How to predict the behavior of molecules from scant data
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
Read More
machine learning
Clearing up a robot’s fuzzy memory
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
Read MoreMaking machines that can listen
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Read MoreQ&A: How AI came to model the “messy” human mind
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Read MoreThis design software reimagines everyday objects as self-aware devices
“X-Hinges” helps design and 3D print items like lamps and gloves with an interactive twist: automatically embedded sensors that perceive and adaptively respond to human contact.
Read MoreWhy teaching AI about symmetry could make prenatal diagnosis more reliable
A model developed by researchers at MIT and Boston Children’s Hospital adjusts scans on the fly to track fetal movement, potentially sparing families repeat hospital visits.
Read MoreGiving AI a “crystal clear” view of the materials world
New research can help AI models understand crystalline materials in 3D and reason about their properties.
Read MoreAI model decodes the language cells use to communicate
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Read MoreResearchers improve weather forecasts looking two to six weeks out
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Read MoreHow artificial neural networks build their own learning machinery
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Read More
You must be logged in to post a comment.