I always knew that reinforcement learning would teach us more about ourselves than any other kind of AI approach. This feeling was backed up in a paper published recently in Nature.
DeepMind, Alphabet’s AI subsidiary, has once again used lessons from reinforcement learning to propose a new theory about the reward mechanisms within our brains.
The hypothesis, supported by initial experimental findings, could not only improve our understanding of mental health and motivation. It could also validate the current direction of AI research toward building more human-like general intelligence.
It turns out the brain’s reward system works in much the same way—a discovery made in the 1990s, inspired by reinforcement-learning algorithms. When a human or animal is about to perform an action, its dopamine neurons make a prediction about the expected reward.