Artificial intelligence is increasingly used to integrate and analyze multiple types of data formats, such as text, images, audio and video. One challenge slowing advances in multimodal AI, however, is the process of choosing the algorithmic method best aligned to the specific task an AI system needs to perform.
Scientists have developed a unified view of AI methods aimed at systemizing this process. The Journal of Machine Learning Research published the new framework for deriving algorithms, developed by former Emory graduate student, Eslam Abdelaleem, postdoc Michael Martini, and Prof. Ilya Nemenman.
“We found that many of today’s most successful AI methods boil down to a single, simple idea — compress multiple kinds of data just enough to keep the pieces that truly predict what you need,” says Ilya Nemenman, Emory professor of physics and senior author of the paper. “This gives us a kind of ‘periodic table’ of AI methods. Different methods fall into different cells, based on which information a method’s loss function retains or discards.”
“Our approach is a generalized, principled one,” adds Eslam Abdelaleem, first author of the paper. Abdelaleem took on the project as an Emory PhD candidate in physics before graduating in May and joining Georgia Tech as a postdoctoral fellow.
Read the full story here: https://news.emory.edu/stories/2025/12/er_ai_methods_10-12-2025/story.html