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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. "We wondered if there was a simpler way than starting from scratch each time you confront a problem in multimodal AI." A unifying framework To address this, the team developed a mathematical framework that links the design of loss functions directly to decisions about which information should be preserved and which can be ignored. "Our goal is to help people to design AI models that are tailored to the problem that they are trying to solve," he says, "while also allowing them to understand how and why each part of the model is working." AI-system developers can use the framework to propose new algorithms, to predict which ones might work, to estimate the needed data for a particular multimodal algorithm, and to anticipate when it might fail. "Just as important," Nemenman says, "it may let us design new AI methods that are more accurate, efficient and trustworthy." A physics approach The researchers brought a unique perspective to the problem of optimizing the design process for multimodal AI systems."