Artificial Intelligence: Why You Should Have Paid Attention in Your Statistics Classes

While not a particularly new field in Computer Science, Artificial Intelligence has gone through many changes over the years with many advances and setbacks. As it has become increasingly widespread and marked with new successes over the past decade or two, one thing has become clear: statistics and probabilistic approaches seem to be the key to achieving continues success in just about all facets of the field.

One area with AI that has seen tremendous success from statistical and probabilistic approaches is that of Computational Linguistics. This area has a longtime rivalry between approaches based on hand-generated rules from linguists versus statistical/probabilistic approaches. As Peter Norvig notes, the majority of systems successfully solving a number of problems within Computational Linguistics use statistical and/or probabilistic approaches at least partially if not entirely. These include such popular and widespread problems and application areas as search engines, machine translation, and speech recognition. As new research in this area tends to be towards newer and better statistical and probabilistic approaches, this trend does not seem to be changing anytime soon.

Yet another area with traditionally more formal roots that has benefited greatly from probability and statistics is Graph Theory. The work of Thomas Bayes and Andrey Markov to formulate probabilistic graph-based models is very widespread through all of Artificial Intelligence these days and the applications of such models may be limitless. Such models are used widely in Computational Linguistics, pattern recognition, and Bioinformatics, just to name a few. Such models are capable of encoding the fluctuations, randomness, and uncertainty that are a part of most things we try to model.

Machine Learning is another popular area within Artificial Intelligence that makes heavy use of statistics. A lot of machine learning techniques are actually intended to solve problems from statistics such as linear and logistic regression using new approaches. A number of other parallels exist as well showing the interrelation between Machine Learning and Statistics.

While math in general always seems to be a driving force behind the discovery of new techniques and algorithms in software, statistics and probability in particular are becoming increasingly popular in computer science. It doesn't seem likely that computer science as a discipline will ever break away from its dependence on math, so to the CS majors out there be sure to pay attention in math class!