Explained: Generative AI | MIT News | Massachusetts Institute of Technology What do people mean when they say “generative AI,” and why are these systems finding their way into practically every application imaginable? MIT AI experts help break down the ins and outs of this increasingly popular, and ubiquitous, technology
AI Assist - Stack Overflow stackoverflow ai is an AI-powered search and discovery tool designed to modernize the Stack Overflow experience by helping developers get answers instantly, learn along the way and provide a path into the community
AI tool generates high-quality images faster than state-of-the-art . . . A hybrid AI approach known as hybrid autoregressive transformer can generate realistic images with the same or better quality than state-of-the-art diffusion models, but that runs about nine times faster and uses fewer computational resources The new tool uses an autoregressive model to quickly capture the big picture and then a small diffusion model to refine the details of the image
Creating AI that matters - MIT News The MIT-IBM Watson AI Lab bridges research and deployment in AI through advances like smaller, efficient foundation models, vision and multimodal systems, and causal discovery
What does the future hold for generative AI? - MIT News Hundreds of scientists, business leaders, faculty, and students shared the latest research and discussed the potential future course of generative AI advancements during the inaugural symposium of the MIT Generative AI Impact Consortium (MGAIC) on Sept 17
Can AI really code? Study maps the roadblocks to autonomous software . . . An AI that can shoulder the grunt work — and do so without introducing hidden failures — would free developers to focus on creativity, strategy, and ethics” says Gu “But that future depends on acknowledging that code completion is the easy part; the hard part is everything else Our goal isn’t to replace programmers It’s to
Introducing the MIT Generative AI Impact Consortium The MIT Generative AI Impact Consortium is a collaboration between MIT, founding member companies, and researchers across disciplines who aim to develop open-source generative AI solutions, accelerating innovations in education, research, and industry
Improving AI models’ ability to explain their predictions A new technique transforms any computer vision model into one that can explain its predictions using a set of concepts a human could understand The method generates more appropriate concepts that boost the accuracy of the model