Human-machine teaming dives underwater
Researchers are developing hardware and algorithms to improve collaboration between divers and autonomous underwater vehicles engaged in maritime missions.
Researchers are developing hardware and algorithms to improve collaboration between divers and autonomous underwater vehicles engaged in maritime missions.
Researchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.
Read MoreResearchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.
Read MoreDean Price, assistant professor in the Department of Nuclear Science and Engineering, sees a bright future for nuclear power, and believes AI can help us realize that vision.
Read MoreMIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.
Read MoreBy quickly generating aesthetically accurate previews of fabricated objects, the VisiPrint system could make prototyping faster and less wasteful.
Read MoreA new model measures defects that can be leveraged to improve materials’ mechanical strength, heat transfer, and energy-conversion efficiency.
Read MoreThis new approach adapts to decide which robots should get the right of way at every moment, avoiding congestion and increasing throughput.
Read MoreMIT Sea Grant works with the Woodwell Climate Research Center and other collaborators to demonstrate a deep learning-based system for fish monitoring.
Read MoreBy moving their hands and fingers, users can direct a robot to play piano or shoot a basketball, or they can manipulate objects in a virtual environment.
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