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.

“It’s important to have balanced, high-throughput routing in PCNs to ensure the money that users put into joint accounts is used efficiently,” says first author Vibhaalakshmi Sivaraman, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL).

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.

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.

A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) tested the boundaries of text. They came up with “TextFooler,” a general framework that can successfully attack natural language processing (NLP) systems — the types of systems that let us interact with our Siri and Alexa voice assistants — and “fool” them into making the wrong predictions. 

“Most updated digital maps are from places that big companies care the most about. If you’re in places they don’t care about much, you’re at a disadvantage with respect to the quality of map,” says co-author Sam Madden, a professor in the Department of Electrical Engineering and Computer Science (EECS) and a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “Our goal is to automate the process of generating high-quality digital maps, so they can be available in any country.”