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- Artificial intelligence | MIT News | Massachusetts Institute of Technology
Generative AI tool helps 3D print personal items that sustain daily use “MechStyle” allows users to personalize 3D models, while ensuring they’re physically viable after fabrication, producing unique personal items and assistive technology
- 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
- 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
- What do we know about the economics of AI? - MIT News
Since much economic growth comes from tech innovation, the way societies use artificial intelligence is of keen interest to MIT Institute Professor Daron Acemoglu, who has published several papers on AI economics in recent months
- 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
- 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
- “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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