Toward artificial intelligence that learns to write code
Researchers combine deep learning and symbolic reasoning for a more flexible way of teaching computers to program.
MIT-IBM Watson AI Lab
Q&A: Phillip Isola on the art and science of generative models
Image-translation pioneer discusses the past, present, and future of generative adversarial networks, or GANs.
Teaching language models grammar really does make them smarter
Researchers submit deep learning models to a set of psychology tests to see which ones grasp key linguistic rules.
Smarter training of neural networks
MIT CSAIL project shows the neural nets we typically train contain smaller “subnetworks” that can learn just as well, and often faster.
Improving security as artificial intelligence moves to smartphones
Researchers unveil a tool for making compressed deep learning models less vulnerable to attack.
Giving robots a better feel for object manipulation
Model improves a robot’s ability to mold materials into shapes and interact with liquids and solid objects.
Teaching machines to reason about what they see
Researchers combine statistical and symbolic artificial intelligence techniques to speed learning and improve transparency.
Exploring the nature of intelligence
Undergraduate research projects show how students are advancing research in human and artificial intelligence, and applying intelligence tools to other disciplines.
Learning to teach to speed up learning
An algorithm that teaches robot agents how to exchange advice to complete a task helps them learn faster.
Artificial intelligence in action
At the MIT-IBM Watson AI Lab, researchers are training computers to recognize dynamic events.
3Q: Anantha Chandrakasan on new MIT–IBM Watson AI Lab
Lab seeks to expand the boundaries of research on artificial intelligence.