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Journal of Artificial General Intelligence

The Journal of the Artificial General Intelligence Society

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Online
ISSN
1946-0163
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Artificial General Intelligence: Concept, State of the Art, and Future Prospects

1OpenCog Foundation G/F, 51C Lung Mei Village Tai Po, N.T., Hong Kong

© by Ben Goertzel. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

Citation Information: Journal of Artificial General Intelligence. Volume 5, Issue 1, Pages 1–48, ISSN (Online) 1946-0163, DOI: https://doi.org/10.2478/jagi-2014-0001, December 2014

Publication History

Accepted:
2014-03-15
Published Online:
2014-12-30

Abstract

In recent years broad community of researchers has emerged, focusing on the original ambitious goals of the AI field - the creation and study of software or hardware systems with general intelligence comparable to, and ultimately perhaps greater than, that of human beings. This paper surveys this diverse community and its progress. Approaches to defining the concept of Artificial General Intelligence (AGI) are reviewed including mathematical formalisms, engineering, and biology inspired perspectives. The spectrum of designs for AGI systems includes systems with symbolic, emergentist, hybrid and universalist characteristics. Metrics for general intelligence are evaluated, with a conclusion that, although metrics for assessing the achievement of human-level AGI may be relatively straightforward (e.g. the Turing Test, or a robot that can graduate from elementary school or university), metrics for assessing partial progress remain more controversial and problematic.

Keywords: AGI; general intelligence; cognitive science

References

  • Achler, T. 2012a. Artificial General Intelligence Begins with Recognition: Evaluating the Flexibility of Recognition. In Theoretical Foundations of Artificial General Intelligence. Springer. 197-217.

  • Achler, T. 2012b. Towards Bridging the Gap Between Pattern Recognition and Symbolic Representation Within Neural Networks. Workshop on Neural-Symbolic Learning and Reasoning, AAAI-2012.

  • Adams, S.; Arel, I.; Bach, J.; Coop, R.; Furlan, R.; Goertzel, B.; Hall, J. S.; Samsonovich, A.; Scheutz, M.; Schlesinger, M.; et al. 2012. Mapping the landscape of human-level artificial general intelligence. AI Magazine 33(1):25-42.

  • Albus, J. S. 2001. Engineering of mind: An introduction to the science of intelligent systems. Wiley.

  • Alvarado, N.; Adams, S. S.; Burbeck, S.; and Latta, C. 2002. Beyond the Turing test: Performance metrics for evaluating a computer simulation of the human mind. In The 2nd International Conference on Development and Learning, 147-152. IEEE.

  • Anderson, J. R., and Lebiere, C. 2003. The Newell test for a theory of cognition. Behavioral and Brain Sciences 26(05):587-601.

  • Anselmi, F.; Leibo, J. Z.; Rosasco, L.; Mutch, J.; Tacchetti, A.; and Poggio, T. 2013. Magic Materials: a theory of deep hierarchical architectures for learning sensory representations.

  • Arel, I.; Rose, D.; and Coop, R. 2009. Destin: A scalable deep learning architecture with application to high-dimensional robust pattern recognition. In Proc. AAAI Fall Symposium on Biologically Inspired Cognitive Architectures, 1150-1157.

  • Arel, I.; Rose, D.; and Karnowski, T. 2009. A deep learning architecture comprising homogeneous cortical circuits for scalable spatiotemporal pattern inference. In NIPS 2009 Workshop on Deep Learning for Speech Recognition and Related Applications.

  • Baars, B. J., and Franklin, S. 2009. Consciousness is computational: The LIDA model of global workspace theory. International Journal of Machine Consciousness 1(01):23-32.

  • Bach, J. 2009. Principles of synthetic intelligence PSI: an architecture of motivated cognition, volume 4. Oxford University Press.

  • Baran`es, A., and Oudeyer, P.-Y. 2009. R-IAC: Robust intrinsically motivated exploration and active learning. Autonomous Mental Development, IEEE Transactions on 1(3):155-169.

  • Ben-David, S., and Schuller, R. 2003. Exploiting task relatedness for multiple task learning. In Learning Theory and Kernel Machines. Springer. 567-580.

  • Bengio, Y. 2009. Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1):1-127.

  • Binet, A., and Simon, T. 1916. The development of intelligence in children: The Binet-Simon Scale. Number 11. Williams & Wilkins Company.

  • Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

  • Brooks, R. A. 2002. Flesh and machines: How robots will change us. Pantheon Books New York

  • Cassimatis, N. 2007. Adaptive algorithmic hybrids for human-level Artificial Intelligence. In Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, 94-112.

  • Damer, B.; Newman, P.; Gordon, R.; and Barbalet, T. 2010. The EvoGrid: simulating pre-biotic emergent complexity.

  • De Garis, H.; Shuo, C.; Goertzel, B.; and Ruiting, L. 2010. A world survey of artificial brain projects, Part I: Large-scale brain simulations. Neurocomputing 74(1):3-29. [Crossref]

  • Duch, W.; Oentaryo, R. J.; and Pasquier, M. 2008. Cognitive Architectures: Where do we go from here? In Proceedings of the First Conference on Artificial General Intelligence, volume 171, 122-136.

  • Dye, L. 2010. Are Dolphins Also Persons? ABC News, Feb. 24 2010.

  • Franklin, S., and Graesser, A. 1997. Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents. In Intelligent agents III: agent theories, architectures, and languages. Springer. 21-35.

  • Franklin, S.; Strain, S.; Snaider, J.; McCall, R.; and Faghihi, U. 2012. Global workspace theory, its LIDA model and the underlying neuroscience. Biologically Inspired Cognitive Architectures 1:32-43.

  • French, R. M. 1996. Subcognition and the Limits of the Turing Test. Machines and thought 11-26.

  • Frye, J.; Ananthanarayanan, R.; and Modha, D. S. 2007. Towards real-time, mouse-scale cortical simulations. CoSyNe: Computational and Systems Neuroscience, Salt Lake City, Utah.

  • Gardner, H. 1999. Intelligence reframed: Multiple intelligences for the 21st century. Basic Books.

  • Gazzaniga, M. S.; Ivry, R. B.; and Mangun, G. R. 2009. Cognitive Neuroscience: The Biology of the Mind. W W Norton.

  • Goertzel, B., and Pennachin, C. 2007. Artificial General Intelligence. Springer.

  • Goertzel, B., and Pitt, J. 2012. Nine Ways to Bias Open-Source AGI Toward Friendliness. Journal of Evolution and Technology 22:1.

  • Goertzel, B., and Wigmore, J. 2011. Cognitive Synergy Is Tricky. Chinese Journal of Mind and Computation.

  • Goertzel, B.; Lian, R.; Arel, I.; de Garis, H.; and Chen, S. 2010a. A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures. Neurocomputing 74(1):30-49. [Crossref]

  • Goertzel, B.; Pennachin, C.; Araujo, S.; Silva, F.; Queiroz, M.; Lian, R.; Silva, W.; Ross, M.; Vepstas, L.; and Senna, A. 2010b. A general intelligence oriented architecture for embodied natural language processing. In 3d Conference on Artificial General Intelligence (AGI-2010). Atlantis Press.

  • Goertzel, B.; Pitt, J.; Wigmore, J.; Geisweiller, N.; Cai, Z.; Lian, R.; Huang, D.; and Yu, G. 2011. Cognitive Synergy between Procedural and Declarative Learning in the Control of Animated and Robotic Agents Using the OpenCogPrime AGI Architecture. In Proceedings of AAAI-11.

  • Goertzel, B.; Ikl´e, M.; and Wigmore, J. 2012. The Architecture of Human-Like General Intelligence. In Theoretical Foundations of Artificial General Intelligence. Springer. 123-144.

  • Goertzel, B. 2009. OpenCogPrime: A cognitive synergy based architecture for artificial general intelligence. In Proceedings of ICCI’09: 8th IEEE International Conference on Cognitive Informatics, 60-68. IEEE.

  • Goertzel, B. 2010. Toward a formal characterization of real-world general intelligence. In Proceedings of the Third Conference on Artificial General Intelligence, 19-24.

  • Goertzel, B. 2014. Artificial General Intelligence. Japanese Artificial Intelligence Society Magazine, 2014-1.

  • Gregory, R. J. 2004. Psychological testing: History, principles, and applications. Allyn & Bacon.

  • Gubrud, M. A. 1997. Nanotechnology and international security. In Fifth Foresight Conference on Molecular Nanotechnology, 1.

  • Hammer, B., and Hitzler, P. 2007. Perspectives of neural-symbolic integration, volume 77. Springer.

  • Han, J.; Zeng, S.; Tham, K.; Badgero, M.; and Weng, J. 2002. Dav: A humanoid robot platform for autonomous mental development. In Development and Learning, 2002. Proceedings. The 2nd International Conference on, 73-81. IEEE.

  • Hawkins, J., and Blakeslee, S. 2007. On intelligence. Macmillan.

  • Hayes, P., and Ford., K. 1995. Turing Test Considered Harmful. IJCAI-14.

  • Hern´andez-Orallo, J., and Dowe, D. L. 2010. Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18):1508-1539.

  • Hibbard, B. 2012. Avoiding unintended AI behaviors. In Artificial General Intelligence. Springer. 107-116.

  • Horwitz, B.; Friston, K. J.; and Taylor, J. G. 2000. Neural modeling and functional brain imaging: an overview. Neural networks 13(8):829-846.

  • Hutter, M. 2005. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer.

  • Hutter, M. 2006. Human Knowledge Compression Contest. http://prize.hutter1.net/.

  • Izhikevich, E. M., and Edelman, G. M. 2008. Large-scale model of mammalian thalamocortical systems. Proc. of the national academy of sciences 105(9):3593-3593.

  • Jilk, D. J., and Lebiere, C. 2008. SAL: An explicitly pluralistic cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence 20:197-218.

  • Jurafsky, D., and James, H. 2000. Speech and language processing: An introduction to natural language processing, computational linguistics, and speech.

  • Just, M. A., and Varma, S. 2007. The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition. Cognitive, Affective, and Behavioral Neuroscience 7:153-191.

  • Kaplan, F. 2008. Neurorobotics: an experimental science of embodiment. Frontiers in neuroscience 2(1):22.

  • Koza, J. R. 1992. Genetic programming: on the programming of computers by means of natural selection, volume 1. MIT press.

  • Krichmar, J. L., and Edelman, G. M. 2006. Principles underlying the construction of brain-based devices. In Proceedings of AISB, volume 6, 37-42.

  • Kurzweil, R. 2005. The singularity is near: When humans transcend biology. Penguin.

  • Laird, J. E.; Wray, R.; Marinier, R.; and Langley, P. 2009. Claims and challenges in evaluating human-level intelligent systems. In Proceedings of the Second Conference on Artificial General Intelligence, 91-96.

  • Laird, J. 2012. The Soar cognitive architecture. MIT Press.

  • Langley, P. 2005. An adaptive architecture for physical agents. In Proceedings of the 2005

  • IEEE/WIC/ACM International Conference on Web Intelligence, 18-25. IEEE.

  • Laud, A., and Dejong, G. 2003. The influence of reward on the speed of reinforcement learning. Proc. of the 20th International Conf. on Machine Learning.

  • Le, Q. V. 2013. Building high-level features using large scale unsupervised learning. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 8595-8598. IEEE.

  • Legg, S., and Hutter, M. 2007a. A collection of definitions of intelligence. Frontiers in Artificial Intelligence and Applications 157:17.

  • Legg, S., and Hutter, M. 2007b. Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4):391-444. [Crossref]

  • Legg, S., and Veness, J. 2013. An approximation of the universal intelligence measure. In Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence. Springer. 236-249.

  • Lenat, D. B., and Guha, R. V. 1989. Building large knowledge-based systems; representation and inference in the Cyc project. Addison-Wesley Longman Publishing Co., Inc.

  • Li, G.; Lou, Z.; Wang, L.; Li, X.; and Freeman, W. J. 2005. Application of chaotic neural model based on olfactory system on pattern recognitions. In Advances in Natural Computation. Springer. 378-381.

  • Li, L.; Walsh, T.; and Littman, M. 2006. Towards a unified theory of state abstraction for MDPs. Proc. of the ninth international symposium on AI and mathematics.

  • Markram, H. 2006. The blue brain project. Nature Reviews Neuroscience 7(2):153-160.

  • Metta, G.; Sandini, G.; Vernon, D.; Natale, L.; and Nori, F. 2008. The iCub humanoid robot: an open platform for research in embodied cognition. In Proceedings of the 8th workshop on performance metrics for intelligent systems, 50-56. ACM.

  • Modayil, J., and Kuipers, B. 2007. Autonomous development of a grounded object ontology by a learning robot. In Proceedings of the national conference on Artificial intelligence, volume 22, 1095. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.

  • Mugan, J., and Kuipers, B. 2008. Towards the application of reinforcement learning to undirected developmental learning. International Conf. on Epigenetic Robotics.

  • Mugan, J., and Kuipers, B. 2009. Autonomously Learning an Action Hierarchy Using a Learned Qualitative State Representation. In IJCAI, 1175-1180.

  • Muggleton, S. 1991. Inductive logic programming. New generation computing 8(4):295-318.

  • Nestor, A., and Kokinov, B. 2004. Towards Active Vision in the DUAL Cognitive Architecture. International Journal on Information Theories and Applications 11.

  • Nilsson, N. J. 2005. Human-level artificial intelligence? Be serious! AI magazine 26(4):68.

  • Nilsson, N. J. 2007. The physical symbol system hypothesis: status and prospects. In 50 years of artificial intelligence. Springer. 9-17.

  • Oudeyer, P.-Y., and Kaplan, F. 2006. Discovering communication. Connection Science 18(2):189-206.

  • Pfeifer, R., and Bongard, J. 2007. How the body shapes the way we think: a new view of intelligence. MIT press.

  • Reeke Jr, G. N.; Sporns, O.; and Edelman, G. M. 1990. Synthetic neural modeling: theDarwin’series of recognition automata. Proceedings of the IEEE 78(9):1498-1530. [Crossref]

  • Richardson, M., and Domingos, P. 2006. Markov logic networks. Machine learning 62(1-2):107-136.

  • Rosbe, J.; Chong, R. S.; and Kieras, D. E. 2001. Modeling with Perceptual and Memory Constraints: An EPIC-Soar Model of a Simplified Enroute Air Traffic Control Task. SOAR Technology Inc. Report.

  • Russell, S. J., and Norvig, P. 2010. Artificial intelligence: a modern approach. Prentice Hall.

  • Samsonovich, A. V. 2010. Toward a Unified Catalog of Implemented Cognitive Architectures. BICA 221:195-244.

  • Schmidhuber, J. 1991a. Curious model-building control systems.. Proc. International Joint Conf. on Neural Networks.

  • Schmidhuber, J. 1991b. A possibility for implementing curiosity and boredom in model-building neural controllers. Proc. of the International Conf. on Simulation of Adaptive Behavior: From Animals to Animats.

  • Schmidhuber, J. 1995. Reinforcement-driven information acquisition in non-deterministic environments. Proc. ICANN’95.

  • Schmidhuber, J. 2003. Exploring the predictable. In Advances in evolutionary computing. Springer. 579-612.

  • Schmidhuber, J. 2006. Godel machines: Fully Self-Referential Optimal Universal Self-Improvers. In Goertzel, B., and Pennachin, C., eds., Artificial General Intelligence. 119-226.

  • Searle, J. R. 1980. Minds, brains, and programs. Behavioral and brain sciences 3(03):417-424.

  • Seth Baum, B. G., and Goertzel, T. 2011. Technological Forecasting and Social Change. Technological Forecasting and Social Change.

  • Shapiro, S. C.; Rapaport,W. J.; Kandefer, M.; Johnson, F. L.; and Goldfain, A. 2007. Metacognition in SNePS. AI Magazine 28(1):17.

  • Shastri, L., and Ajjanagadde, V. 1993. From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony. Behavioral and brain sciences 16(3):417-451.

  • Silver, R.; Boahen, K.; Grillner, S.; Kopell, N.; and Olsen, K. L. 2007. Neurotech for neuroscience: unifying concepts, organizing principles, and emerging tools. The Journal of Neuroscience 27(44):11807-11819. [Crossref]

  • Sloman, A. 2001. Varieties of affect and the cogaff architecture schema. In Proceedings of the AISB01 symposium on emotions, cognition, and affective computing. The Society for the Study of Artificial Intelligence and the Simulation of Behaviour.

  • Socher, R.; Huval, B.; Bath, B. P.; Manning, C. D.; and Ng, A. Y. 2012. Convolutional-Recursive Deep Learning for 3D Object Classification. In NIPS, 665-673.

  • Solomonoff, R. J. 1964a. A formal theory of inductive inference. Part I. Information and control 7(1):1-22.

  • Solomonoff, R. J. 1964b. A formal theory of inductive inference. Part II. Information and control 7(2):224-254.

  • Spearman, C. 1904. General Intelligence, Objectively Determined and Measured. The American Journal of Psychology 15(2):201-292. [Crossref]

  • Sun, R., and Zhang, X. 2004. Top-down versus bottom-up learning in cognitive skill acquisition. Cognitive Systems Research 5(1):63-89.

  • Taylor, M. E.; Kuhlmann, G.; and Stone, P. 2008. Transfer Learning and Intelligence: an Argument and Approach. FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS 171:326.

  • Terman, L. M. 1915. The mental hygiene of exceptional children. The Pedagogical Seminary 22(4):529-537. [Crossref]

  • Thrun, S., and Mitchell, T. 1995. Lifelong robot learning. Robotics and Autonomous Systems.

  • Turing, A. M. 1950. Computing machinery and intelligence. Mind 433-460.

  • Veness, J.; Ng, K. S.; Hutter, M.; Uther,W.; and Silver, D. 2011. A monte-carlo aixi approximation. Journal of Artificial Intelligence Research 40(1):95-142.

  • Wang, P. 2006. Rigid Flexibility: The Logic of Intelligence. Springer.

  • Wang, P. 2009. Embodiment: Does a Laptop Have a Body? In Proceedings of AGI-09, 74-179.

  • Weng, J., and Hwang, W.-S. 2006. From neural networks to the brain: Autonomous mental development. Computational Intelligence Magazine, IEEE 1(3):15-31.

  • Weng, J.; Hwang, W. S.; Zhang, Y.; Yang, C.; and Smith, R. 2000. Developmental humanoids: Humanoids that develop skills automatically. In Proc. The First IEEE-RAS International Conference on Humanoid Robots, 7-8. Citeseer.

  • Yudkowsky, E. 2008. Artificial intelligence as a positive and negative factor in global risk. In Global catastrophic risks. Oxford University Press. 303

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