On the Crossroad of Artificial Intelligence: A Revisit to Alan Turing and Norbert Wiener

To give a high-level summary to current approaches for implementing artificial intelligence (AI), we explain the key commonalities and major differences between Turing’s approach and Wiener’s approach in this perspective. Especially, the problems, successful achievements, limitations, and future research directions of existing approaches that follow Weiner’s ideas are addressed, respectively, aiming to provide readers with a good start point and a roadmap. Some other related topics, for example, the role of human experts in developing AI, are also discussed to seek potential solutions for some existing difficulties.

[1]  Kai-Uwe Kühnberger,et al.  Neural-Symbolic Learning and Reasoning: A Survey and Interpretation , 2017, Neuro-Symbolic Artificial Intelligence.

[2]  W. Hager,et al.  and s , 2019, Shallow Water Hydraulics.

[3]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[4]  Nanning Zheng,et al.  Artificial intelligence test: a case study of intelligent vehicles , 2018, Artificial Intelligence Review.

[5]  Benjamin Peters,et al.  Rise of the Machines: A Cybernetic History by Thomas Rid (review) , 2018 .

[6]  Razvan Pascanu,et al.  Relational Deep Reinforcement Learning , 2018, ArXiv.

[7]  Frank L. Lewis,et al.  Optimal and Autonomous Control Using Reinforcement Learning: A Survey , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Razvan Pascanu,et al.  Vector-based navigation using grid-like representations in artificial agents , 2018, Nature.

[9]  Sergey Levine,et al.  Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review , 2018, ArXiv.

[10]  Joel Z. Leibo,et al.  Prefrontal cortex as a meta-reinforcement learning system , 2018, bioRxiv.

[11]  Neel Kant,et al.  Recent Advances in Neural Program Synthesis , 2018, ArXiv.

[12]  Gary Marcus,et al.  Deep Learning: A Critical Appraisal , 2018, ArXiv.

[13]  Silvio Savarese,et al.  Neural Task Programming: Learning to Generalize Across Hierarchical Tasks , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Munther A. Dahleh,et al.  Advancing systems and control research in the era of ML and AI , 2018, Annu. Rev. Control..

[15]  Demis Hassabis,et al.  Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm , 2017, ArXiv.

[16]  Marc Peter Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[17]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[18]  Nanning Zheng,et al.  Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives , 2017, Artificial Intelligence Review.

[19]  Matthieu Geist,et al.  Bridging the Gap Between Imitation Learning and Inverse Reinforcement Learning , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[20]  Shie Mannor,et al.  End-to-End Differentiable Adversarial Imitation Learning , 2017, ICML.

[21]  Nanning Zheng,et al.  Parallel learning: a perspective and a framework , 2017, IEEE/CAA Journal of Automatica Sinica.

[22]  Fei-Yue Wang,et al.  Master general parking skill via deep learning , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[23]  刘 子熠,et al.  Hybrid-augmented intelligence: collaboration and cognition , 2017 .

[24]  Zeb Kurth-Nelson,et al.  Learning to reinforcement learn , 2016, CogSci.

[25]  Tim Rocktäschel,et al.  Programming with a Differentiable Forth Interpreter , 2016, ICML.

[26]  Jie Zhang,et al.  PDP: parallel dynamic programming , 2017, IEEE CAA J. Autom. Sinica.

[27]  Nanning Zheng,et al.  Hybrid-augmented intelligence: collaboration and cognition , 2017, Frontiers of Information Technology & Electronic Engineering.

[28]  Anuradha M. Annaswamy,et al.  Systems & Control for the future of humanity, research agenda: Current and future roles, impact and grand challenges , 2017, Annu. Rev. Control..

[29]  Gerd Folkers,et al.  On computable numbers , 2016 .

[30]  Li Li,et al.  Steps toward Parallel Intelligence , 2016 .

[31]  Li Li,et al.  Intelligence Testing for Autonomous Vehicles: A New Approach , 2016, IEEE Transactions on Intelligent Vehicles.

[32]  Murray Shanahan,et al.  Towards Deep Symbolic Reinforcement Learning , 2016, ArXiv.

[33]  Marcin Andrychowicz,et al.  Learning to learn by gradient descent by gradient descent , 2016, NIPS.

[34]  Qiao Wang,et al.  VirtualWorlds as Proxy for Multi-object Tracking Analysis , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Liuqing Yang,et al.  Where does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond , 2016, IEEE/CAA Journal of Automatica Sinica.

[36]  Joshua B. Tenenbaum,et al.  Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.

[37]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[38]  H. Freud.  Mathematical Control Theory , 2016 .

[39]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[40]  Visvanathan Ramesh,et al.  Simulations for Validation of Vision Systems , 2015, ArXiv.

[41]  Li Li,et al.  The Integration of CPS, CPSS, and ITS: A Focus on Data , 2015 .

[42]  Markus Wulfmeier,et al.  Maximum Entropy Deep Inverse Reinforcement Learning , 2015, 1507.04888.

[43]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[44]  Alex Graves,et al.  Neural Turing Machines , 2014, ArXiv.

[45]  Zhongke Shi,et al.  Composite Neural Dynamic Surface Control of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form , 2014, IEEE Transactions on Cybernetics.

[46]  Stephen Muggleton,et al.  Alan Turing and the development of Artificial Intelligence , 2014, AI Commun..

[47]  Bogdan Gabrys,et al.  Metalearning: a survey of trends and technologies , 2013, Artificial Intelligence Review.

[48]  Sorin Istrail,et al.  Alan Turing and John von Neumann - Their Brains and Their Computers , 2012, Int. Conf. on Membrane Computing.

[49]  A. Subramaniam.  The Mathematician , 2012 .

[50]  Edward A. Wasserman,et al.  The Oxford handbook of comparative cognition , 2012 .

[51]  W. Marsden.  I and J , 2012 .

[52]  Fei-Yue Wang,et al.  The Emergence of Intelligent Enterprises: From CPS to CPSS , 2010, IEEE Intelligent Systems.

[53]  S. Casper.  Book Review: Mind as machine: a history of cognitive science , 2010, Medical History.

[54]  T. Stankowich.  Behavior , 2009, The Quarterly Review of Biology.

[55]  Huaguang Zhang,et al.  Adaptive Dynamic Programming: An Introduction , 2009, IEEE Computational Intelligence Magazine.

[56]  Charles Petzold,et al.  The Annotated Turing: A Guided Tour Through Alan Turing's Historic Paper on Computability and the Turing Machine , 2008 .

[57]  Eyal Amir,et al.  Bayesian Inverse Reinforcement Learning , 2007, IJCAI.

[58]  Edward A. Lee.  Computing Foundations and Practice for Cyber- Physical Systems: A Preliminary Report , 2007 .

[59]  Hugh Mann.  What is common sense? , 2006, Irish medical journal.

[60]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[61]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[62]  Arndt von Twickel,et al.  Foundations of Swarm Intelligence: From Principles to Practice , 2005, nlin/0502003.

[63]  James S. Albus,et al.  Engineering of Mind: An Introduction to the Science of Intelligent Systems , 2001 .

[64]  Andrew Y. Ng,et al.  Pharmacokinetics of a novel formulation of ivermectin after administration to goats , 2000, ICML.

[65]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[66]  Jing Wang,et al.  Swarm Intelligence in Cellular Robotic Systems , 1993 .

[67]  Kevin Kelly,et al.  Out of Control: The New Biology of Machines, Social Systems, and the Economic World , 1992 .

[68]  D. Haraway.  A Cyborg Manifesto , 2013 .

[69]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[70]  Eduardo D. Sontag,et al.  Mathematical Control Theory: Deterministic Finite Dimensional Systems , 1990 .

[71]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[72]  Russell A. Epstein,et al.  ‘Insight’ in the pigeon: antecedents and determinants of an intelligent performance , 1984, Nature.

[73]  T. Paine.  Common Sense , 1995 .

[74]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[75]  Saulius Vydas.  Cyborg: Evolution of the Superman , 1965 .

[76]  N. Wiener.  I Am a Mathematician , 1956 .

[77]  Norbert Wiener,et al.  The human use of human beings - cybernetics and society , 1988 .

[78]  F. H. Adler.  Cybernetics, or Control and Communication in the Animal and the Machine. , 1949 .

[79]  N. Wiener,et al.  Behavior, Purpose and Teleology , 1943, Philosophy of Science.

[80]  A. Church.  An Unsolvable Problem of Elementary Number Theory , 1936 .

[81]  Alonzo Church,et al.  A note on the Entscheidungsproblem , 1936, Journal of Symbolic Logic.