Category Archives: AI

Input-Output Maps are Strongly Biased Towards Simple Outputs

About this paper Input-Output Maps are Strongly Biased Towards Simple Outputs, Kamaludin Dingle, Chico Q. Camargo and Ard A. Louis, Nature Communications 9, 761 (2018). Notes On Saturday I went to my alma mater’s Morning of Theoretical Physics, which was … Continue reading

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Structured Pruning of Deep Convolutional Neural Networks

Structured Pruning of Deep Convolutional Neural Networks, Sajid Anwar et al. In the ACM Journal on Emerging Technologies in Computing special issue on hardware and algorithms for learning-on-a-chip, May 2017. Notes Quick, a software engineer mentions a “performance” problem to … Continue reading

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On the continuous history of approximation

The Difference Engine – the Charles Babbage machine, not the steampunk novel – is a device for finding successive solutions to polynomial equations by adding up the differences introduced by each term between the successive input values. This sounds like … Continue reading

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Impossibility and Uncertainty in AI

About this paper Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function), Peter Eckersley. Submitted to the ArXiV on December 31, 2018. Notes Ethical considerations in artificial intelligence applications have arguably … Continue reading

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