Artificial general intelligence by Dr. Ben Goertzel

By Dr. Ben Goertzel

This is often the 1st publication on present learn on man made basic intelligence (AGI), paintings explicitly eager about engineering basic intelligence – independent, self-reflective, self-improving, commonsensical intelligence. every one writer explains a selected element of AGI intimately in each one bankruptcy, whereas additionally investigating the typical subject matters within the paintings of numerous teams, and posing the massive, open questions during this very important area.
This publication willbe of curiosity to researchers and scholars who require a coherent remedy of AGI and the relationships among AI and similar fields equivalent to physics, philosophy, neuroscience, linguistics, psychology, biology, sociology, anthropology and engineering.

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Combinatory Logic. North-Holland, 1958. Contemporary Approaches to Artificial General Intelligence 29 11. M. Dorigo and L. M. Gambardella. Ant Colonies for the Traveling Salesman Problem. BioSystems, 43:73–81, 1997. 12. M. Dorigo and L. M. Gambardella. Ant Colony Systems: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Trans. Evol. , 1:53–66, 1997. 13. W. Fontana and L. W. Buss. The Arrival of the Fittest: Toward a Theory of Biological Organization. Bull. Math. , 56:1–64, 1994.

The end result of this teaching process will be an autonomous AGI system, oriented toward assisting humans in collectively solving pragmatic problems. 10 The Outlook for AGI The AGI subfield is still in its infancy, but it is certainly encouraging to observe the growing attention that it has received in the past few years. Both the number of people and research groups working on systems designed to achieve general intelligence and the interest from outsiders have been growing. Traditional, narrow AI does play a key role here, as it provides useful examples, inspiration and results for AGI.

39. L. A. Levin. Laws of information conservation (non-growth) and aspects of the foundation of probability theory. Problems of Information Transmission, 10:206–210, 1974. 40. L. A. Levin. On a concrete method of assigning complexity measures. DAN SSSR: Soviet Mathematics Doklady, 17(2):727–731, 1977. 41. T. M. Martinetz and K. J. Schulten. A “neural-gas” network learns topologies, pages 397–402. North-Holland, 1991. 42. A. Newell and H. A. Simon. GPS, a Program that Simulates Human Thought, pages 109–124.

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