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Machine learning for everyone
A new EECS course on applications of machine learning teaches students from a variety of disciplines about one of today’s hottest topics.
Tiny motor can “walk” to carry out tasks
Mobile motor could pave the way for robots to assemble complex structures — including other robots.
Lincoln Laboratory staff use race cars as a vehicle to teach coding
Laboratory staff teamed up with the Timothy Smith Network to offer a four-week coding course for middle school students.
New AI programming language goes beyond deep learning
General-purpose language works for computer vision, robotics, statistics, and more.
Study: Social robots can benefit hospitalized children
Interacting with a robotic teddy bear invented at MIT boosted young patients’ positive emotions, engagement, and activity level.
Want to learn how to train an artificial intelligence model? Ask a friend.
MIT Machine Intelligence Community introduces students to nuts and bolts of machine learning.
3Q: Julien de Wit on searching for red worlds in the northern skies
MIT has completed the installation of its newest exoplanet-hunting telescope, Artemis, in the Canary Islands, joining the SPECULOOS network.
Spotting objects amid clutter
New approach quickly finds hidden objects in dense point clouds, for use in driverless cars or work spaces with robotic assistants.
Engineers 3-D print flexible mesh for ankle and knee braces
Techniques could lead to personalized wearable and implantable devices.
3Q: David Mindell on his vision for human-centered robotics
Engineer and historian discusses how the MIT Schwarzman College of Computing might integrate technical and humanistic research and education.
Teaching artificial intelligence to connect senses like vision and touch
MIT CSAIL system can learn to see by touching and feel by seeing, suggesting future where robots can more easily grasp and recognize objects.
Algorithm tells robots where nearby humans are headed
A new tool for predicting a person’s movement trajectory may help humans and robots work together in close proximity.
Chip design drastically reduces energy needed to compute with light
Simulations suggest photonic chip could run optical neural networks 10 million times more efficiently than its electrical counterparts.
Preliminary reports examine options for MIT Schwarzman College of Computing
Working groups identify key ideas for new college; period of community feedback continues.