Injecting fairness into machine-learning models
A new technique boosts models’ ability to reduce bias, even if the dataset used to train the model is unbalanced.
Using artificial intelligence to find anomalies hiding in massive datasets
A new machine-learning technique could pinpoint potential power grid failures or cascading traffic bottlenecks in real time.
Deep-learning technique predicts clinical treatment outcomes
A new methodology simulates counterfactual, time-varying, and dynamic treatment strategies, allowing doctors to choose the best course of action.
More sensitive X-ray imaging
Improvements in the material that converts X-rays into light, for medical or industrial images, could allow a tenfold signal enhancement.
Can machine-learning models overcome biased datasets?
A model’s ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report.
Toward a stronger defense of personal data
Engineers build a lower-energy chip that can prevent hackers from extracting hidden information from a smart device.
Research advances technology of AI assistance for anesthesiologists
A new deep-learning algorithm trained to optimize doses of propofol to maintain unconsciousness during general anesthesia could augment patient monitoring.
An explorer in the sprawling universe of possible chemical combinations
Heather Kulik embraces computer models as “the only way to make a dent” in the vast number of potential materials that could solve important problems.
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.
Artificial intelligence system rapidly predicts how two proteins will attach
The machine-learning model could help scientists speed the development of new medicines.
Where did that sound come from?
MIT neuroscientists have developed a computer model that can answer that question as well as the human brain.
Demystifying machine-learning systems
A new method automatically describes, in natural language, what the individual components of a neural network do.
Deploying machine learning to improve mental health
MIT scientist Rosalind Picard collaborates with clinicians to develop tools for mental health care delivery.
Cynthia Breazeal named dean for digital learning at MIT
Overseeing business and research units across MIT Open Learning, Breazeal will focus on the future of digital technologies and their applications in education.