By Dr. Ben Goertzel
This can be the 1st e-book on present examine on man made common intelligence (AGI), paintings explicitly taken with engineering normal intelligence - independent, self-reflective, self-improving, commonsensical intelligence. each one writer explains a particular point of AGI intimately in each one bankruptcy, whereas additionally investigating the typical topics within the paintings of various teams, and posing the massive, open questions during this very important sector. This ebook might be of curiosity to researchers and scholars who require a coherent therapy of AGI and the relationships among AI and similar fields resembling physics, philosophy, neuroscience, linguistics, psychology, biology, sociology, anthropology and engineering.
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For our current purpose, there is no “right” or “wrong” working deﬁnition for intelligence, but there are “better” and “not-so-good” ones. When comparing proposed deﬁnitions, the four requirements may conﬂict with each other. For example, one deﬁnition is more fruitful, while another is simpler. In such a situation, some weighting and trade-oﬀ become necessary. However, there is no evidence showing that in general the requirements cannot be satisﬁed at the same time. 2 A Working Deﬁnition of Intelligence Following the preparation of the previous section, we propose here a working deﬁnition of intelligence: Intelligence is the capacity of a system to adapt to its environment while operating with insuﬃcient knowledge and resources.
Acad. Sci. USA, 91:757–761, 1994. 15. Howard Gardner. Intelligence Reframed: Multiple Intelligences for the 21st Century. Basic Books, 2000. 16. Ben Goertzel. The Evolving Mind. Gordon and Breach, 1993. 17. Ben Goertzel. The Structure of Intelligence. Springer-Verlag, 1993. 18. Ben Goertzel. Chaotic Logic. Plenum Press, 1994. 19. Ben Goertzel. From Complexity to Creativity. Plenum Press, 1997. 20. Ben Goertzel. Creating Internet Intelligence. Plenum Press, 2001. 21. Ben Goertzel. Novamente: Design for an Artiﬁcial General Intelligence.
Chickering. Learning Bayesian Networks: the Combination of Knowledge and Statistical Data. Technical Report Tech. Rep. MSR-TR-94-09, Microsoft Research, 1994. 30. John Holland. A Mathematical Framework for Studying Learning in Classiﬁer Systems. Physica D, 2, n. 1-3, 1986. 31. Bipin Indurkhya. Metaphor and Cognition: An Interactionist Approach. Kluwer Academic, 1992. 32. John Josephson and Susan Josephson. Abductive Inference: Computation, Philosophy, Technology. Cambridge University Press, 1994.