Scaling audio-visual learning without labels
A new multimodal technique blends major self-supervised learning methods to learn more similarly to humans.
Algorithms
Driven to driverless
Cindy Alejandra Heredia’s journey from Laredo, Texas, took her to leading the MIT autonomous vehicle team and to an MBA from MIT Sloan.
A more effective way to train machines for uncertain, real-world situations
Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own.
Probabilistic AI that knows how well it’s working
It’s more important than ever for artificial intelligence to estimate how accurately it is explaining data.
Using data to write songs for progress
Senior Ananya Gurumurthy adds her musical talents to her math and computer science studies to advocate using data for social change.
Is medicine ready for AI? Doctors, computer scientists, and policymakers are cautiously optimistic
With the artificial intelligence conversation now mainstream, the 2023 MIT-MGB AI Cures conference saw attendance double from previous years.
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.
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.
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.