Choosing what is worth learning next [ftip-00N1]
Choosing what is worth learning next [ftip-00N1]
A contributor may offer a promising question or a direction of inquiry without knowing its final answer. The difficulty is prospective: the learner must choose where to spend effort before observing how much that effort will teach it. Surprise can prioritize noise, while measured learning progress arrives after the investment.
Herrmann and Schmidhuber model interestingness through complexity–runtime profiles and future compression progress under specified Length, Algorithmic and Speed priors. Their analysis and finite enumeration experiments study when present structure predicts further compressibility. Section 4.4 limits the formal correspondence through Busy Beaver time scales; a long plateau does not exclude a later breakthrough. The analysis supplies neither an affordable selector for frontier models nor a lower bound over their possible research strategies.
For the contribution model, a direction's quality needs an independent property: for example, a reduction exposing a learnable subproblem with bounded translation cost. Calling a direction “interesting” cannot supply that property for free. An informative experiment would compare equal-cost choices by the recipient, a contributor and a shuffled-direction control, then measure verified progress and fresh-task performance after a fixed learning budget. A plausible prediction is that a useful structural selector outperforms mere surprise when high-surprise distractors are present. A successful internal selector weakens the proposed external advantage for that specification.