In our current age of artificial intelligence, computers can generate their own “art” by way of diffusion models, iteratively adding structure to a noisy initial state until a clear image or video emerges. Diffusion models have suddenly grabbed a seat at everyone’s table: Enter a few words and experience instantaneous, dopamine-spiking dreamscapes at the intersection of reality and fantasy. Behind the scenes, it involves a complex, time-intensive process requiring numerous iterations for the algorithm to perfect the image.

Imagine yourself glancing at a busy street for a few moments, then trying to sketch the scene you saw from memory. Most people could draw the rough positions of the major objects like cars, people, and crosswalks, but almost no one can draw every detail with pixel-perfect accuracy. The same is true for most modern computer vision algorithms: They are fantastic at capturing high-level details of a scene, but they lose fine-grained details as they process information.

Artists who bring to life heroes and villains in animated movies and video games could have more control over their animations, thanks to a new technique introduced by MIT researchers.

By synchronizing media streams transmitted from the cloud to two devices, researchers could improve cloud gaming and AR/VR applications.

With FabO, PhD student Dishita Turakhia wants to empower students to learn digital fabrication by making video game objects and characters come alive.

MIT EECS professor Jonathan Ragan-Kelley received ACM SIGGRAPH'S 2021 Significant New Researcher Award, for his “outstanding contributions to systems and compilers in rendering and computational photography,” according to ACM’s press release. 

The national dialogue on race has progressed powerfully and painfully in the past year, and issues of racial bias in the news have become ubiquitous. However, for over a decade, researchers from MIT’s Imagination, Computation, and Expression Laboratory (ICE Lab) have been developing systems to model, simulate, and analyze such issues of identity. 

CSAIL will be launching a new initiative focused on machine learning applications. MachineLearningApplications@CSAIL, or “MLA@CSAIL”, will focus on current challenges in machine learning to prepare industry members for digital transformation in their workplaces.

View the annual list from MIT Technology Review that includes technologies that "will make a real difference in solving important problems."