The AI That Taught Itself: USC Researchers Show How Artificial Intelligence Can Learn What It Never Knew

Illustration: Midjourney For years, the guiding assumption of artificial intelligence has been simple: an AI is only as good as the data it has seen. Feed it more, train it longer, and it performs better. Feed it less, and it stumbles.  A new study from the USC Viterbi School of Engineering was accepted at the IEEE SoutheastCon 2026, taking place March 12-15. It suggests something far more surprising: with the right method in place, an AI model can dramatically improve its performance in territory it was barely trained on, pushing well past what its training data alone would ever allow. The method was developed by Minda Li, a USC Viterbi undergraduate who has been pursuing research since her freshman year, working alongside her advisor Bhaskar Krishnamachari*, a Faculty Fellow and Systems Professor in the Ming Hsieh Department of Electrical and Computer Engineering, with a joint appointment in the Thomas Lord Department of Computer Science at the USC Viterbi School of Engineering and the USC School of Advanced Computing. Together, they tested GPT-5’s ability to write code in Idris, an extraordinarily obscure programming language with a fraction of the online presence of mainstream languages like Python. The results were striking: by giving the AI feedback on its errors and letting it try again, Li  pushed the model’s success rate from a dismal 39% all the way to 96%. “Our AI tools are now able to transcend their initial training. Used to be, maybe a year or two ago, you wo...