Understanding Generalization Requires Universal Induction
Classical statistical theory is insufficient to explain the successes of general-purpose AI models, because it depends on handcrafted inductive biases that it cannot justify. No Free Lunch (NFL) theorems force any learner that beats chance on some environments to underperform on others. We might hope that past experience informs which environments to expect, but NFL applies equally to meta-learning. Thus, any method that makes meaningful predictions necessarily begins with an inductive bias external to the data. Choosing to bias toward short programs yields Solomonoff induction (SI), whose performance is competitive against all computable learners - albeit up to "constants" that become la...
| Authors | Aram Ebtekar, Marcus Hutter, Danica J. Sutherland |
|---|---|
| Primary category | stat.ML |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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