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- Massachusetts Institute of Technology - MIT News
MIT researchers “speak objects into existence” using AI and robotics The speech-to-reality system combines 3D generative AI and robotic assembly to create objects on demand
- 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
- 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
- Exploring how AI will shape the future of work - MIT News
What happens when AI begins making many of our decisions?” These are some of the questions MIT Sloan School of Management PhD candidate Benjamin Manning is researching Part of his work investigates how to design and evaluate artificial intelligence agents that act on behalf of people, and how their behavior shapes markets and institutions
- Massachusetts Institute of Technology - MIT News
A generative AI-driven system enables non-experts to design and build common objects like furniture simply by describing the objects with words
- 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
- 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
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