Distinct AI Models Seem To Converge On How They Encode Reality

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"... Researchers investigate such questions by peering inside AI systems and studying how they represent scenes and sentences. A growing body of research has found that different AI models can develop similar representations, even if they’re trained using different datasets or entirely different data types. What’s more, a few studies have suggested that those representations are growing more similar as models grow more capable. In a 2024 paper, four AI researchers at the Massachusetts Institute of Technology argued that these hints of convergence are no fluke. Their idea, dubbed the Platonic representation hypothesis, has inspired a lively debate among researchers and a slew of follow-up work. ...

“Why do the language model and the vision model align? Because they’re both shadows of the same world,”...

Researchers began to explore representational similarity among AI models with this approach in the mid-2010s and found that different models’ representations of the same concepts were often similar, though far from identical. Intriguingly, a few studies found that more powerful models seemed to have more similarities in their representations than weaker ones. One 2021 paper dubbed this the “Anna Karenina scenario(opens a new tab),” a nod to the opening line of the classic Tolstoy novel. Perhaps successful AI models are all alike, and every unsuccessful model is unsuccessful in its own way. ..."

Distinct AI Models Seem To Converge On How They Encode Reality | Quanta Magazine