Mastering Claude in 18 steps, v/
@AnatoliKopadze
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@mit_csail
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MIT’s CALM helps robots stay on task after disturbances
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Even when a robot is doing predictable, everyday tasks, it can struggle to adjust to disturbances like collisions. MIT’s "CALM" helps them stay on task by tracking the motions of a few human demos & averaging them into a path that’s easy to stick to: https://
bit.ly/3SY1dff -
Free MIT guide to key concepts of computer vision
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A free MIT guide to key concepts of computer vision: https://bit.ly/43Tn1vW
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Free MIT Deep Learning Course 2026 with Alexander Amini
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A free MIT course about deep learning, 2026 edition: https://t.co/dEgRHps7Ro
— MIT CSAIL (@MIT_CSAIL) 18 juin 2026
Here, MIT CSAIL researcher Alexander Amini discusses the differences between supervised, unsupervised, & reinforcement learning approaches (Lecture 5). pic.twitter.com/XQ8hKJ0l2sA free MIT course on deep learning, 2026 edition: https:// tinyurl.com/3v4x44zx Here, MIT CSAIL researcher Alexander Amini discusses the differences between supervised, unsupervised, and reinforcement learning approaches (Lecture 5).
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MIT recommends transparency and dialogue for medical AI
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Many AI tools impacting patient care could operate outside FDA oversight. To change this, MIT researchers recommend increased public disclosure, structured dialogue between industry and regulators, and
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Free 30-minute guide to mastering Claude Code
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A free 30-minute guide to mastering Claude Code: https://t.co/DBAkVyT2K1 pic.twitter.com/X1J8XP3vsT
— MIT CSAIL (@MIT_CSAIL) 6 juin 2026A free 30-minute guide to mastering Claude Code: https://
tinyurl.com/2s3pb6mr -
Collaborative Battleship shows AI better at answering than asking questions
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"Collaborative Battleship" game has revealed that AI agents are better at answering questions than asking them. CSAIL & SEAS had LMs play together, where Monte Carlo inference strategies helped small agents outpace the largest models at ~1% of the cost: https://
bit.ly/4afOE4T -
MIT Deep Learning Course: Breakthroughs in AI Applications
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Free MIT course breaks down deep learning: https://t.co/S9DmjSlveB
— MIT CSAIL (@MIT_CSAIL) 28 mai 2026
Here, MIT ass't prof. Sara Beery discusses how it's driven breakthroughs in areas like image generation, coding, & playing games (Lecture 1). pic.twitter.com/qXuJiI3MamFree MIT course breaks down deep learning: https://
bit.ly/4t9gHJL Here, MIT ass't prof. Sara Beery discusses how it's driven breakthroughs in areas like image generation, coding, & playing games (Lecture 1). -
MIT’s “Insum” speeds up einsum for sparse datasets
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MIT researchers developed “Insum,” a technique for speeding up computations on datasets replete w/zeros. It rewrites Einstein summation (“einsum”) operations to avoid inefficient handling of zeros, improving memory efficiency & performance: https://
bit.ly/4upJM5s
