MIT engineers design an aerial microrobot that can fly as fast as a bumblebee
With insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.
With insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.
A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.
The approach combines physics and machine learning to avoid damaging disruptions when powering down tokamak fusion machines.
The research center, sponsored by the DoE’s National Nuclear Security Administration, will advance the simulation of extreme environments, such as those in hypersonic flight and atmospheric reentry.
Popular mechanical engineering course applies machine learning and AI theory to real-world engineering design.
MIT researchers found that special kinds of neural networks, called encoders or “tokenizers,” can do much more than previously realized.
A new framework from the MIT-IBM Watson AI Lab supercharges language models, so they can reason over, interactively develop, and verify valid, complex travel agendas.
The system automatically learns to adapt to unknown disturbances such as gusting winds.
Researchers are developing algorithms to predict failures when automation meets the real world in areas like air traffic scheduling or autonomous vehicles.
New phase will support continued exploration of ideas and solutions in fields ranging from AI to nanotech to climate — with emphasis on educational exchanges and entrepreneurship.