Reducing false positives in credit card fraud detection
Model extracts granular behavioral patterns from transaction data to more accurately flag suspicious activity.
Model extracts granular behavioral patterns from transaction data to more accurately flag suspicious activity.
National Academies report cites need for strong leadership and cultural change; will be focus of upcoming MIT panel discussion.
As delivery logistics become more challenging with expanding e-commerce, planners such as Matthias Winkenbach are offering solutions.
Neural network learns speech patterns that predict depression in clinical interviews.
Stimulating the brain’s caudate nucleus generates a negative outlook that clouds decision-making.
Personalized machine-learning models capture subtle variations in facial expressions to better gauge how we feel.
The McKnight technology award supports scientists using novel and creative approaches to understanding brain function.
Computer scientists find that physicians’ “gut feelings” influence how many tests they order for patients.
Given a video of a musical performance, CSAIL’s deep-learning system can make individual instruments louder or softer.
Machine learning network offers personalized estimates of children’s behavior.
Wireless smart-home system from the Computer Science and Artificial Intelligence Laboratory could monitor diseases and help the elderly “age in place.”
Scientists pinpoint neural interactions that are necessary for observational learning.
Neuroscientist honored for his work on the biology of neural stem cells and neural circuits that control emotional behaviors.
Startup’s platform crunches anonymized smartphone GPS data to understand how people shop, work, and live.
Children as young as 3 have brain network devoted to interpreting thoughts of other people.