Computer scientists including @danielkhashabi.bsky.social, @aliu33.bsky.social, and @mikeschatz.bsky.social have proved that in-context learning is a general capability that emerges whenever a large AI model is trained to predict the next element with ANY sufficiently complex sequence data.
Their work, which proves that in-context learning is not tied to language, appears in Transactions on Machine Learning Research.
