Soweit fürs maschinelle Lernen relevant. Stichwortartige Beilage zur Vorlesung "Maschinelles Lernen II" (SS 2005).

This is a mailing list for discussion of the idea that all possible universes exist.

Applies basic concepts of Kolmogorov compexity theory to the set of possible universes. Ideas on life, true randomness, generalization, and learning in a given universe.

A new simplicity measure for near-optimal computable predictions (based on the fastest way of describing objects, not the shortest).

From foundations of algorithmic information theory to asymptotically optimal yet infeasible methods showing the ultimate limits of machine learning, and all the way down to practically useful tricks for recurrent nets.

"We focus on learning agents interacting with an initially unknown world. Since the world is dynamic, unlike many other machine learning courses ours will put strong emphasis on learning to deal with sequential data."

The CoTeSys cluster of excellence investigates cognition for technical systems such as vehicles, robots, and factories.

"Is there an optimal way of learning non-reactive behaviors in general, unknown environments? Our recent theoretical insights affirm that the answer is yes."

The new millennium has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference, problem solving, decision making, and reinforcement learning.

Answers to some of the frequently asked questions received since the publication of the Gödel Machine TR in 2003.

Gödel machines are self-referential universal problem solvers making provably optimal self- improvements.

"We focus on machine learning, optimal universal artificial intelligence and optimal rational agents, operations research, complexity theory, and robotics." — I need to get there.