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 > > -- 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/CACYTDBct_WhHxJNfosHaaafyuh42e_1S6zc%2B%3D_9cm-%3DtmcUZ2A%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
