A new generation of AIs that become increasingly general by producing their own training data

We are currently at the cusp of transitioning from “learning from data” to “learning what data to learn from” as the central focus of AI research.

If deep learning can be described as “Software 2.0”—software that programs itself based on example inputs/output pairs, then this promising, data-centric paradigm, in which software effectively improves itself by searching for its own training data, can be described as a kind of “Software²”. This paradigm inherits the benefits of Software 2.0 while improving on its core, data-bound weaknesses: While deep learning (Software 2.0) requires the programmer to manually provide training data for each new task, Software² recasts data as software that models or searches the world to produce its own, potentially unlimited, training tasks and data.