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Artificial General Intelligence: Concept, State of the Art, and Future Prospects Cover

Artificial General Intelligence: Concept, State of the Art, and Future Prospects

Journal of Artificial General Intelligence
Volume 5 (2014): Issue 1 (December 2014)
By: Ben Goertzel  
Open Access
|Dec 2014

References

  1. Achler, T. 2012a. Artificial General Intelligence Begins with Recognition: Evaluating the Flexibility of Recognition. In Theoretical Foundations of Artificial General Intelligence. Springer. 197-217.10.2991/978-94-91216-62-6_11
    Search in Google Scholar
  2. 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.
    Search in Google Scholar
  3. 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):25-42.10.1609/aimag.v33i1.2322
    Search in Google Scholar
  4. Albus, J. S. 2001. Engineering of mind: An introduction to the science of intelligent systems. Wiley.
    Search in Google Scholar
  5. 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.
    Search in Google Scholar
  6. Anderson, J. R., and Lebiere, C. 2003. The Newell test for a theory of cognition. Behavioral and Brain Sciences 26(05):587-601.10.1017/S0140525X0300013X15179936
    Search in Google Scholar
  7. 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.
    Search in Google Scholar
  8. 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.
    Search in Google Scholar
  9. 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.
    Search in Google Scholar
  10. Baars, B. J., and Franklin, S. 2009. Consciousness is computational: The LIDA model of global workspace theory. International Journal of Machine Consciousness 1(01):23-32.10.1142/S1793843009000050
    Search in Google Scholar
  11. Bach, J. 2009. Principles of synthetic intelligence PSI: an architecture of motivated cognition, volume 4. Oxford University Press.10.1093/acprof:oso/9780195370676.001.0001
    Search in Google Scholar
  12. 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):155-169.10.1109/TAMD.2009.2037513
    Search in Google Scholar
  13. Ben-David, S., and Schuller, R. 2003. Exploiting task relatedness for multiple task learning. In Learning Theory and Kernel Machines. Springer. 567-580.10.1007/978-3-540-45167-9_41
    Search in Google Scholar
  14. Bengio, Y. 2009. Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1):1-127.10.1561/2200000006
    Search in Google Scholar
  15. Binet, A., and Simon, T. 1916. The development of intelligence in children: The Binet-Simon Scale. Number 11. Williams & Wilkins Company.10.1037/11069-000
    Search in Google Scholar
  16. Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
    Search in Google Scholar
  17. Brooks, R. A. 2002. Flesh and machines: How robots will change us. Pantheon Books New York
    Search in Google Scholar
  18. Cassimatis, N. 2007. Adaptive algorithmic hybrids for human-level Artificial Intelligence. In Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, 94-112.
    Search in Google Scholar
  19. Damer, B.; Newman, P.; Gordon, R.; and Barbalet, T. 2010. The EvoGrid: simulating pre-biotic emergent complexity.
    Search in Google Scholar
  20. 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):3-29.10.1016/j.neucom.2010.08.004
    Search in Google Scholar
  21. 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.
    Search in Google Scholar
  22. Dye, L. 2010. Are Dolphins Also Persons? ABC News, Feb. 24 2010.
    Search in Google Scholar
  23. 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.
    Search in Google Scholar
  24. 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 1:32-43.10.1016/j.bica.2012.04.001
    Search in Google Scholar
  25. French, R. M. 1996. Subcognition and the Limits of the Turing Test. Machines and thought 11-26.
    Search in Google Scholar
  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.
    Search in Google Scholar
  27. Gardner, H. 1999. Intelligence reframed: Multiple intelligences for the 21st century. Basic Books.
    Search in Google Scholar
  28. Gazzaniga, M. S.; Ivry, R. B.; and Mangun, G. R. 2009. Cognitive Neuroscience: The Biology of the Mind. W W Norton.
    Search in Google Scholar
  29. Goertzel, B., and Pennachin, C. 2007. Artificial General Intelligence. Springer.10.1007/978-3-540-68677-4
    Search in Google Scholar
  30. Goertzel, B., and Pitt, J. 2012. Nine Ways to Bias Open-Source AGI Toward Friendliness. Journal of Evolution and Technology 22:1.
    Search in Google Scholar
  31. Goertzel, B., and Wigmore, J. 2011. Cognitive Synergy Is Tricky. Chinese Journal of Mind and Computation.
    Search in Google Scholar
  32. 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.
    Search in Google Scholar
  33. 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.10.2991/agi.2010.16
    Search in Google Scholar
  34. 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. 10.1609/aaai.v25i1.7831
    Search in Google Scholar
  35. Goertzel, B.; Ikl´e, M.; and Wigmore, J. 2012. The Architecture of Human-Like General Intelligence. In Theoretical Foundations of Artificial General Intelligence. Springer. 123-144.10.2991/978-94-91216-62-6_8
    Search in Google Scholar
  36. 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.10.1109/COGINF.2009.5250807
    Search in Google Scholar
  37. Goertzel, B. 2010. Toward a formal characterization of real-world general intelligence. In Proceedings of the Third Conference on Artificial General Intelligence, 19-24.
    Search in Google Scholar
  38. Goertzel, B. 2014. Artificial General Intelligence. Japanese Artificial Intelligence Society Magazine, 2014-1.
    Search in Google Scholar
  39. Gregory, R. J. 2004. Psychological testing: History, principles, and applications. Allyn & Bacon.
    Search in Google Scholar
  40. Gubrud, M. A. 1997. Nanotechnology and international security. In Fifth Foresight Conference on Molecular Nanotechnology, 1.
    Search in Google Scholar
  41. Hammer, B., and Hitzler, P. 2007. Perspectives of neural-symbolic integration, volume 77. Springer.10.1007/978-3-540-73954-8
    Search in Google Scholar
  42. 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.
    Search in Google Scholar
  43. Hawkins, J., and Blakeslee, S. 2007. On intelligence. Macmillan.
    Search in Google Scholar
  44. Hayes, P., and Ford., K. 1995. Turing Test Considered Harmful. IJCAI-14.
    Search in Google Scholar
  45. Hern´andez-Orallo, J., and Dowe, D. L. 2010. Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18):1508-1539.10.1016/j.artint.2010.09.006
    Search in Google Scholar
  46. Hibbard, B. 2012. Avoiding unintended AI behaviors. In Artificial General Intelligence. Springer. 107-116.10.1007/978-3-642-35506-6_12
    Search in Google Scholar
  47. Horwitz, B.; Friston, K. J.; and Taylor, J. G. 2000. Neural modeling and functional brain imaging: an overview. Neural networks 13(8):829-846.10.1016/S0893-6080(00)00062-9
    Search in Google Scholar
  48. Hutter, M. 2005. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer.
    Search in Google Scholar
  49. Hutter, M. 2006. Human Knowledge Compression Contest. http://prize.hutter1.net/.
    Search in Google Scholar
  50. Izhikevich, E. M., and Edelman, G. M. 2008. Large-scale model of mammalian thalamocortical systems. Proc. of the national academy of sciences 105(9):3593-3593.10.1073/pnas.0712231105226516018292226
    Search in Google Scholar
  51. Jilk, D. J., and Lebiere, C. 2008. SAL: An explicitly pluralistic cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence 20:197-218.10.1080/09528130802319128
    Search in Google Scholar
  52. Jurafsky, D., and James, H. 2000. Speech and language processing: An introduction to natural language processing, computational linguistics, and speech.
    Search in Google Scholar
  53. 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 7:153-191.10.3758/CABN.7.3.153
    Search in Google Scholar
  54. Kaplan, F. 2008. Neurorobotics: an experimental science of embodiment. Frontiers in neuroscience 2(1):22.10.3389/neuro.01.023.2008257008218982102
    Search in Google Scholar
  55. Koza, J. R. 1992. Genetic programming: on the programming of computers by means of natural selection, volume 1. MIT press.
    Search in Google Scholar
  56. Krichmar, J. L., and Edelman, G. M. 2006. Principles underlying the construction of brain-based devices. In Proceedings of AISB, volume 6, 37-42.
    Search in Google Scholar
  57. Kurzweil, R. 2005. The singularity is near: When humans transcend biology. Penguin.
    Search in Google Scholar
  58. 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.
    Search in Google Scholar
  59. Laird, J. 2012. The Soar cognitive architecture. MIT Press.10.7551/mitpress/7688.001.0001
    Search in Google Scholar
  60. Langley, P. 2005. An adaptive architecture for physical agents. In Proceedings of the 2005
    Search in Google Scholar
  61. IEEE/WIC/ACM International Conference on Web Intelligence, 18-25. IEEE.
    Search in Google Scholar
  62. 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.
    Search in Google Scholar
  63. 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.10.1109/ICASSP.2013.6639343
    Search in Google Scholar
  64. Legg, S., and Hutter, M. 2007a. A collection of definitions of intelligence. Frontiers in Artificial Intelligence and Applications 157:17.
    Search in Google Scholar
  65. Legg, S., and Hutter, M. 2007b. Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4):391-444.10.1007/s11023-007-9079-x
    Search in Google Scholar
  66. Legg, S., and Veness, J. 2013. An approximation of the universal intelligence measure. In Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence. Springer. 236-249.10.1007/978-3-642-44958-1_18
    Search in Google Scholar
  67. 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.
    Search in Google Scholar
  68. 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. 378-381.10.1007/11539087_47
    Search in Google Scholar
  69. 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.
    Search in Google Scholar
  70. Markram, H. 2006. The blue brain project. Nature Reviews Neuroscience 7(2):153-160.10.1038/nrn184816429124
    Search in Google Scholar
  71. 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.10.1145/1774674.1774683
    Search in Google Scholar
  72. 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.
    Search in Google Scholar
  73. Mugan, J., and Kuipers, B. 2008. Towards the application of reinforcement learning to undirected developmental learning. International Conf. on Epigenetic Robotics.
    Search in Google Scholar
  74. Mugan, J., and Kuipers, B. 2009. Autonomously Learning an Action Hierarchy Using a Learned Qualitative State Representation. In IJCAI, 1175-1180.
    Search in Google Scholar
  75. Muggleton, S. 1991. Inductive logic programming. New generation computing 8(4):295-318.10.1007/BF03037089
    Search in Google Scholar
  76. Nestor, A., and Kokinov, B. 2004. Towards Active Vision in the DUAL Cognitive Architecture. International Journal on Information Theories and Applications 11.
    Search in Google Scholar
  77. Nilsson, N. J. 2005. Human-level artificial intelligence? Be serious! AI magazine 26(4):68.
    Search in Google Scholar
  78. Nilsson, N. J. 2007. The physical symbol system hypothesis: status and prospects. In 50 years of artificial intelligence. Springer. 9-17.10.1007/978-3-540-77296-5_2
    Search in Google Scholar
  79. Oudeyer, P.-Y., and Kaplan, F. 2006. Discovering communication. Connection Science 18(2):189-206.10.1080/09540090600768567
    Search in Google Scholar
  80. Pfeifer, R., and Bongard, J. 2007. How the body shapes the way we think: a new view of intelligence. MIT press.10.7551/mitpress/3585.001.0001
    Search in Google Scholar
  81. 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):1498-1530.10.1109/5.58327
    Search in Google Scholar
  82. Richardson, M., and Domingos, P. 2006. Markov logic networks. Machine learning 62(1-2):107-136.10.1007/s10994-006-5833-1
    Search in Google Scholar
  83. 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.10.1037/e446312006-001
    Search in Google Scholar
  84. Russell, S. J., and Norvig, P. 2010. Artificial intelligence: a modern approach. Prentice Hall.
    Search in Google Scholar
  85. Samsonovich, A. V. 2010. Toward a Unified Catalog of Implemented Cognitive Architectures. BICA 221:195-244.
    Search in Google Scholar
  86. Schmidhuber, J. 1991a. Curious model-building control systems.. Proc. International Joint Conf. on Neural Networks. 10.1109/IJCNN.1991.170605
    Search in Google Scholar
  87. 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.
    Search in Google Scholar
  88. Schmidhuber, J. 1995. Reinforcement-driven information acquisition in non-deterministic environments. Proc. ICANN’95.
    Search in Google Scholar
  89. Schmidhuber, J. 2003. Exploring the predictable. In Advances in evolutionary computing. Springer. 579-612.10.1007/978-3-642-18965-4_23
    Search in Google Scholar
  90. Schmidhuber, J. 2006. Godel machines: Fully Self-Referential Optimal Universal Self-Improvers. In Goertzel, B., and Pennachin, C., eds., Artificial General Intelligence. 119-226.
    Search in Google Scholar
  91. Searle, J. R. 1980. Minds, brains, and programs. Behavioral and brain sciences 3(03):417-424.10.1017/S0140525X00005756
    Search in Google Scholar
  92. Seth Baum, B. G., and Goertzel, T. 2011. Technological Forecasting and Social Change. Technological Forecasting and Social Change.
    Search in Google Scholar
  93. Shapiro, S. C.; Rapaport,W. J.; Kandefer, M.; Johnson, F. L.; and Goldfain, A. 2007. Metacognition in SNePS. AI Magazine 28(1):17.
    Search in Google Scholar
  94. 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):417-451.10.1017/S0140525X00030910
    Search in Google Scholar
  95. 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):11807-11819.10.1523/JNEUROSCI.3575-07.2007
    Search in Google Scholar
  96. 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.
    Search in Google Scholar
  97. 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.
    Search in Google Scholar
  98. Solomonoff, R. J. 1964a. A formal theory of inductive inference. Part I. Information and control 7(1):1-22.10.1016/S0019-9958(64)90223-2
    Search in Google Scholar
  99. Solomonoff, R. J. 1964b. A formal theory of inductive inference. Part II. Information and control 7(2):224-254.10.1016/S0019-9958(64)90131-7
    Search in Google Scholar
  100. Spearman, C. 1904. General Intelligence, Objectively Determined and Measured. The American Journal of Psychology 15(2):201-292.10.2307/1412107
    Search in Google Scholar
  101. Sun, R., and Zhang, X. 2004. Top-down versus bottom-up learning in cognitive skill acquisition. Cognitive Systems Research 5(1):63-89.10.1016/j.cogsys.2003.07.001
    Search in Google Scholar
  102. 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.
    Search in Google Scholar
  103. Terman, L. M. 1915. The mental hygiene of exceptional children. The Pedagogical Seminary 22(4):529-537.10.1080/08919402.1915.10533983
    Search in Google Scholar
  104. Thrun, S., and Mitchell, T. 1995. Lifelong robot learning. Robotics and Autonomous Systems.10.1007/978-3-642-79629-6_7
    Search in Google Scholar
  105. Turing, A. M. 1950. Computing machinery and intelligence. Mind 433-460.10.1093/mind/LIX.236.433
    Search in Google Scholar
  106. 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):95-142.10.1613/jair.3125
    Search in Google Scholar
  107. Wang, P. 2006. Rigid Flexibility: The Logic of Intelligence. Springer.
    Search in Google Scholar
  108. Wang, P. 2009. Embodiment: Does a Laptop Have a Body? In Proceedings of AGI-09, 74-179.
    Search in Google Scholar
  109. Weng, J., and Hwang, W.-S. 2006. From neural networks to the brain: Autonomous mental development. Computational Intelligence Magazine, IEEE 1(3):15-31.10.1109/MCI.2006.1672985
    Search in Google Scholar
  110. 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.
    Search in Google Scholar
  111. Yudkowsky, E. 2008. Artificial intelligence as a positive and negative factor in global risk. In Global catastrophic risks. Oxford University Press. 303 10.1093/oso/9780198570509.003.0021
    Search in Google Scholar
DOI: https://doi.org/10.2478/jagi-2014-0001 | Journal eISSN: 1946-0163
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Language: English
Page range: 1 - 48
Accepted on: Mar 15, 2014
Published on: Dec 30, 2014
Published by: Artificial General Intelligence Society
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
AGI,
general intelligence,
cognitive science
Related subjects:
Computer sciences,
Artificial intelligence

© 2014 Ben Goertzel, published by Artificial General Intelligence Society
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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