New hope for early pancreatic cancer intervention via AI-based risk prediction
MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.
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MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.
The MIT Orbital Capacity Assessment Tool lets users model the long-term future space environment.
A multimodal system uses models trained on language, vision, and action data to help robots develop and execute plans for household, construction, and manufacturing tasks.
Computer vision enables contact-free 3D printing, letting engineers print with high-performance materials they couldn’t use before.
Researchers use synthetic data to improve a model’s ability to grasp conceptual information, which could enhance automatic captioning and question-answering systems.
“Lightning” system connects photons to the electronic components of computers using a novel abstraction, creating the first photonic computing prototype to serve real-time machine-learning inference requests.
MIT researchers develop a protocol to extend the life of quantum coherence.
With a new, user-friendly interface, researchers can quickly design many cellular metamaterial structures that have unique mechanical properties.
“PhotoGuard,” developed by MIT CSAIL researchers, prevents unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models.
BioAutoMATED, an open-source, automated machine-learning platform, aims to help democratize artificial intelligence for research labs.
Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own.
Researchers create a new simulation tool for robots to manipulate complex fluids in a step toward helping them more effortlessly assist with daily tasks.
“DribbleBot” can maneuver a soccer ball on landscapes such as sand, gravel, mud, and snow, using reinforcement learning to adapt to varying ball dynamics.
Codon compiles Python code to run more efficiently and effectively while allowing for customization and adaptation to various domains.
The chip, which can decipher any encoded signal, could enable lower-cost devices that perform better while requiring less hardware.