Modern food systems may look stable on the surface, but they are increasingly dependent on digital systems that can quietly become a major point of failure. Today, food must be “recognized” by databases and automated platforms to be transported, sold, or even released, meaning that if systems go down, food can effectively become unusable—even when it’s physically available.
Choosing the right method for multimodal AI—systems that combine text, images, and more—has long been trial and error. Emory physicists created a unifying mathematical framework that shows many AI techniques rely on the same core idea: compress data while preserving what’s most predictive. Their “control knob” approach helps researchers design better algorithms, use less data, and avoid wasted computing power. The team believes it could pave the way for more accurate, efficient, and environmentally friendly AI.