4 1 Introduction
these algorithms and structures are integrated; and so far the integration has not been done in the correct way.
The human brain appears to be an integration of an assemblage of diverse structures and dynamics, built using common components and arranged according to a sensible cognitive archi- tecture. However, its algorithms and structures have been honed by evolution to work closely together — they are very tightly inter-adapted, in the same way that the different organs of the body are adapted to work together. Due to their close interoperation they give rise to the overall systemic behaviors that characterize human-like general intelligence. We believe that the main missing ingredient in AI so far is cognitive synergy: the fitting-together of differ- ent intelligent components into an appropriate cognitive architecture, in such a way that the components richly and dynamically support and assist each other, interrelating very closely in a similar manner to the components of the brain or body and thus giving rise to appropriate emergent structures and dynamics. This leads us to one of the central hypotheses underlying the CogPrime approach to AGI: that the cognitive synergy ensuing from integrating multiple symbolic and subsymbolic learning and memory components in an appro- priate cognitive architecture and environment, can yield robust intelligence at the human level and ultimately beyond.
The reason this sort of intimate integration has not yet been explored much is that it’s difficult on multiple levels, requiring the design of an architecture and its component algorithms with a view toward the structures and dynamics that will arise in the system once it is coupled with an appropriate environment. Typically, the AI algorithms and structures corresponding to different cognitive functions have been developed based on divergent theoretical principles, by disparate communities of researchers, and have been tuned for effective performance on different tasks in different environments. Making such diverse components work together in a truly synergetic and cooperative way is a tall order, yet we believe that this — rather than some particular algorithm, structure or architectural principle — is the “secret sauce” needed to create human-level AGI based on technologies available today.
1.5 Extraordinary Proof?
There is a saying that “extraordinary claims require extraordinary proof” and by that stan- dard, if one believes that having a design for an advanced AGI is an extraordinary claim, this book must be rated a failure. We don’t offer extraordinary proof that CogPrime, once fully implemented and educated, will be capable of human-level general intelligence and more.
It would be nice if we could offer mathematical proof that CogPrime has the potential we think it does, but at the current time mathematics is simply not up to the job. We’ll pursue this direction briefly in Chapter 7 and other chapters, where we'll clarify exactly what kind of mathematical claim “CogPrime has the potential for human-level intelligence” turns out to be. Once this has been clarified, it will be clear that current mathematical knowledge does not yet let us evaluate, or even fully formalize, this kind of claim. Perhaps one day rigorous and detailed analyses of practical AGI designs will be feasible — and we look forward to that day — but it’s not here yet.
Also, it would of course be profoundly exciting if we could offer dramatic practical demon- strations of CogPrime’s capabilities. We do have a partial software implementation, in the OpenCogPrime system, but currently the things OpenCogPrime does are too simple to really
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