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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
- 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
- “Periodic table of machine learning” could fuel AI discovery
After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, MIT researchers organized them into a “periodic table of machine learning” that can help scientists combine elements of different methods to improve algorithms or create new ones
- 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
- Accelerating scientific discovery with AI - MIT News
FutureHouse, co-founded by Sam Rodriques PhD ’19, has developed AI agents to automate some of the most critical steps on the path toward scientific progress
- Novel AI model inspired by neural dynamics from the brain
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial intelligence model inspired by neural oscillations in the brain, with the goal of significantly advancing how machine learning algorithms handle long sequences of data AI often struggles with analyzing complex information that unfolds over long periods of time, such as
- New AI tool generates realistic satellite images of future flooding
The AI-only method, in contrast, generated images of flooding in places where flooding is not physically possible The team’s method is a proof-of-concept, meant to demonstrate a case in which generative AI models can generate realistic, trustworthy content when paired with a physics-based model
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