Slides in PDF - of Marcus Hutter
Slides in PDF - of Marcus Hutter
Slides in PDF - of Marcus Hutter
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<strong>Marcus</strong> <strong>Hutter</strong> - 64 - Universal Induction & Intelligence<br />
Universal Inductive Inference: Summary<br />
Universal Solomon<strong>of</strong>f prediction solves/avoids/meliorates many problems<br />
<strong>of</strong> (Bayesian) <strong>in</strong>duction. We discussed:<br />
+ general total bounds for generic class, prior, and loss,<br />
+ i.i.d./universal-specific <strong>in</strong>stantaneous and future bounds,<br />
+ the D n bound for cont<strong>in</strong>uous classes,<br />
+ <strong>in</strong>difference/symmetry pr<strong>in</strong>ciples,<br />
+ the problem <strong>of</strong> zero p(oste)rior & confirm. <strong>of</strong> universal hypotheses,<br />
+ reparametrization and regroup<strong>in</strong>g <strong>in</strong>variance,<br />
+ the problem <strong>of</strong> old evidence and updat<strong>in</strong>g,<br />
+ that M works even <strong>in</strong> non-computable environments,<br />
+ how to <strong>in</strong>corporate prior knowledge,<br />
− the prediction <strong>of</strong> short sequences,<br />
− the constant fudges <strong>in</strong> all results and the U-dependence,<br />
− M’s <strong>in</strong>computability and crude practical approximations.
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