By experimenting, computers are figuring out how to do things that no programmer could teach them.
This approach, known as reinforcement learning, is largely how AlphaGo, a computer developed by a subsidiary of Alphabet called DeepMind, mastered the impossibly complex board game Go and beat one of the best human players in the world in a high-profile match last year.
In 1951, Marvin Minsky, a student at Harvard who would become one of the founding fathers of AI as a professor at MIT, built a machine that used a simple form of reinforcement learning to mimic a rat learning to navigate a maze.
Reinforcement Learning Breakthrough An approach to artificial intelligence that gets computers to learn like people, without explicit instruction.
He says that the key is combining it with deep learning, a technique that involves using a very large simulated neural network to recognize patterns in data (see â 10 Breakthrough Technologies 2013: Deep Learning â).
Reinforcement learning works because researchers figured out how to get a computer to calculate the value that should be assigned to, say, each right or wrong turn that a rat might make on its way out of its maze.
In recent years, however, deep learning has proved an extremely efficient way to recognize patterns in data, whether the data refers to the turns in a maze, the positions on a Go board, or the pixels shown on screen during a computer game.
And researchers at Google, also an Alphabet subsidiary, worked with DeepMind to use deep reinforcement learning to make its data centers more energy efficient.
Reinforcement learning led to AlphaGoâs stunning victory over a human Go champion last year.
Indeed, researchers are still figuring out just how to make reinforcement learning work in complex situations in which there is more than one objective.
Reinforcement learning led to AlphaGoâs stunning victory over a human Go champion last year.