A model of learning that is decades-old is under fire with implications for AI.The buzz of a notification or the ding of an email might inspire excitement - or dread.In a famous experiment, Ivan Pavlov showed that dogs can be taught to salivate at the tick of a metronome or the sound of a harmonium.This connection of cause to effect - known as associative, or reinforcement learning - is central to how most animals deal with the world.Since the early 1970s the dominant theory of what is going on has been that animals learn by trial and error.Associating a cue (a metronome) with a reward (food) happens as follows.When a cue comes, the animal predicts when the reward will occur.Then, it waits to see what arrives.After that, it computes the difference between prediction and result - the error.Finally, it uses that error estimate to update things to make better predictions in future.Belief in this approach was itself reinforced in the late 20th century by two things.One of these was the discovery that it is also good at solving engineering problems related to artificial intelligence (AI).Deep neural networks learn by minimizing the error in their predictions.
