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- Massachusetts Institute of Technology - MIT News
Helping AI agents search to get the best results out of large language models EnCompass executes AI agent programs by backtracking and making multiple attempts, finding the best set of outputs generated by an LLM
- 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
- Responding to the climate impact of generative AI - MIT News
MIT experts discuss strategies and innovations aimed at mitigating the amount of greenhouse gas emissions generated by the training, deployment, and use of AI systems, in the second in a two-part series on the environmental impacts of generative artificial intelligence
- What does the future hold for generative AI? - MIT News
Hundreds of scientists, business leaders, faculty, and students shared the latest research and discussed the potential future course of generative AI advancements during the inaugural symposium of the MIT Generative AI Impact Consortium (MGAIC) on Sept 17
- 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 advance automated interpretability in AI models
MAIA is a multimodal agent for neural network interpretability tasks developed at MIT CSAIL It uses a vision-language model as a backbone and equips it with tools for experimenting on other AI systems
- 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
- Explained: Generative AI | MIT News | Massachusetts Institute of Technology
What do people mean when they say “generative AI,” and why are these systems finding their way into practically every application imaginable? MIT AI experts help break down the ins and outs of this increasingly popular, and ubiquitous, technology
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