A branch of machine learning, deep learning harnesses massive data and algorithms modeled loosely on how the brain processes information to make predictions. The class has been credited with helping to spread machine-learning tools into research labs across MIT.

MIT startup PatternEx starts with the assumption that algorithms can’t protect a system on their own. The company has developed a closed loop approach whereby machine-learning models flag possible attacks and human experts provide feedback. The feedback is then incorporated into the models, improving their ability to flag only the activity analysts care about in the future.

A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a new system that uses an existing technology called ground-penetrating radar (GPR) to send electromagnetic pulses underground that measure the area’s specific combination of soil, rocks, and roots.

Artificial intelligence is reshaping how we live, learn, and work, and this past fall, MIT undergraduates got to explore and build on some of the tools and coming out of research labs at MIT.

Founded by CEO Jon Garrity ’11 and CTO Will Vega-Brown ’11, SM ’13, Tagup is currently being used by energy companies to monitor approximately 60,000 pieces of equipment around North America and Europe.

For the first time, MIT researchers have enabled a soft robotic arm to understand its configuration in 3D space, by leveraging only motion and position data from its own “sensorized” skin.

A system created by MIT researchers could be used to automatically update factual inconsistencies in Wikipedia articles, reducing time and effort spent by human editors who now do the task manually.

“I never thought about the kilowatt-hours I was using. But this hackathon gave me a chance to look at my carbon footprint and find ways to trade a small amount of model accuracy for big energy savings,” says Mohammad Haft-Javaherian.