By Dr. Ben Goertzel
This is often the 1st ebook on present study on synthetic basic intelligence (AGI), paintings explicitly serious about engineering normal intelligence – self reliant, self-reflective, self-improving, commonsensical intelligence. each one writer explains a selected point of AGI intimately in each one bankruptcy, whereas additionally investigating the typical issues 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 therapy of AGI and the relationships among AI and comparable fields comparable to physics, philosophy, neuroscience, linguistics, psychology, biology, sociology, anthropology and engineering.
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Robots on Your Doorstep. Robotics Press, 1978. 58. Eliezer Yudkowsky. General Intelligence and Seed AI. 2002. 59. Lotﬁ A. Zadeh. Fuzzy Sets and Applications: Selected Papers by L. A. Zadeh. John Wiley and Sons, 1987. 60. Lotﬁ A. Zadeh and Janusz Kacprzyk, editors. Fuzzy Logic for the Management of Uncertainty. John Wiley and Sons, 1992. edu/~pwang/ Summary. Is there an “essence of intelligence” that distinguishes intelligent systems from non-intelligent systems? If there is, then what is it? This chapter suggests an answer to these questions by introducing the ideas behind the NARS (Nonaxiomatic Reasoning System) project.
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. Several such examples have been mentioned in the previous sections in connection with one or another AGI approach. Innovative ideas like the application of complexity and algorithmic information theory to the mathematical theorization of intelligence and AI provide valuable ground for AGI researchers.
Another approach, however, is to consider sociality at a more fundamental level, and to create systems from the get-go that are at least as social as they are intelligent. One example of this sort of approach is Steve Grand’s neural-net architecture as embodied in the Creatures game . His neural net based creatures are intended to grow more intelligent by interacting with each other – struggling with each other, learning to outsmart each other, and so forth. John Holland’s classiﬁer systems  are another example of a multi-agent system in which competition and cooperation are both present.
Artificial general intelligence by Dr. Ben Goertzel