A new generation of AI is now starting to take a much more experimental approach that could catapult machine learning way past humans.

Remember Deepmind's AlphaGo? It represented a fundamental breakthrough in AI development, because it was one of the first game-playing AIs that took no human instruction and read no rules.

Instead, it used a technique called self-play reinforcement learning to build up its own understanding of the game. Pure trial and error across millions, even billions of virtual games, starting out more or less randomly pulling whatever levers were available, and attempting to learn from the results.

Within two years of the start of the project in 2014, AlphaGo had beaten the European Go champion 5-0 – and by 2017 it had defeated the world's #1 ranked human player.

At this point, Deepmind unleashed a similar AlphaZero model on the chess world, where models like Deep Blue, trained on human thinking, knowledge and rule sets, had been beating human grandmasters since the 90s. AlphaZero played 100 matches against the reigning AI champion, Stockfish, winning 28 and tying the rest.

Human thinking puts the brakes on AI

Deepmind started dominating these games – and shoji, Dota 2, Starcraft II and many others – when it jettisoned the idea that emulating a human was the best way to get a good result.

Bound by different limits than us, and gifted with different talents, these electronic minds were given the freedom to interact with things on their own terms, play to their own cognitive strengths, and build their own ground-up understanding of what works and what doesn't.

AlphaZero doesn't know chess like Magnus Carlssen does. It's never heard of the Queen's Gambit or studied the great grandmasters. It's just played a shit-ton of chess, and built up its own understanding against the cold, hard logic of wins and losses, in an inhuman and inscrutable language it created itself as it went.