Researchers launched an in-house Data Science and Artificial Intelligence (DSAI) challenge to beat MIT’s machine-learning models for predicting clinical trial outcomes. The results are now available.

Integrating robots into a manufacturing system is often prohibitively expensive. A new approach could change that.

AI is a tool to get things done. To use it properly and generate value, organizations need the right capabilities — including a good understanding of data.

Cloud misconfigurations, more sophisticated ransomware, and exploitation of vendors are contributing to rising cyberattacks.

Organizations must base their cybersecurity strategies on resilience — the ability to weather an attack with minimal damage to data, finances, and reputation.

In a new book, MIT roboticist Daniela Rus looks at the powers and limitations of robots and how humans can work with them to unlock new capabilities.

Companies make common data science mistakes. Here’s an expert’s guide to what they are and how to avoid them.

MIT Sloan and CSAIL researchers apply artificial intelligence techniques to one of the largest datasets of clinical trial outcomes to handicap the drug and device approval process