One problem that dogged early work in AI is uncertainty. Nearly all reasoning and decision-making operates under conditions of uncertainty. For example, you may need to decide whether to go on a picnic while being uncertain whether it will rain. Bayesian decision theory is the standard mathematical model of inference and decision-making under uncertainty. Uncertainty is codified through probability. Precise rules dictate how to update probabilities in light of new evidence and how to select actions in light of probabilities and utilities. (See the entries Bayes’s theorem and normative theories of rational choice: expected utility  for details.)  In the 1980s and 1990s, technological and conceptual developments enabled efficient computer programs that implement or approximate Bayesian inference in realistic scenarios. An explosion of Bayesian AI ensued (Thrun, Burgard, and Fox 2006), including the aforementioned advances in speech recognition and driverless vehicles. Tractable algorithms that handle uncertainty are a major achievement of contemporary AI (Murphy 2012), and possibly a harbinger of more impressive future progress.