Philosophers & Futurists, Catch Up! Response to The Singularity

Responding to Chalmers' The Singularity (2010), I argue that progress towards self-improving AIs is already substantially beyond what many futurists and philosophers are aware of. Instead of rehashing well-trodden topics of the previous millennium, let us start focusing on relevant new millennium results. All indented paragraphs of this paper are quotes taken from Chalmers' paper of 2010, who mentions Good's informal speculations (1965) on ultraintelligent self-improving machines: The key idea is that a machine that is more intelligent than humans will be better than humans at designing machines. So it will be capable of designing a machine more intelligent than the most intelligent machine that humans can design. Chalmers speculates that some sort of meta-evolution could be used to build more and more intelligent machines called AI, AI+, AI++...: The process of evolution might count as an indirect example: less intel- ligent systems have the capacity to create more intelligent systems by reproduction, variation and natural selection. This version would then come to the same thing as an evolutionary path to AI and AI++. (...) If we produce an AI by machine learning, it is likely that soon after we will be able to improve the learning algorithm and extend the learning pro- cess, leading to AI+. If we produce an AI by artificial evolution, it is

[1]  Ronald J. Williams,et al.  Gradient-based learning algorithms for recurrent networks and their computational complexity , 1995 .

[2]  Luca Maria Gambardella,et al.  Flexible, High Performance Convolutional Neural Networks for Image Classification , 2011, IJCAI.

[3]  T. Munich,et al.  Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks , 2008, NIPS.

[4]  Nichael Lynn Cramer,et al.  A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.

[5]  Jürgen Schmidhuber,et al.  Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) , 2010, IEEE Transactions on Autonomous Mental Development.

[6]  J. Urgen Schmidhuber A Computer Scientist's View of Life, the Universe, and Everything , 1997 .

[7]  Jürgen Schmidhuber,et al.  Algorithmic Theories of Everything , 2000, ArXiv.

[8]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..

[9]  K. Gödel Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I , 1931 .

[10]  D. Chalmers The Singularity: a Philosophical Analysis , 2010 .

[11]  Paul M. B. Vitányi,et al.  An Introduction to Kolmogorov Complexity and Its Applications, Third Edition , 1997, Texts in Computer Science.

[12]  K. Gödel Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I , 1931 .

[13]  Jürgen Schmidhuber,et al.  Hierarchies of Generalized Kolmogorov Complexities and Nonenumerable Universal Measures Computable in the Limit , 2002, Int. J. Found. Comput. Sci..

[14]  Konrad Zuse,et al.  Rechnender Raum , 1991, Physik und Informatik.

[15]  Jürgen Schmidhuber,et al.  Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks , 2008, NIPS.

[16]  Marcus Hutter The Fastest and Shortest Algorithm for all Well-Defined Problems , 2002, Int. J. Found. Comput. Sci..

[17]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[18]  Jürgen Schmidhuber,et al.  A Fixed Size Storage O(n3) Time Complexity Learning Algorithm for Fully Recurrent Continually Running Networks , 1992, Neural Computation.

[19]  Jürgen Schmidhuber,et al.  Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts , 2006, Connect. Sci..

[20]  Dr. Marcus Hutter,et al.  Universal artificial intelligence , 2004 .

[21]  Jürgen Schmidhuber,et al.  The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions , 2002, COLT.

[22]  Hans J. Bremermann,et al.  Minimum energy requirements of information transfer and computing , 1982 .

[23]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[24]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..

[25]  Z. Pylyshyn Computation and cognition: issues in the foundations of cognitive science , 1980, Behavioral and Brain Sciences.

[26]  Paul M. B. Vitányi,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 1997, Graduate Texts in Computer Science.

[27]  Jürgen Schmidhuber,et al.  Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes (特集 高次機能の学習と創発--脳・ロボット・人間研究における新たな展開) , 2009 .

[28]  SolomonoffR. Complexity-based induction systems , 2006 .

[29]  PAUL J. WERBOS,et al.  Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.

[30]  Jürgen Schmidhuber,et al.  A Computer Scientist's View of Life, the Universe, and Everything , 1999, Foundations of Computer Science: Potential - Theory - Cognition.