The downside of machine learning in health care
Assistant Professor Marzyeh Ghassemi explores how hidden biases in medical data could compromise artificial intelligence approaches.
Computer Science and Artificial Intelligence Laboratory (CSAIL)
Artificial intelligence system rapidly predicts how two proteins will attach
The machine-learning model could help scientists speed the development of new medicines.
Invisible machine-readable labels that identify and track objects
An MIT team develops 3D-printed tags to classify and store data on physical objects.
Demystifying machine-learning systems
A new method automatically describes, in natural language, what the individual components of a neural network do.
A new language for quantum computing
Twist is an MIT-developed programming language that can describe and verify which pieces of data are entangled to prevent bugs in a quantum program.
Seeing into the future: Personalized cancer screening with artificial intelligence
Scientists demonstrate that AI-risk models, paired with AI-designed screening policies, can offer significant and equitable improvements to cancer screening.
Brave Behind Bars: Prison education program focuses on computing skills for women
MIT computer scientists and mathematicians offer an introductory computing and career-readiness program for incarcerated women in New England.
When should someone trust an AI assistant’s predictions?
Researchers have created a method to help workers collaborate with artificial intelligence systems.
How well do explanation methods for machine-learning models work?
Researchers develop a way to test whether popular methods for understanding machine-learning models are working correctly.
The promise and pitfalls of artificial intelligence explored at TEDxMIT event
MIT scientists discuss the future of AI with applications across many sectors, as a tool that can be both beneficial and harmful.
Q&A: Cathy Wu on developing algorithms to safely integrate robots into our world
Assistant professor of civil engineering describes her career in robotics as well as challenges and promises of human-robot interactions.
From counting blood cells to motion capture, sensors drive patient-centered research
SENSE.nano symposium highlights the importance of sensing technologies in medical studies.
Nonsense can make sense to machine-learning models
Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.
Machine-learning system flags remedies that might do more harm than good
The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.
Machines that see the world more like humans do
A new “common-sense” approach to computer vision enables artificial intelligence that interprets scenes more accurately than other systems do.