Phil 4.4.19

6:00 – 1:30, 3:30 – 7:00 ASRC PhD 1:30 – 2:30 NASA

    • Woke up early, so here we are
    • I have the permutation code running, but I don’t like it. I can tell the overall stability of the terms, which is really good. We go from the terms in one set:
      ---------------- [('Group 1',), ('tymora1',), ('tymora2',), ('tymora3',), ('tymora4',)]
      new_set = {'something', 'dragon', 'grogg'}, master_set = {'something', 'dragon', 'grogg'}, sub_channel_list = ['Group 1']
      new_set = {'light', 'dragon', 'coins'}, master_set = {'light', 'dragon', 'grogg', 'something', 'coins'}, sub_channel_list = ['tymora1']
      new_set = {'eyes', 'coins', 'barrier'}, master_set = {'light', 'dragon', 'eyes', 'grogg', 'something', 'coins', 'barrier'}, sub_channel_list = ['tymora2']
      new_set = {'dragon', 'coins', 'barrier'}, master_set = {'grogg', 'something', 'coins', 'barrier', 'light', 'eyes', 'dragon'}, sub_channel_list = ['tymora3']
      new_set = {'platform', 'dragon', 'coins'}, master_set = {'grogg', 'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora4']
    • To just two groups
      ---------------- [('Group 1', 'tymora1'), ('Group 1', 'tymora2'), ('Group 1', 'tymora3'), ('Group 1', 'tymora4'), ('tymora1', 'Group 1'), ('tymora1', 'tymora2'), ('tymora1', 'tymora3'), ('tymora1', 'tymora4'), ('tymora2', 'Group 1'), ('tymora2', 'tymora1'), ('tymora2', 'tymora3'), ('tymora2', 'tymora4'), ('tymora3', 'Group 1'), ('tymora3', 'tymora1'), ('tymora3', 'tymora2'), ('tymora3', 'tymora4'), ('tymora4', 'Group 1'), ('tymora4', 'tymora1'), ('tymora4', 'tymora2'), ('tymora4', 'tymora3')]
      new_set = {'light', 'something', 'dragon', 'coins'}, master_set = {'light', 'dragon', 'something', 'coins'}, sub_channel_list = ['Group 1', 'tymora1']
      new_set = {'eyes', 'dragon', 'something', 'coins', 'barrier'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'dragon'}, sub_channel_list = ['Group 1', 'tymora2']
      new_set = {'dragon', 'coins', 'barrier'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'dragon'}, sub_channel_list = ['Group 1', 'tymora3']
      new_set = {'platform', 'something', 'dragon', 'coins'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['Group 1', 'tymora4']
      new_set = {'light', 'something', 'dragon', 'coins'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora1', 'Group 1']
      new_set = {'light', 'dragon', 'coins'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora1', 'tymora2']
      new_set = {'light', 'dragon', 'coins'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora1', 'tymora3']
      new_set = {'light', 'dragon', 'coins'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora1', 'tymora4']
      new_set = {'eyes', 'dragon', 'something', 'coins', 'barrier'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora2', 'Group 1']
      new_set = {'eyes', 'dragon', 'coins', 'barrier'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora2', 'tymora1']
      new_set = {'eyes', 'dragon', 'coins', 'barrier'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora2', 'tymora3']
      new_set = {'eyes', 'dragon', 'coins', 'barrier'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora2', 'tymora4']
      new_set = {'dragon', 'coins', 'barrier'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora3', 'Group 1']
      new_set = {'dragon', 'coins', 'barrier'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora3', 'tymora1']
      new_set = {'dragon', 'coins', 'barrier'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora3', 'tymora2']
      new_set = {'platform', 'dragon', 'coins', 'barrier'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora3', 'tymora4']
      new_set = {'platform', 'something', 'dragon', 'coins'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora4', 'Group 1']
      new_set = {'platform', 'dragon', 'coins'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora4', 'tymora1']
      new_set = {'platform', 'dragon', 'coins'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora4', 'tymora2']
      new_set = {'platform', 'dragon', 'coins'}, master_set = {'something', 'coins', 'barrier', 'light', 'eyes', 'platform', 'dragon'}, sub_channel_list = ['tymora4', 'tymora3']
    • As you can see, it settles very fast. I’d just like to figure out a way to get intermediate numbers out of it.
    • Creativity Machine
      • Summary – An artificial neural network that has been trained upon some body of knowledge, and then perturbed in a specially prescribed way, tends to activate into concepts and/or strategies (e.g., new ideas) generalized from that conceptual space. These continuously perturbed networks are called ‘imagination engines‘ or ‘imagitrons‘. If another computational agent, such as a traditional rule-based algorithm or, even better, another trained neural network is allowed to filter for the very best of these emerging ideas, in terms of novelty, utility, or value, we arrive at an extremely valuable neural architecture, the patented Creativity Machine. Optional feedback connections between this latter computational agent and the imagination engine assure swift convergence toward useful ideas or strategies. This new AI paradigm is vastly more powerful than genetic algorithms (GA), efficiently generating new concepts on mere desktop computers rather than on the computational clusters required of GAs. This generative neural network paradigm can and has been extended to whole assemblies of perturbed neural nets generating complex ideas as a multitude of neural modules watch, selectively reinforcing those notions offering novelty, utility, or value of any kind.
    • node2vec: Scalable Feature Learning for Networks

 

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