Tạp chí Quanta | Allison Whitten | Jun 24, 2022
Stephan Schmitz for Quanta Magazine
Smart beings learn by interacting with the world…. artificial intelligence researchers have adopted a similar strategy to teach their virtual agents new skills.
In 2009, a computer scientist then at Princeton University named Phi Phi Lý invented a data set that would change the history of artificial intelligence. Known as ImageNet, the data set included millions of labeled images that could train sophisticated machine-learning models to recognize something in a picture. The machines surpassed human recognition abilities in 2015. Soon after, Li began looking for what she called another of the “North Stars” that would give AI a different push toward true intelligence.
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She found inspiration by looking back in time over 530 million years to the Cambrian explosion, when numerous land-dwelling animal species appeared for the first time. An influential theory posits that the burst of new species was driven in part by the emergence of eyes that could see the world around them for the first time. Li realized that vision in animals never occurs by itself but instead is “deeply embedded in a holistic body that needs to move, navigate, survive, manipulate and change in the rapidly changing environment,” she said. “That’s why it was very natural for me to pivot towards a more active vision [for AI].”
Today, Li’s work focuses on AI agents that don’t simply accept static images from a data set but can move around and interact with their environments in simulations of three-dimensional virtual worlds.
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.
Work in embodied AI today includes any agent that can probe and change its own environment.
“The meaning of embodiment is not the body itself, it is the holistic need and functionality of interacting and doing things with your environment,” said Li.
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This interactivity gives agents a whole new — and in many cases, better — way of learning about the world. It’s the difference between observing a possible relationship between two objects and being the one to experiment and cause the relationship to happen yourself. Armed with this new understanding, the thinking goes, greater intelligence will follow. And with a suite of new virtual worlds up and running, embodied AI agents have already begun to deliver on this potential, making significant progress in their new environments.
The New Robotic Frontier
Robots are, inherently, embodied intelligence agents. By inhabiting some type of physical body in the real world, they represent the most extreme form of embodied AI agents. But many researchers are now finding that even these agents can benefit from training in virtual worlds.
“State-of-the-art algorithms [in robotics], like reinforcement learning and those types of things, usually require millions of iterations to learn something meaningful,” said Mottaghi. As a result, training real robots on difficult tasks can take years.
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