AI generates playful, human-like games
Peer-Reviewed Publication
Updates every hour. Last Updated: 4-May-2025 10:09 ET (4-May-2025 14:09 GMT/UTC)
A team of NYU scientists has now created a computer model that can represent and generate human-like goals by learning from how people create games. The work could lead to AI systems that better understand human intentions and more faithfully model and align with our goals. It may also lead to AI systems that can help us design more human-like games.
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By watching their own motions with a camera, robots can teach themselves about the structure of their own bodies and how they move, a new study from researchers at Columbia Engineering now reveals. Equipped with this knowledge, the robots could not only plan their own actions, but also overcome damage to their bodies.
"Like humans learning to dance by watching their mirror reflection, robots now use raw video to build kinematic self-awareness," says study lead author Yuhang Hu, a doctoral student at the Creative Machines Lab at Columbia University, directed by Hod Lipson, James and Sally Scapa Professor of Innovation and chair of the Department of Mechanical Engineering. "Our goal is a robot that understands its own body, adapts to damage, and learns new skills without constant human programming."
Ethylene oxide is a “platform chemical” with a $40 billion annual worldwide market used in the production of plastics, textiles and many other common products. Tufts University chemists discovered an inexpensive way to reduce CO2 emissions and decrease the need for chlorine to produce the chemical.
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