Essentials of General Intelligence: The Direct Path to Artificial General Intelligence
Understanding general intelligence and identifying its essential components are key to building next-generation AI systems — systems that are far less expensive, yet significantly more capable. In addition to concentrating on general learning abilities, a fast-track approach should also seek a path of least resistance — one that capitalizes on human engineering strengths and available technology. Sometimes, this involves selecting the AI road less traveled.
minimizing initial environment-specific programming (through self-adaptive configuration);
substantially reducing ongoing software changes, because a large amount of additional functionality and knowledge will be acquired autonomously via self-supervised learning;
greatly increasing the scope of applications, as users teach and train additional capabilities; and
improved flexibility and robustness resulting from systems’ ability to adapt to changing data patterns, environments and goals.
AGI promises to make an important contribution toward realizing software and robotic systems that are more usable, intelligent, and human-friendly. The time seems ripe for a major initiative down this new path of human advancement that is now open to us.
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- 1.Aha DW (ed) (1997) Lazy Learning. Artificial Intelligence Review, 11:1–5.Google Scholar
- 3.Arbib MA (1992) Schema theory. In: Shapiro S (ed), Encyclopedia of Artificial Intelligence, 2nd ed. John Wiley and Sons, New York.Google Scholar
- 4.Braitenberg V (1984) Vehicles: Experiments in Synthetic Psychology. MIT Press, Cambridge, MA.Google Scholar
- 5.Brooks RA, Stein LA (1993) Building Brains for Bodies. Memo 1439, Artificial Intelligence Lab, Massachusetts Institute of Technology.Google Scholar
- 6.Brooks RA (1994) Coherent behavior from Many Adaptive Processes. In: Cliff D, Husbands P, Meyer JA, Wilson SW (eds), From animals to animats: Proceedings of the third International Conference on Simulation of Adaptive Behavior. MIT Press, Cambridge, MA.Google Scholar
- 7.Churchland PM (1995) The Engine of Reason, the Seat of the Soul: A Philosophical Journey into the Brain. MIT Press, Cambridge, MA.Google Scholar
- 8.Clark A (1997) Being There: Putting Brain, Body and World Together Again. MIT Press, Cambridge, MA.Google Scholar
- 10.Fritzke B (1995) AF Growing Neural Gas Network Learns Topologies. In: Tesauro G, Touretzky DS, Leen TK (eds), Advances in Neural Information Processing Systems 7. MIT Press, Cambridge, MA.Google Scholar
- 12.Goertzel B (2001) Creating Internet Intelligence. Plenum Press, New York.Google Scholar
- 14.Gottfredson LS (1998) The General Intelligence Factor. Scientific American, 9(4):24–29.Google Scholar
- 15.Grimson WEL, Stauffer C, Lee L, Romano R (1998) Using Adaptive Tracking to Classify and Monitor Activities in a Site. Proc. IEEE Conf. on Computer Vision and Pattern Recognition.Google Scholar
- 17.Kelley D (1986) The Evidence of the Senses. Louisiana State University Press, Baton Rouge, LA.Google Scholar
- 19.Lenat D, Guha R (1990) Building Large Knowledge Based Systems. Addison-Wesley, Reading, MA.Google Scholar
- 20.Margolis H (1987) Patterns, Thinking, and Cognition: A Theory of Judgment. University of Chicago Press, Chicago, IL.Google Scholar
- 21.McCarthy J, Hayes P (1969) Some Philosophical Problems from the Standpoint of Artificial Intelligence. Machine Intelligence, 4:463–502.Google Scholar
- 22.Nenov VI, Dyer MG (1994) Language Learning via Perceptual/Motor Association: A Massively Parallel Model. In: Kitano H, Hendler JA (eds), Massively Parallel Artificial Intelligence, MIT Press, Cambridge, MA.Google Scholar
- 23.Pfeifer R, Scheier C (1999) Understanding Intelligence. MIT Press, Cambridge, MA.Google Scholar
- 24.Picard RW (1997) Affective Computing. MIT Press, Cambridge, MA.Google Scholar
- 25.Pylyshyn ZW (ed) (1987) The Robot’s Dilemma: The Frame Problem in A.I. Ablex, Norwood, NJ.Google Scholar
- 26.Rand A (1990) Introduction to Objectivist Epistemology. Meridian, New York.Google Scholar
- 27.Russell S, Norvig P (1995) Artificial Intelligence: A Modern Approach. Prentice Hall, Upper Saddle River, NJ.Google Scholar
- 28.Yip K, Sussman GJ (1997) Sparse Representations for Fast, One-shot Learning. Proc. of National Conference on Artificial Intelligence.Google Scholar