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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
- How we really judge AI - MIT News
A new study finds people are more likely to approve of the use of AI in situations where its abilities are perceived as superior to humans’ and where personalization isn’t necessary
- 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
- What do we know about the economics of AI? - MIT News
Since much economic growth comes from tech innovation, the way societies use artificial intelligence is of keen interest to MIT Institute Professor Daron Acemoglu, who has published several papers on AI economics in recent months
- Q A: The climate impact of generative AI - MIT News
MIT Lincoln Laboratory Supercomputing Center staff member Vijay Gadepally discusses the climate impact of generative artificial intelligence and the tools his team are developing to improve data center efficiency
- 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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