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The new AI is general and mathematically rigorous

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Frontiers of Electrical and Electronic Engineering in China

Abstract

Most traditional artificial intelligence (AI) systems of the past decades are either very limited, or based on heuristics, or both. The new millennium, however, has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference based on Occam’s razor, problem solving, decision making, and reinforcement learning in environments of a very general type. Since inductive inference is at the heart of all inductive sciences, some of the results are relevant not only for AI and computer science but also for physics, provoking nontraditional predictions based on Zuse’s thesis of the computer-generated universe. We first briefly review the history of AI since Gödel’s 1931 paper, then discuss recent post-2000 approaches that are currently transforming general AI research into a formal science.

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Correspondence to Jürgen Schmidhuber.

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Part of this work is reprinted from Refs. [11] and [100] with friendly permission by Springer-Verlag.

Jürgen Schmidhuber wants to build an optimal scientist, then retire. He is Director of the Swiss Artificial Intelligence Lab IDSIA (since 1995), Professor of Artificial Intelligence at the University of Lugano, Switzerland (since 2009), Head of the CogBotLab at TU Munich, Germany (since 2004, as Professor Extraordinarius until 2009), and Professor SUPSI, Switzerland (since 2003). He obtained his doctoral degree in computer science from TUM in 1991 and his Habilitation degree in 1993, after a postdoctoral stay at the University of Colorado at Boulder. 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. In 2008 he was elected member of the European Academy of Sciences and Arts. He has published more than 200 peer-reviewed scientific papers (some won best paper awards) on topics such as machine learning, mathematically optimal universal AI, artificial curiosity and creativity, artificial recurrent neural networks (which won several recent handwriting recognition contests), adaptive robotics, algorithmic information and complexity theory, digital physics, theory of beauty, and the fine arts.

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Schmidhuber, J. The new AI is general and mathematically rigorous. Front. Electr. Electron. Eng. China 5, 347–362 (2010). https://doi.org/10.1007/s11460-010-0105-z

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