New AI model could streamline operations in a robotic warehouse
By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.
By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.
MIT LIDS awarded funding from the Appalachian Regional Commission as part of a multi-state collaborative project to model and test new smart grid technologies for use in rural areas.
An easy-to-use technique could assist everyone from economists to sports analysts.
During the last week of November, MIT hosted symposia and events aimed at examining the implications and possibilities of generative AI.
A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.
How do powerful generative AI systems like ChatGPT work, and what makes them different from other types of artificial intelligence?
By focusing on causal relationships in genome regulation, a new AI method could help scientists identify new immunotherapy techniques or regenerative therapies.
A cross-departmental team is leading efforts to utilize machine learning for increased efficiency in heating and cooling MIT’s buildings.
With this new approach, a tailsitter aircraft, ideal for search-and-rescue missions, can plan and execute complex, high-speed acrobatic maneuvers.
The MIT Schwarzman College of Computing awards seed grants to seven interdisciplinary projects exploring AI-augmented management.
Nine faculty members have been granted tenure in six units across MIT’s School of Engineering.
The former director of LIDS was a beloved professor who blended intellectual rigor with curiosity.
Assistant Professor Cathy Wu is addressing traffic control problems by leveraging deep reinforcement learning.
Researchers develop a machine-learning technique that can efficiently learn to control a robot, leading to better performance with fewer data.
Luca Carlone and Jonathan How of MIT LIDS discuss how future robots might perceive and interact with their environment.