Amos Storkey

rdf:langString Amos Storkey

rdf:langString Amos James Storkey

rdf:langString Amos James Storkey

rdf:langString First Convolutional Network for Learning Go

rdf:langString British

rdf:langString Amos James Storkey is Professor of Machine Learning and Artificial Intelligence at the School of Informatics, University of Edinburgh. Storkey studied mathematics at Trinity College, Cambridge and obtained his doctorate from Imperial College, London. In 1997 during his PhD, he worked on the Hopfield Network a form of recurrent artificial neural network popularized by John Hopfield in 1982. Hopfield nets serve as content-addressable ("associative") memory systems with binary threshold nodes and Storkey developed what became known as the "Storkey Learning Rule" . Subsequently, he has worked on approximate Bayesian methods, machine learning in astronomy, graphical models, inference and sampling, and neural networks. Storkey joined the School of Informatics at the University of Edinburgh in 1999, was Microsoft Research Fellow from 2003 to 2004, appointed as reader in 2012, and to a personal chair in 2018. He is currently a Member of Institute for Adaptive and Neural Computation, Director of CDT in Data Science [2014-22] leading the Bayesian and Neural Systems Group. In December 2014, Clark and Storkey together published an innovative paper "Teaching Deep Convolutional Neural Networks to Play Go". Convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. Their paper showed that a Convolutional Neural Network trained by supervised learning from a database of human professional games could outperform GNU Go and win some games against Monte Carlo tree search Fuego 1.1 in a fraction of the time it took Fuego to play.