System helps humans predict when self-driving cars will make mistakes
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
MIT students designed, built, and tested a jet engine with AI copilots, assessing AI’s usefulness in developing high-performance aerospace systems.
Read MoreDuring the AI and Society Forum, leading MIT researchers examined critical questions about AI’s influence on employment and democracy.
Read MoreResearchers combined an efficient algorithm with dedicated hardware to rapidly generate 3D maps for navigation using minimal memory and power.
Read MoreMIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.
Read MoreAcademia-industry relationship is an early-stage accelerator, supporting professional progress and research.
Read MoreA new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.
Read MoreWith insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.
Read MoreA 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.
Read MoreThe approach combines physics and machine learning to avoid damaging disruptions when powering down tokamak fusion machines.
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