Chess Com August 30 1999 Homepage Archive Wayback

The Signal and the Noise

The International Bestseller by 'The Galileo of number crunchers' (Independent) Every time we choose a route to work, decide whether to go on a second date, or set aside money for a rainy day, we are making a prediction about the future. Yet from the financial crisis to ecological disasters, we routinely fail to foresee hugely significant events, often at great cost to society. The rise of 'big data' has the potential to help us predict the future, yet much of it is misleading, useless or distracting. In The Signal and the Noise, the New York Times political forecaster Nate Silver, who accurately predicted the results of every state in the 2012 US election, reveals how we can all develop better foresight in an uncertain world. From the stock market to the poker table, from earthquakes to the economy, he takes us on an enthralling insider's tour of the high-stakes world of forecasting, showing how we can all learn to detect the true signals amid a noise of data. 'Remarkable and rewarding' Matthew D'Ancona, Sunday Telegraph 'A lucid explanation of how to think probabilistically' Guardian

Smith-Morra Gambit Finegold Defense

Reinforcement Learning

Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.