In a new MIT course co-taught by EECS and philosophy professors, students tackle moral dilemmas of the digital age.
SketchAgent, a drawing system developed by MIT CSAIL researchers, sketches up concepts stroke-by-stroke, teaching language models to visually express concepts on their own and collaborate with humans.
Today’s security systems usually fall into one of two categories: human or machine. So-called “analyst-driven solutions” rely on rules created by living experts and therefore miss any attacks that don’t match the rules. Meanwhile, today’s machine-learning approaches rely on “anomaly detection,” which tends to trigger false positives that both create distrust of the system and end up having to be investigated by humans, anyway.But what if there were a solution that could merge those two worlds? What would it look like?
John “Jack” Holloway, Project MAC and MIT AI Laboratory researcher, passed away on July 9 at 77 years old.
MIT professor and CSAIL member Dina Katabi earns SIGCOMM Lifetime Achievement Award for her innovative contributions to both the Internet and wireless networks.
Game-playing artificial intelligence has proved to be a game-changer for even the most seasoned veterans.
Inspired by the mechanics of the human vocal tract, MIT CSAIL researchers’ AI model can produce and understand vocal imitations of everyday sounds. Their method could help build new sonic interfaces for entertainment and education.
Researchers use multiple AI models to collaborate, debate, and improve their reasoning abilities to advance the performance of LLMs while increasing accountability and factual accuracy.
MIT CSAIL Principal Research Scientist Una-May O’Reilly discusses how she develops agents that reveal AI models’ security weaknesses before hackers do.
Human Guided Exploration (HuGE) enables AI agents to learn quickly with some help from humans, even if the humans make mistakes.