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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
- “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
- 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
- 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
- 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
- MIT researchers develop an efficient way to train more reliable AI . . .
MIT researchers developed an efficient approach for training more reliable reinforcement learning models, focusing on complex tasks that involve variability This could enable the leverage of reinforcement learning across a wide range of applications
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