Image
 EECS faculty and CSAIL principal investigators Sara Beery, Marzyeh Ghassemi, and Yoon Kim (Credit: MIT EECS).
CSAIL article

Sara Beery, Marzyeh Ghassemi, and Yoon Kim, EECS faculty and CSAIL principal investigators, were awarded AI2050 Early Career Fellowships earlier this week for their pursuit of “bold and ambitious work on hard problems in AI.” They received this honor from Schmidt Futures, Eric and Wendy Schmidt’s philanthropic initiative that aims to accelerate scientific innovation.

Image
alt="The “Diffusion Forcing” method can sort through noisy data and reliably predict the next steps in a task, helping a robot complete manipulation tasks, for example. In one experiment, it helped a robotic arm rearrange toy fruits into target spots on circular mats despite starting from random positions and visual distractions (Credits: Mike Grimmett/MIT CSAIL)."
CSAIL article

In the current AI zeitgeist, sequence models have skyrocketed in popularity for their ability to analyze data and predict what to do next. For instance, you’ve likely used next-token prediction models like ChatGPT, which anticipate each word (token) in a sequence to form answers to users’ queries. There are also full-sequence diffusion models like Sora, which convert words into dazzling, realistic visuals by successively “denoising” an entire video sequence

Image
alt="The “Faces in Things” dataset is a comprehensive, human-labeled collection of over 5,000 pareidolic images. The research team trained face-detection algorithms to see faces in these pictures, giving insight into how humans learned to recognize faces within their surroundings (Credits: Alex Shipps/MIT CSAIL)."
CSAIL article

In 1994, Florida jewelry designer Diana Duyser discovered what she believed to be the Virgin Mary’s image in a grilled cheese sandwich, which she preserved and later auctioned for $28,000. But how much do we really understand about pareidolia, the phenomenon of seeing faces and patterns in objects when they aren’t really there?