A team of scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic (J-Clinic) demonstrated a deep learning system to predict cancer risk using just a patient’s mammogram. The model showed significant promise and even improved inclusivity: It was equally accurate for both white and Black women, which is especially important given that Black women are 43 percent more likely to die from breast cancer.
CSAIL's Sabrina Neuman has found a way to fight the mismatch between a robot’s “mind” and body. The method, called robomorphic computing, uses a robot’s physical layout and intended applications to generate a customized computer chip that minimizes the robot’s response time.
Antonio Torralba, faculty head of Artificial Intelligence and Decision Making within the Department of Electrical Engineering and Computer Science (EECS) and the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science, has been selected as a 2021 Fellow by the Association for the Advancement of Artificial Intelligence (AAAI).
Modern health care has been reinvigorated by the widespread adoption of artificial intelligence. From speeding image analysis for radiology to advancing precision medicine for personalized care, AI has countless applications, but can it rise to the challenge in the fight against Covid-19?
In an update to a five-year project from CSAIL and the Senseable City Lab, researchers have been developing the world's first fleet of autonomous boats for the City of Amsterdam, the Netherlands, and have recently added a new, larger vessel to the group: “Roboat II.”
Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs. MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability.
Researchers at CSAIL recently made a major development in the area of lost languages: a new system that has been shown to be able to automatically decipher a lost language, without needing advanced knowledge of its relation to other languages.
A group led by researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) has developed a machine learning model that can look at an X-ray to quantify how severe the edema is, on a four-level scale ranging from 0 (healthy) to 3 (very, very bad). The system determined the right level more than half of the time, and correctly diagnosed level 3 cases 90 percent of the time.
