DeepMind’s Vibrant New Virtual World Trains Flexible AI With Endless Play
...The game-managing AI keeps an eye on what the game-playing algorithms are learning and automatically generates new worlds, games, and tasks to continuously confront them with new experiences.
The team said some veteran algorithms faced 3.4 million unique tasks while playing around 700,000 games in 4,000 XLand worlds. But most notably, they developed a general skillset not related to any one game, but useful in all of them.
...By presenting deep reinforcement learning algorithms with an open-ended, always-shifting world to learn from, DeepMind says their algorithms are beginning to demonstrate “zero-shot” learning at new never-before-seen tasks. That is, they don’t need retraining to perform novel tasks at a decent level—sight-unseen.
...But if XLand is a proof-of-concept, their findings may suggest increasingly sophisticated worlds will give birth to increasingly sophisticated algorithms.
...Some believe deep learning will hit a wall and have to pair up with other approaches, like symbolic AI. But three of the field’s pioneers—Geoffrey Hinton, Yoshua Bengio, and Yann LeCun—recently co-wrote a paperarguing the opposite. They acknowledge deep learning’s shortcomings, including its lack of flexibility and inefficiency, but believe it will overcome its challenges without resorting to other disciplines.
See the full story here: https://singularityhub.com/2021/08/01/deepminds-vibrant-new-virtual-world-trains-flexible-ai-with-endless-play
