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 -- 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/75E26928-FCCB-4C0F-83E8-9E847C866FB2%40gmail.com. For more options, visit https://groups.google.com/d/optout.
