On progressing human understanding in the shadow of superhuman deep learning artificial intelligence entities

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For our testbed application, the game of Go, we developed the `cost of passing' measure to assess the evolution of human understanding. It allows us to have a fine-grained context-dependent evaluation of playing performance and will be the primary tool in the historical game analysis. The cost of passing is also suitable for the visual 'fingerprinting' of a large number of games, which will help the data mining efforts greatly.We also researched and evaluated several hardware options for computationally intensive bulk game analysis. We purchased, set up the equipment, and stress-tested it with machine learning algorithms for training better AIs for the analysis.We also considered mathematically and philosophically what the existence of a perfect player would mean for this application domain. Philosophically it gives an interesting perspective on scientific realism, as the ultimate reality of a board game is the complete game tree, and it has a not-efficiently computable but seemingly straightforward existence. Mathematically, considering the space of possibilities for AI and human knowledge growth led to a side project for constructing transducers for representing perfect game strategies using logic programming.