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Prof. Dr. Jürgen Schmidhuber
Father of Modern Artificial Intelligence & Deep Learning, Scientific Director of Swiss AI Lab IDSIA, President of NNAISENSE
Prof. Dr. Jürgen Schmidhuber is a computer scientist and artist known for his work on machine learning, Artificial Intelligence (AI), artificial neural networks, digital physics, and low-complexity art. His contributions also include generalizations of Kolmogorov complexity and the Speed Prior. Since 1995 he has been co-director of the Swiss AI Lab IDSIA in Lugano, since 2009 also professor of Artificial Intelligence at the University of Lugano.
Since age 15 or so his main scientific ambition has been to build an optimal scientist through self-improving Artificial Intelligence, then retire.
His AI team has won nine international competitions in machine learning and pattern recognition (more than any other AI research group worldwide) and six independent best paper/best video awards, has pioneered Deep Learning methods for Artificial Neural Networks since 1991, achieved the world's first superhuman visual classification results, and established the field of mathematically rigorous universal AI and optimal universal problem solvers.
His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He generalized algorithmic information theory, and the many-worlds theory of physics, to obtain a minimal theory of all constructively computable universes - an elegant algorithmic theory of everything.
Many famous leading companies (Google, Apple, Microsoft, Baidu, etc) are now using the machine learning techniques developed in his groups at the Swiss AI Lab IDSIA (USI & SUPSI) and at the TUM CogBotLab.
He also created and illustrated the concept of Low-Complexity Art, the information age's extreme form of minimal art. Since 2009 he is member of the European Academy of Sciences and Arts.
He also is recipient of the 2013 Helmholtz Award of the International Neural Networks Society, and of the 2016 IEEE Neural Networks Pioneer Award, for "pioneering contributions to deep learning and neural networks."
At numerous events (TEDx etc) his talks were the most popular ones.
Since age 15 or so his main scientific ambition has been to build an optimal scientist through self-improving Artificial Intelligence, then retire.
His AI team has won nine international competitions in machine learning and pattern recognition (more than any other AI research group worldwide) and six independent best paper/best video awards, has pioneered Deep Learning methods for Artificial Neural Networks since 1991, achieved the world's first superhuman visual classification results, and established the field of mathematically rigorous universal AI and optimal universal problem solvers.
His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He generalized algorithmic information theory, and the many-worlds theory of physics, to obtain a minimal theory of all constructively computable universes - an elegant algorithmic theory of everything.
Many famous leading companies (Google, Apple, Microsoft, Baidu, etc) are now using the machine learning techniques developed in his groups at the Swiss AI Lab IDSIA (USI & SUPSI) and at the TUM CogBotLab.
He also created and illustrated the concept of Low-Complexity Art, the information age's extreme form of minimal art. Since 2009 he is member of the European Academy of Sciences and Arts.
He also is recipient of the 2013 Helmholtz Award of the International Neural Networks Society, and of the 2016 IEEE Neural Networks Pioneer Award, for "pioneering contributions to deep learning and neural networks."
At numerous events (TEDx etc) his talks were the most popular ones.
possible topics:
- Art
- Artificial curiosity and creativity
- Artificial Intelligence
- artificial neural networks
- computer-generated universe
- digital physics
- Future
- genetic programming
- machine learning
- Roboticist
publications:
- Generative Adversarial Learning: Architectures and Applications (Intelligent Systems Reference Library, Band 217 - 2022 - Mit-Hrsg.)
- Artificial General Intelligence. Proceedings of the 4th International Conference (2011 - Hrsg.)
- Netzwerkarchitekturen, Zielfunktionen und Kettenregel (Habilitation - 1993)
- Dynamische neuronale Netze und das fundamentale raumzeitliche Lernproblem (Dissertation - 1990)
- Evolutionary principles in self-referential learning, or on learning how to learn: The meta-meta-... hook (Diploma thesis - 1987)