An MIT/IBM system could help artists and designers make quick tweaks to visuals while also helping researchers identify “fake” images.
Researchers used a powerful deep-learning model to extract important data from electronic health records that could assist with personalized medicine.
These models, which can predict a patient’s race, gender, and age, seem to use those traits as shortcuts when making medical diagnoses.
Project Gutenberg, MIT, and Microsoft collaborate to expand access to classic literature.
Designed to ensure safer skies, “Air-Guardian” blends human intuition with machine precision, creating a more symbiotic relationship between pilot and aircraft.
For the first time, researchers use a combination of MEG and fMRI to map the spatio-temporal human brain dynamics of a visual image being recognized.
Selecting the right method gives users a more accurate picture of how their model is behaving, so they are better equipped to correctly interpret its predictions.
The event welcomed graduate students and postdocs of historically underrepresented genders. Panelists advised the Rising Stars on how to pursue academic careers in electrical engineering, computer science, and AI and decision-making.
The framework helps clinicians choose phrases that more accurately reflect the likelihood that certain conditions are present in X-rays.
A multimodal system uses models trained on language, vision, and action data to help robots develop and execute plans for household, construction, and manufacturing tasks.