MIT researchers make language models scalable self-learners
The scientists used a natural language-based logical inference dataset to create smaller language models that outperformed much larger counterparts.
3 Questions: Jacob Andreas on large language models
The CSAIL scientist describes natural language processing research through state-of-the-art machine-learning models and investigation of how language can enhance other types of artificial intelligence.
Drones navigate unseen environments with liquid neural networks
MIT researchers exhibit a new advancement in autonomous drone navigation, using brain-inspired liquid neural networks that excel in out-of-distribution scenarios.
MIT CSAIL researchers discuss frontiers of generative AI
Experts convene to peek under the hood of AI-generated code, language, and images as well as its capabilities, limitations, and future impact.
A four-legged robotic system for playing soccer on various terrains
“DribbleBot” can maneuver a soccer ball on landscapes such as sand, gravel, mud, and snow, using reinforcement learning to adapt to varying ball dynamics.
Large language models are biased. Can logic help save them?
MIT researchers trained logic-aware language models to reduce harmful stereotypes like gender and racial biases.
Self-transforming robot blocks jump, spin, flip, and identify each other
Developed at MIT’s Computer Science and Artificial Intelligence Laboratory, robots can self-assemble to form various structures with applications including inspection.
Using machine learning to estimate risk of cardiovascular death
CSAIL system uses a patient’s ECG signal to estimate potential for cardiovascular death.
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.
Robot hand is soft and strong
Gripper device inspired by “origami magic ball” can grasp wide array of delicate and heavy objects.