Machine learning branches out
Much artificial-intelligence research is concerned with finding statistical correlations between variables: What combinations of visible features indicate the presence of a particular object in a digital image? What speech sounds correspond with instances of what words? What medical, genetic, and environmental factors are correlated with what diseases?As the number of variables grows, calculating their aggregate statistics becomes dauntingly complex. But that calculation can be drastically simplified if you know something about the structure of the data — that, for instance, the sound corresponding to the letter “T” is frequently followed by the sound corresponding to the letter “R,” but never by the sound corresponding to the letter “Q.”In a paper being presented in December at the annual conference of the Neural Information Processing Systems Foundation, MIT researchers describe a new technique that expands the class of data sets whose structure can be efficiently deduced. Not only that, but their technique...