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"This type of approach can speed up learning times and improve the efficiency of algorithms, says Max Jaderberg at Google's AI company DeepMind. The company used a similar technique last year to teach an AI to explore a virtual maze. Its algorithm learned much more quickly than conventional reinforcement learning approaches. "Our agent is far quicker and requires a lot less experience from the world to train, making it much more data efficient," he says."

"This is the broad goal of a new field known as embodied AI, and Li's not the only one embracing it. It overlaps with robotics, since robots can be the physical equivalent of embodied AI agents in the real world, and reinforcement learning - which has always trained an interactive agent to learn using long-term rewards as incentive. But Li and others think embodied AI could power a major shift from machines learning straightforward abilities, like recognizing images, to learning how to perform complex humanlike tasks with multiple steps, such as making an omelet."

"Learning from rewards seems like the simplest thing. I make coffee, I sip coffee, I'm happy. My brain registers "brewing coffee" as an action that leads to a reward. That's the guiding insight behind deep reinforcement learning, a family of algorithms that famously smashed most of Atari's gaming catalog and triumphed over humans in strategy games like Go. Here, an AI "agent" explores the game, trying out different actions and registering ones that let it win."

"In the future, the team predict the software could also learn the appropriate emotion to express at a given time. "It could be mostly empathic," said Huang, adding that a challenge would be to avoid the chatbot reinforcing negative feelings such as rage."

"Qoves founder Shafee Hassan claimed to MIT Technology Review that beauty scoring is widespread; social media platforms use it to identify attractive faces and give them more attention."

"Hassabis was a child chess prodigy, who learned the game aged four and was able to beat his dad three weeks later - indeed, when he started playing competitively he was so small he had to bring a pillow with him to reach the board - and became a strong player. Yet in AlphaZero's case there was no human input, other than telling it the rules of each game. "In a matter of a few hours it was superhuman," Hassabis says proudly."