AI

Compression Progress, Pseudorandomness, & Hyperbolic Discounting

Abstract

General intelligence requires open-ended exploratory learning. The principle of compression progress proposes that agents should derive intrinsic reward from maximizing "interestingness", the first derivative of compression progress over the agent's history. Schmidhuber posits that such a drive can explain "essential aspects of ... curiosity, creativity, art, science, music, [and] jokes", implying that such phenomena might be replicated in an artificial general intelligence programmed with such a drive. I pose two caveats: 1) as pointed out by Rayhawk, not everything that can be considered "interesting" according to this definition is interesting to humans; 2) because of (irrational) hyperbolic discounting of future rewards, humans have an additional preference for rewards that are structured to prevent premature satiation, often superseding intrinsic preferences for compression progress.