Category Archives: AI

Input-Output Maps are Strongly Biased Towards Simple Outputs

Posted on 2019-02-11 by Graham

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

Posted on 2019-01-24 by Graham

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

Posted on 2019-01-17 by Graham

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

Posted on 2019-01-03 by Graham

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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