Leveraging language to understand machines
Master's students Irene Terpstra ’23 and Rujul Gandhi ’22 use language to design new integrated circuits and make it understandable to robots.
MIT-IBM Watson AI Lab
A flexible solution to help artists improve animation
This new method draws on 200-year-old geometric foundations to give artists control over the appearance of animated characters.
Image recognition accuracy: An unseen challenge confounding today’s AI
“Minimum viewing time” benchmark gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification.
New method uses crowdsourced feedback to help train robots
Human Guided Exploration (HuGE) enables AI agents to learn quickly with some help from humans, even if the humans make mistakes.
Students pitch transformative ideas in generative AI at MIT Ignite competition
Twelve teams of students and postdocs across the MIT community presented innovative startup ideas with potential for real-world impact.
Technique enables AI on edge devices to keep learning over time
With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.
New technique helps robots pack objects into a tight space
Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.
A more effective experimental design for engineering a cell into a new state
By focusing on causal relationships in genome regulation, a new AI method could help scientists identify new immunotherapy techniques or regenerative therapies.
How machine learning models can amplify inequities in medical diagnosis and treatment
MIT researchers investigate the causes of health-care disparities among underrepresented groups.
AI models are powerful, but are they biologically plausible?
A new study bridging neuroscience and machine learning offers insights into the potential role of astrocytes in the human brain.