Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world’s most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models.
MIT researchers have designed a scalable system that secures the metadata — such as who’s corresponding and when — of millions of users in communications networks, to help protect the information against possible state-level surveillance.
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.
