1.6.6 Use heuristic computer science methods

8 1 Introduction 1.6.6 Use heuristic computer science methods The computer science field contains a number of abstract formalisms, algorithms and structures that have relevance beyond specific narrow AI applications, yet aren’t necessarily understood as thoroughly as would be required to integrate them into the rigorous mathematical theory of intelligence. Based on these formalisms, algorithms and structures, a number of "single formal- ism/algorithm focused" AGI approaches have been outlined, some of which will be reviewed in Chapter 4. For example Pei Wang’s NARS ("Non-Axiomatic Reasoning System”) approach is based on a specific logic which he argues to be the "logic of general intelligence" — so, while his system contains many other aspects than this logic, he considers this logic to be the crux of the system and the source of its potential power as an AGI system. The basic intuition on the part of these "single formalism/algorithm focused" researchers seems to be that there is one key formalism or algorithm underlying intelligence, and if you achieve this key aspect in your AGI program, you're going to get something that fundamentally thinks like a person, even if it has some differences due to its different implementation and embodiment. On the other hand, it’s also possible that this idea is philosophically incorrect: that there is no one key formalism, algorithm, structure or idea underlying general intelligence. The CogPrime approach is based on the intuition that to achieve human-level, roughly human- like general intelligence based on feasible computational resources, one needs an appropriate heterogeneous combination of algorithms and structures, each coping with different types of knowledge and different aspects of the problem of achieving goals in complex environments. 1.6.7 Integrative Cognitive Architecture Finally, to create advanced AGI one can try to build some sort of integrative cognitive architec- ture: a software system with multiple components that each carry out some cognitive function, and that connect together in a specific way to try to yield overall intelligence. Cognitive science gives us some guidance about the overall architecture, and computer science and neuroscience give us a lot of ideas about what to put in the different components. But still this approach is very complex and there is a lot of need for creative invention. This is the approach we consider most “serious” at present (at least until neuroscience ad- vances further). And, as will be discussed in depth in these pages, this is the approach we’ve chosen: CogPrime is an integrative AGI architecture. 1.6.8 Can Digital Computers Really Be Intelligent? All the AGI approaches we’ve just mentioned assume that it’s possible to make AGI on digital computers. While we suspect this is correct, we must note that it isn’t proven. It might be that — as Penrose [Pen96], Hameroff [Mam87] and others have argued — we need quantum computers or quantum gravity computers to make AGI. However, there is no evidence of this at this stage. Of course the brain like all matter is described by quantum mechanics, but this doesn’t imply that the brain is a “macroscopic quantum system” in a strong sense (like, say, a Bose-Einstein condensate). And even if the brain does use quantum phenomena in HOUSE_OVERSIGHT_012924