AI and Geoeconomics

"Cutting plane" methods converge on the optimal values of a mathematical function by repeatedly cutting out regions of a much larger set of possibilities (gold sphere). Credit: Jose-Luis Olivares/MIT Optimisation is everywhere Source: Phys.org 23 October 2015 Optimisation problems are everywhere in engineering: Balancing design trade-offs is an optimisation problem, as are scheduling and logistical planning. The theory—and sometimes the implementation—of control systems relies heavily on optimisation, and so does machine learning, which has been the basis of most recent advances in artificial intelligence. Last October, at the IEEE Symposium on Foundations of Computer Science, a trio of present and past MIT graduate students won a best-student-paper award for a new "cutting-plane" algorithm, a general-purpose algorithm for solving optimisation problems. The algorithm improves on the running time of its most efficient predecessor, and the researcher...

Drawing of the Eiffel Tower by Maurice Koechlin Science Classics Robust Statistics , 1981, New York, Wiley by Peter J. Huber. Peter J. Huber Peter J. Huber was born on March 25, 1934, in Wohlen, a small town in the Swiss countryside. He obtained a diploma in mathematics in 1958 and a Ph.D. in mathematics in 1961, both from ETH Zurich. His thesis was in pure mathematics, but he then decided to go into statistics. He spent 1961–1963 as a postdoc at the statistics department in Berkeley where he wrote his first and most famous paper on robust statistics, “Robust Estimation of a Location Parameter.” After a position as a visiting professor at Cornell University, he became a full professor at ETH Zurich. He worked at ETH until 1978, interspersed by visiting positions at Cornell, Yale, Princeton and Harvard. After leaving ETH, he held professor positions at Harvard University 1978–1988, at MIT 1988–1992, and finally at the University of Bayreuth from 1992 unti...