Compression Progress, Pseudorandomness, & Hyperbolic Discounting

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. Schmid-huber 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.