30 2 What Is Human-Like General Intelligence?

30 2 What Is Human-Like General Intelligence? between the learning components associated with different kinds of memory, and hence are un- likely to give rise to the emergent structures and dynamics characterizing general intelligence. One of the central ideas underlying CogPrime is that with an integrative cognitive architecture that combines multiple aspects of intelligence, achieved by diverse structures and algorithms, within a common framework designed specifically to support robust synergetic interactions between these aspects. The simplest way to create an integrative AI architecture is to loosely couple multiple com- ponents carrying out various functions, in such a way that the different components pass inputs and outputs amongst each other but do not interfere with or modulate each others’ internal functioning in real-time. However, the human brain appears to be integrative in a much tighter sense, involving rich real-time dynamical coupling between various components with distinct but related functions. In [Goe09a] we have hypothesized that the brain displays a property of cognitive synergy, according to which multiple learning processes can not only dispatch subproblems to each other, but also share contextual understanding in real-time, so that each one can get help from the others in a contextually savvy way. By imbuing AI ar- chitectures with cognitive synergy, we hypothesize, one can get past the bottlenecks that have plagued AT in the past. Part of the reasoning here, as elaborated in Chapter 9 and [Goe09b], is that real physical and social environments display a rich dynamic interconnection between their various aspects, so that richly dynamically interconnected integrative AI architectures will be able to achieve goals within them more effectively. And this brings us to the patternist perspective on intelligent systems, alluded to above and fleshed out further in Chapter 3 with its focus on the emergence of hierarchically and heterarchi- cally structured networks of patterns, and pattern-systems modeling self and others. Ultimately the purpose of cognitive synergy in an AGI system is to enable the various AI algorithms and structures composing the system to work together effectively enough to give rise to the right system-wide emergent structures characterizing real-world general intelligence. The underlying theory is that intelligence is not reliant on any particular structure or algorithm, but is reliant on the emergence of appropriately structured networks of patterns, which can then be used to guide ongoing dynamics of pattern recognition and creation. And the underlying hypothesis is that the emergence of these structures cannot be achieved by a loosely interconnected assem- blage of components, no matter how sensible the architecture; it requires a tightly connected, synergetic system. It is possible to make these theoretical ideas about cognition mathematically rigorous; for instance, Appendix ?? briefly presents a formal definition of cognitive synergy that has been analyzed as part of an effort to prove theorems about the importance of cognitive synergy for giving rise to emergent system properties associated with general intelligence. However, while we have found such formal analyses valuable for clarifying our designs and understanding their qualitative properties, we have concluded that, for the present, the best way to explore our hypotheses about cognitive synergy and human-like general intelligence is empirically — via building and testing systems like CogPrime. 2.5.1 Achieving Humanlike Intelligence via Cognitive Synergy Summing up: at the broadest level, there are four primary challenges in constructing an inte- grative, cognitive synergy based approach to AGI: HOUSE_OVERSIGHT_012946