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Titlebook: Artificial General Intelligence; 14th International C Ben Goertzel,Matthew Iklé,Alexey Potapov Conference proceedings 2022 Springer Nature

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Univalent Foundations of AGI are (not) All You Need,r building a language of a cognitive architecture, but it is not sufficient by itself to build an AGI system, which should contain grounded types and operation, including those that alter already defined types in a not strictly provable (within available types themselves) way.
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,Elements of Task Theory,n is one that allows an objective comparison of tasks, based on measurable physical properties, and that can serve as a foundation for studying, evaluating, and comparing learning controllers of various kinds on a variety of tasks by providing principled ways for constructing, comparing, and changin
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Mesarovician Abstract Learning Systems,GI. In particular, current approaches to learning hold notions of problem domain and problem task as fundamental precepts, but it is hardly apparent that an AGI encountered in the wild will be discernable into a set of domain-task pairings. Nor is it apparent that the outcomes of AGI in a system can
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