Cloud platforms, such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform, are tremendously complex. Cloud platforms are also extremely expensive to build and operate, so providers have a strong incentive to optimize their use. A nascent approach is to leverage machine learning (ML) in the platforms’ resource management using supervised learning techniques.
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Communications of the ACM, March 2019
By Judea Pearl
“Unlike the rules of geometry, mechanics, optics, or probabilities, the rules of cause and effect have been denied the benefits of mathematical analysis.” “…the art of automated reasoning.”
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Communications of the ACM, January 2019
By Yolanda Gil, Suzanne A. Pierce, et al.
“Many aspects of geosciences pose novel problems for intelligent systems research… A recently launched Research Coordination Network on Intelligent Systems for Geosciences followed a workshop at the National Science Foundation on this topic. This expanding network builds on the momentum of the NSF EarthCube initiative for geosciences, and is driven by practical problems in Earth, ocean, atmospheric, polar, and geospace sciences. Based on discussions and activities within this network, this article presents a research agenda for intelligent systems inspired by geosciences challenges.”
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Communications of the ACM, June 2018, Vol. 61 No. 6, Pages 13-14
By Chris Edwards
“The secret to deep learning’s success in avoiding the traps of poor local minima may lie in a decision taken primarily to reduce computation time.”
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MIT Technology Review, Feb. 15, 2019
By David Rotman
“In other words, AI’s chief legacy might not be driverless cars or image search or even Alexa’s ability to take orders, but its ability to come up with new ideas to fuel innovation itself.”
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