A better way to study ocean currents
A new machine-learning model makes more accurate predictions about ocean currents, which could help with tracking plastic pollution and oil spills, and aid in search and rescue.
MIT Schwarzman College of Computing
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.
Study: AI models fail to reproduce human judgements about rule violations
Models trained using common data-collection techniques judge rule violations more harshly than humans would, researchers report.
Success at the intersection of technology and finance
Citadel founder and CEO Ken Griffin visits MIT, discusses how technology will continue to transform trading and investing.
Training machines to learn more like humans do
Researchers identify a property that helps computer vision models learn to represent the visual world in a more stable, predictable way.
Researchers create a tool for accurately simulating complex systems
The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.
AI system can generate novel proteins that meet structural design targets
These tunable proteins could be used to create new materials with specific mechanical properties, like toughness or flexibility.
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.