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- Massachusetts Institute of Technology - MIT News
An AI pipeline developed by CSAIL researchers enables unique hydrodynamic designs for bodyboard-sized vehicles that glide underwater and could help scientists gather marine data
- Explained: Generative AI’s environmental impact - MIT News
MIT News explores the environmental and sustainability implications of generative AI technologies and applications
- 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
- Teaching AI models what they don’t know - MIT News
A team of MIT researchers founded Themis AI to quantify artificial intelligence model uncertainty and address knowledge gaps
- 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
- New AI JetPack accelerates the entrepreneurial process
The MIT Entrepreneurship JetPack is a generative artificial intelligence tool that helps students navigate the 24-step Disciplined Entrepreneurship process developed by Trust Center’s managing director Bill Aulet
- The multifaceted challenge of powering AI - MIT News
The sudden need for more data centers to power AI presents a massive challenge to the technology and energy industries, government policymakers, and everyday consumers Researchers at the MIT Energy Initiative (MITEI) are exploring multiple facets of this problem
- “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
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