Helping companies prioritize their cybersecurity investments
By securely aggregating sensitive data from cyber-attacks, the SCRAM platform from MIT CSAIL can quantify an organization’s level of security and suggest what to prioritize.
By securely aggregating sensitive data from cyber-attacks, the SCRAM platform from MIT CSAIL can quantify an organization’s level of security and suggest what to prioritize.
Honorees will engage in the life of the Institute through teaching, research, and other interactions with the MIT community.
New faculty in these areas will connect the MIT Schwarzman College of Computing and a department or school.
Co-design Center for Quantum Advantage and Quantum Systems Accelerator are funded by the U.S. Department of Energy to accelerate the development of quantum computers.
Researchers train a model to reach human-level performance at recognizing abstract concepts in video.
With creativity and hard work, the Institute is striving to provide the best possible experience for the Class of 2024.
IAIFI will advance physics knowledge — from the smallest building blocks of nature to the largest structures in the universe — and galvanize AI research innovation.
An artificial intelligence tool lets users edit generative adversarial network models with simple copy-and-paste commands.
Storage tool developed at MIT CSAIL adapts to what its datasets’ users want to search.
Through innovation in software and hardware, researchers move to reduce the financial and environmental costs of modern artificial intelligence.
Analysis shows requiring masks for public-facing U.S. business employees on April 1 would have saved tens of thousands of lives.
MIT Task Force on the Work of the Future examines job changes in the AV transition and how training can help workers move into careers that support mobility systems.
Machine learning system from MIT CSAIL can look at chest X-rays to diagnose pneumonia — and also knows when to defer to a radiologist.
A team from MIT helped create an image retrieval system to find the closest matches of paintings from different artists and cultures.
Recent advances give theoretical insight into why deep learning networks are successful.