A new programming language for high-performance computers
With a tensor language prototype, “speed and correctness do not have to compete ... they can go together, hand-in-hand.”
New device detects heat strain in military trainees
In the intensity of basic training, a smartwatch-based system warns recruits when they are at risk of heat injury.
The downside of machine learning in health care
Assistant Professor Marzyeh Ghassemi explores how hidden biases in medical data could compromise artificial intelligence approaches.
2021-22 Takeda Fellows: Leaning on AI to advance medicine for humans
New fellows are working on electronic health record algorithms, remote sensing data related to environmental health, and neural networks for the development of antibiotics.
Deploying machine learning to improve mental health
MIT scientist Rosalind Picard collaborates with clinicians to develop tools for mental health care delivery.
3 Questions: Anuradha Annaswamy on building smart infrastructures
Senior research scientist and her team are designing intelligent systems that could someday transform the way we travel and consume energy.
Computing for ocean environments
MIT ocean and mechanical engineers are using advances in scientific computing to address the ocean’s many challenges, and seize its opportunities.
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.
Scientists make first detection of exotic “X” particles in quark-gluon plasma
The findings could redefine the kinds of particles that were abundant in the early universe.
When should someone trust an AI assistant’s predictions?
Researchers have created a method to help workers collaborate with artificial intelligence systems.
A new way to perform "general inverse design" with high accuracy
SMART breakthrough could help develop technologies that can identify materials according to desired properties for specific applications.
Physics and the machine-learning “black box”
In 2.C01, George Barbastathis demonstrates how mechanical engineers can use their knowledge of physical systems to keep algorithms in check and develop more accurate predictions.
Tackling hard computational problems
David Gamarnik has developed a new tool, the overlap gap property, for understanding computational problems that appear intractable.
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.
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.