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- Massachusetts Institute of Technology - MIT News
Researchers present bold ideas for AI at MIT Generative AI Impact Consortium kickoff event Presentations targeted high-impact intersections of AI and other areas, such as health care, business, and education
- Explained: Generative AI’s environmental impact - MIT News
MIT News explores the environmental and sustainability implications of generative AI technologies and applications
- 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
- Algorithms and AI for a better world - MIT News
MIT Assistant Professor Manish Raghavan uses computational techniques to push toward better solutions to long-standing societal problems
- Graph-based AI model maps the future of innovation - MIT News
The new AI approach uses graphs based on methods inspired by category theory as a central mechanism to understand symbolic relationships in science This Illustration shows one such graph and how it maps key points of related ideas and concepts
- MIT researchers introduce generative AI for databases
Researchers from MIT and elsewhere developed an easy-to-use tool that enables someone to perform complicated statistical analyses on tabular data using just a few keystrokes Their method combines probabilistic AI models with the programming language SQL to provide faster and more accurate results than other methods
- “An AI future that honors dignity for everyone” - MIT News
Ben Vinson III, president of Howard University, made a compelling call for AI to be “developed with wisdom,” as he delivered MIT’s annual Karl Taylor Compton Lecture
- 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
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