A new robot has overcome a fundamental challenge of locomotion by teaching itself how to walk. Researchers from Google developed algorithms that helped the four-legged bot to learn how to walk across a range of surfaces within just hours of practice, annihilating the record times set by its human overlords. Their system uses deep reinforcement learning, a form of AI that teaches through trial and error by providing rewards for certain actions. This technique is typically evaluated in virtual environments. However, building simulations that could replicate the robot walking on various surfaces would be highly complex and time-consuming, so the researchers chose to train their system in the real world.

The same image shown using different analysis methods. Defects that don't exist are shown in purple, and defects that weren't identified are orange. In mere hours, researchers created a neural network that performed as well as a human expert, demonstrating MENNDL's ability to significantly reduce the time to analyze electron microscopy images. Finding defects in electron microscopy images takes months. It's called MENNDL, the Multinode Evolutionary Neural Networks for Deep Learning.

News of a specialized computer program beating human champions at games like chess and Go might not surprise people as much as it might have before, as it did when Deep Blue beat world chess champ Garry Kasparov back in 1997, or even more recently when Google DeepMind's AI AlphaGo beat Lee Sedol in a stunning upset back in 2016. But the goal for AI researchers has always been to develop an artificial general intelligence (AGI) that's capable at not only merely mastering games, but also learning and solving all kinds of things in a general way, as humans do. And it seems that Google's subsidiary DeepMind has once again gotten one step closer to this goal with AlphaZero, their latest AI development. Their recently published pre-print research outlined how AlphaZero succeeded in handily beating one of the world's top chess engines -- after teaching itself and mastering the game in four hours and reaching a "superhuman" level of play in a mere 24 hours in not only chess but in two other different types of board games. The most remarkable thing about this latest evolution is that in contrast to finely hand-tuned game-playing programs, the only input AlphaZero had were the basic rules of the game.