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.
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 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.
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.
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.
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.
MIT researchers at the School of Architecture and Planning developed a speech-to-reality system that combines generative AI, natural language processing, and robotic assembly to fabricate physical objects from spoken prompts.
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.
