Human evolutionary success no doubt has rested largely on the brain’s ability to incorporate the principles of Bayesian mathematics. Ironically, though, about a century ago human scientists largely abandoned Bayesian methods for scientific purposes. Standard statistical methods for reaching scientific conclusions instead follow a textbook
existence where we recognize objects, navigate space, exchange communication … a lot of the things that philosophers had written off about the human species precisely because animals shared them. These have turned out to be the greater hurdles for artificial intelligence.”


“The first several decades of computer science were defined by the rigidity of the logic — either it was zero or it was one, it was true or false,” Christian said. “I think the next few decades are going to be defined by probabilistic reasoning using Bayesian inference.”
Someday, of course, machines may close the gap between them and humans (and other animals). Humans are still better than computers at driving cars, for instance, because of the need to quickly identify objects appearing in the road — distinguishing a shadow from a piece of trash or a dog or a child. Computers may achieve such skill, Christian believes, through programming that makes use of Bayesian probability.
With Bayesian reasoning, inferences are based on probabilities weighted by evidence from experience. It seems to be the way humans learn how to cope with the world and reason about appropriate behaviors (see “The Probabilistic Mind” SN: 10/8/11, p. 1.

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