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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
- 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
- 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
- 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
- Explained: Generative AI’s environmental impact - MIT News
MIT News explores the environmental and sustainability implications of generative AI technologies and applications
- AI generates high-quality images 30 times faster in a single step
A new distribution matching distillation (DMD) technique merges GAN principles with diffusion models, achieving 30x faster high-quality image generation in a single computational step and enhancing tools like Stable Diffusion and DALL-E
- AI simulation gives people a glimpse of their potential future self
The AI system uses this information to create what the researchers call “future self memories” which provide a backstory the model pulls from when interacting with the user For instance, the chatbot could talk about the highlights of someone’s future career or answer questions about how the user overcame a particular challenge
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