Abstract
I have argued that a simple but general formal theory of creativity explains many essential aspects of intelligence including science, art, music, humor. It is based on the concept of maximizing reward for the creation or discovery of novel patterns allowing for improved data compression or prediction. Here I discuss what kind of general bias towards algorithmic regularities we insert into our robots by implementing the principle, why that bias is good, and how the approach greatly generalizes the field of active learning. I emphasize the importance of limited computational resources for online prediction and compression, and provide discrete and continuous time formulations for ongoing work on building an Artificial General Intelligence (AGI) based on variants of the artificial creativity framework.