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Intelligence, physics and information – the tradeoff between accuracy and simplicity in machine learning
[article]
2020
arXiv
pre-print
How can we enable machines to make sense of the world, and become better at learning? To approach this goal, I believe viewing intelligence in terms of many integral aspects, and also a universal two-term tradeoff between task performance and complexity, provides two feasible perspectives. In this thesis, I address several key questions in some aspects of intelligence, and study the phase transitions in the two-term tradeoff, using strategies and tools from physics and information. Firstly, how
arXiv:2001.03780v2
fatcat:piduzlhoafcjhhsgthulbbhtke