Jürgen Schmidhuber

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In the beginning was the code: Juergen Schmidhuber at TEDxUHasselt

Published on Jan 14, 2013

The universe seems incredibly complex. But could its rules be dead simple? Juergen Schmidhuber's fascinating story will convince you that this universe and your own life are just by-products of a very simple and fast program computing all logically possible universes.

Juergen Schmidhuber is Director of the Swiss Artificial Intelligence Lab IDSIA (since 1995), Professor of Artificial Intelligence at the University of Lugano, Switzerland (since 2009), and Professor SUPSI (since 2003).
He helped to transform IDSIA into one of the world's top ten AI labs (the smallest!), according to the ranking of Business Week Magazine. His group pioneered the field of mathematically optimal universal AI and universal problem solvers. The algorithms developed in his lab won seven first prizes in international pattern recognition competitions, as well as several best paper awards.
Since 1990 he has developed a formal theory of fun and curiosity and creativity to build artificial scientists and artists. He also generalized the many-worlds theory of physics to a theory of all constructively computable universes - an algorithmic theory of everything.
He has published nearly 300 peer-reviewed scientific works on topics such as machine learning, artificial recurrent neural networks, fast deep neural nets, adaptive robotics, algorithmic information and complexity theory, digital physics, the formal theory of beauty & humor, and the fine arts.
In 2008 he was elected member of the European Academy of Sciences and Arts.

[Airicist]

Deep Learning — Jürgen Schmidhuber

Nov 9, 2020

AI specialist Jürgen Schmidhuber on credit assignment, recurrent neural networks and how can you solve the parity problem with a network of only five connections.

'The learning, or the credit assignment, is about finding weights that make the neural network exhibit some desired behavior, such as controlling a robot. Depending on the problem and on how the units are connected, such behavior may require long causal chains of computational stages where each stage is setting the stage for the next processing step, where each stage transforms, often in a non-linear way, the aggregate activation of the entire network. '

Jürgen Schmidhuber, Scientific Director, Swiss AI Lab IDSIA