Almost every artificial intelligence system we know begins in the same way: it is exposed to vast amounts of information from the real world—books, websites, images and recordings—and trained to identify patterns within it. A new study involving researchers from Tel Aviv University and Stanford University raises another possibility: Can a system develop general learning capabilities even when all the data it trains on is generated entirely from scratch within the system itself?