MIT has developed a shape-changing antenna for more versatile sensing & communication compared to static antennas.
You can generate these durable, inexpensive metamaterial antennas using the researchers' complementary editing tool: bit.ly/4mTFdw5
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Ask us your questions about embodied intelligence or AI systems that interact w/the world. We’re featuring a few in an upcoming explainer w/MIT prof. Vincent Sitzmann.
For more on his work: www.vincentsitzmann.com/
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M.C. Escher's artwork famously features depth-defying optical illusions.
New MIT tool creates multi-dimensional representations of these "impossible objects" that users can relight, smooth, & solve geometry problems on w/o bending or cutting shapes: bit.ly/4oilDuZ
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Sneak peek of a new project out of the HCI Engineering Group here at CSAIL 👀
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Lung cancer is the leading cause of cancer-related deaths worldwide.
A deep learning model developed by MIT & MGH researchers called "Sybil" can accurately predict a patient’s risk of lung cancer up to 6 years in advance by analyzing a low-dose CT scan: bit.ly/45bcHyA
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Image generation systems normally use a tokenizer to compress & encode visual data, along w/a generator that combines & arranges these representations to create images.
MIT's new method creates, converts, & "inpaints" images w/o using a generator at all: bit.ly/40ZlKkU
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How do language models track dynamic scenarios, like completing code or guessing your responses?
MIT research finds that LMs don't track "state changes" step by step — they use mathematical shortcuts that we can control to boost LMs' prediction skills: bit.ly/4lJfYfK
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Can AI actually code for us?
MIT research reveals there’s a "long way to go" due to bottlenecks like assessment, codebase scale, & incorrect retrievals. The work reflects a vision to let humans focus on high-level design while routine work is automated: bit.ly/46g5uj2
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A new handheld interface from MIT gives anyone the ability to train a robot for tasks in fields like manufacturing.
The versatile tool can teach a robot new skills using one of three approaches: natural teaching, kinesthetic training, & teleoperation: bit.ly/4nTAw6F
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MIT CSAIL's "PhysicsGen" system helps robots handle items efficiently by customizing & multiplying the data they train on.
It turns a few VR demonstrations into thousands of simulations, potentially helping build huge datasets for dexterous robots: bit.ly/4nJl6ln
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The Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory (CSAIL) conducts research in all areas of computer science and AI, such as robotics, systems, theory, biology, machine learning, speech recognition, vision and graphics. Here you'll find videos showcasing the exciting research being done at CSAIL.
Information about accessibility can be found at accessibility.mit.edu/