), to create a >>>> dimensional space. At any moment in time, the WordNodes and named >>>> ConceptNodes in the AttentionalFocus would then assign the AF a >>>> certain point in the dimensional space defined by the PLSI factors. >>>> >>>> This would turn the AF into a trajectory in n-dimensional space... >>>> >>>> One could then use some approach to figure out the optimal delay and >>>> do a delay-embedding of this trajectory, hopefully revealing the >>>> underlying attractor structure... >>>> >>>> TISEAN seems only to do delay embedding from 1D time series >>>> >>>> https://www.pks.mpg.de/~tisean/Tisean_3.0.1/index.html >>>> >>>> but there are papers explaining how to do it from multi-D time series >>>> >>>> https://arxiv.org/pdf/nlin/0609029.pdf >>>> >>>> https://arxiv.org/pdf/1409.5974.pdf >>>> >>>> Showing that the AF contents occupy a certain strange attractor -- >>>> maybe shifting which strange attractor over time, or shifting the >>>> shape of the strange attractor over time, would be interesting >>>> >>>> Some association between the Phi (IIT) value and some property of the >>>> inferred attractor would also be interesting... >>>> >>>> -- Ben >>>> >>>> >>>> >>>> >>>> -- >>>> Ben Goertzel, PhD >>>> http://goertzel.org >>>> >>>> "I am God! I am nothing, I'm play, I am freedom, I am life. I am the >>>> boundary, I am the peak." -- Alexander Scriabin >>> >> > > > > -- > Ben Goertzel, PhD > http://goertzel.org > > "I am God! I am nothing, I'm play, I am freedom, I am life. I am the > boundary, I am the peak." -- Alexander Scriabin -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/57F4DB20-446A-4565-AA3D-D7C4FFFD3762%40gmail.com. For more options, visit https://groups.google.com/d/optout.
