Human-Level Artificial Intelligence? Be Serious!

I claim that achieving real human-level artificial intelligence would necessarily imply that most of the tasks that humans perform for pay could be automated. Rather than work toward this goal of automation by building special-purpose systems, I argue for the development of general-purpose, educable systems that can learn and be taught to perform any of the thousands of jobs that humans can perform. Joining others who have made similar proposals, I advocate beginning with a system that has minimal, although extensive, built-in capabilities. These would have to include the ability to improve through learning along with many other abilities.

[1]  John R. Searle,et al.  Minds, brains, and programs , 1980, Behavioral and Brain Sciences.

[2]  Rodney A. Brooks,et al.  From earwigs to humans , 1997, Robotics Auton. Syst..

[3]  Gary L. Drescher,et al.  Made-up minds - a constructivist approach to artificial intelligence , 1991 .

[4]  James S. Albus,et al.  The NIST Real-time Control System (RCS): an approach to intelligent systems research , 1997, J. Exp. Theor. Artif. Intell..

[5]  Nils J. Nilsson,et al.  Teleo-Reactive Programs and the Triple-Tower Architecture , 2001, Electron. Trans. Artif. Intell..

[6]  J. Hawkins,et al.  On Intelligence , 2004 .

[7]  B. Jack Copeland,et al.  The Turing Test* , 2000, Minds and Machines.

[8]  Douglas B. Lenat,et al.  CYC: a large-scale investment in knowledge infrastructure , 1995, CACM.

[9]  Nils J. Nilsson,et al.  Eye on the Prize , 1995, AI Mag..

[10]  Steffen Staab,et al.  Project Halo: Towards a Digital Aristotle , 2004, AI Mag..

[11]  De,et al.  Relational Reinforcement Learning , 2001, Encyclopedia of Machine Learning and Data Mining.

[12]  Murray Cole,et al.  Dividing and Conquering , 1997 .

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

[14]  J. McCarthy,et al.  Dividing and conquering logic , 2001 .

[15]  D. Corkill.  Blackboard Systems , 1991 .

[16]  李幼升,et al.  Ph , 1989 .

[17]  P. Dayan,et al.  A framework for mesencephalic dopamine systems based on predictive Hebbian learning , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[18]  Sridhar Mahadevan,et al.  Recent Advances in Hierarchical Reinforcement Learning , 2003, Discret. Event Dyn. Syst..

[19]  Scott Sherwood Benson,et al.  Learning action models for reactive autonomous agents , 1996 .

[20]  Edward A. Feigenbaum,et al.  Some challenges and grand challenges for computational intelligence , 2003, JACM.

[21]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[22]  Aaron Sloman,et al.  The St. Thomas Common Sense Symposium: Designing Architectures for Human-Level Intelligence , 2004, AI Mag..

[23]  Richard S. Sutton,et al.  Learning to predict by the methods of temporal differences , 1988, Machine Learning.

[24]  Rodney A. Brooks,et al.  Prospects for Human Level Intelligence for Humanoid Robots , 1998 .

[25]  John R. Koza,et al.  Genetic Programming IV: Routine Human-Competitive Machine Intelligence , 2003 .

[26]  Nils J. Nilsson,et al.  Artificial Intelligence, Employment, and Income , 1984, AI Mag..

[27]  G. Lakoff,et al.  Metaphors We Live by , 1982 .

[28]  Richard Fikes,et al.  On-line learning of predictive compositional hierarchies , 2002 .