The better we come to understand the way intelligence develops in complex systems in the universe, the more clearly we’ll perceive our own role and limits in fostering technological evolutionary development. Top-down AI designers assume that human minds must furnish the most important goals to our AI systems as they develop. Certainly some such goal-assignment must occur, but it is becoming increasingly likely that this strategy has rapidly diminishing marginal returns. Evolutionary developmental computation (in both biological and technological systems) generally creates and discovers its own goals and encodes learned information in its own bottom-up, incremental, and context-dependent fashion, in a manner only partially accessible to our rational analysis. Ask yourself, for example, how much of your own mental learning has been due to inductive, trial-and-error internalization of experience, and how much was a deductive, architected, rationally-directed process. This topic, the self-organization of intelligence, is observed in all complex systems to the extent that each system’s physics allows, from molecules to minds.

In line with the new paradigm of evolutionary development of complex systems, we are learning that tomorrow’s most successful technological systems must be organic in nature. Self-organization emerges only through a process of cyclic development with limited evolution/variation within each cycle, a self-replicating development that becomes incrementally tuned for progressively greater self-assembly, self-repair, and self-reorganization, particularly at the lowest component levels. At the same time, progressive self-awareness (self-modelling) and general intelligence (environmental modelling) are emergent features of such systems.

Most of today’s technological systems are a long way from having these capacities. They are rigidly modular, and do not adapt to or interdepend with each other or their environment. They engage not in self-assembly, but are mostly externally constructed. In discussing proteins, Michael Denton reminds us of how far our technological systms have to go toward this ideal. Living molecular systems engage extensively in the features listed above. A proteins three dimensional shape is a result of a network of local and nonlocal physical interdependences (e.g., covalent, electrostatic, electrodynamic, steric, and solvent interactions). Both its assembly and its final form are a developmentally computed emergent feature of that interdependent network. A protein taken out of its interdependent milieu soon becomes nonfunctional, as its features are a convergent property of the interdependent system.

Today’s artificial neural networks, genetic algorithms, and evolutionary programs are promising examples of systems that demonstrate an already surprising degree of self-replication, self-assembly, self-repair, and self-reorganization, even at the component level. Implementing a hardware description language genotype, which in turn specifys a hardware-deployed neural net phenotype, and allowing this genotype-phenotype system to tune for ever more complex, modular, and interdependent neural net emergence is one future path likely to take us a lot further toward technological autonomy. At the same time, as Kurzweil has argued, advances in human brain scanning will allow us to instantiate ever more interdependent computational architectures directly into the technological substrate, architectures that the human mind will have less and less ability to model as we engage in the construction process. In this latter example, human beings are again acting as a decreasingly central part of the replication and variation loop for the continually improving technological substrate.

Collective or “swarm” computation is also a critical element of evolutionary development of complexity, and thus facilitating the emergence of systems we only partially understand, but collectively utilize (agents, distributed computation, biologically inspired computation), will be very important to achieving the emergences we desire. Linking physically-based self-replicating systems (SRS’s) to the emerging biologically inspired computational systems (neural networks, genetic algorithms, evolutionary systems) which are their current predecessors will be another important bottom up method, as first envisioned by John Von Neumann in the 1950’s.

Physical SRS’s, like today’s primitive self-replicating robots, provide an emerging body for the emerging mind of the coming machine intelligence, a way for it to learn, from the bottom up the myriad lessons of “common sense” interaction in the physical world (e.g., sensorimotor before instinctual before linguistic learning). As our simulation capacity, solid state physics, and fabrication systems allow us to develop ever more functional micro, meso and nano computational evolutionary hardware and evolutionary robotic SRS’s in coming decades (these will be functionally restricted versions of the “general assembler” goal in nanotechnology) we may come to view our technological systems simulation and fabrication capacity as their “DNA-guided protein synthesis”, their evolutionary hardware and software as their emerging “nervous system” and evolutionary robotics as the “body” of their emergent autonomous intelligence.

At best, we conscious humans may create selection pressures which reward for certain types of emergent complexity within the biologically inspired computation/SRS environment. At the same time, all our rational striving for a top down design and understanding of the AI we are now engaged in creating will remain an important (though ever decreasing) part of the process. Thus at this still-primitive stage of evolution of the coming autonomous technologic substrate a variety of differentiated, not-yet-convergent approaches to AI are to be expected. Comparing and contrasting the various paths available to us, and choosing carefully how to allocate our resources will be an essential part of humanity’s role as memetically driven catalysts of the coming transition.

In this spirit, let me now point out that on close inspection of the present state of AI research, one finds that there are very few investigators remaining who do not acknowledge the fundamental utility of evolution as a creative component in future AI systems. Those nonevolutionary, top-down AI approaches which still remain in vogue (whether classical symbolic or one of the many historical derivatives of this) are now few in number, and despite decades of iterative refinement, have consistently demonstrated only minor incremental improvements in performance and functional adaptation. To me, this is a strong indication that human-centric, human-envisioned design has reached a “saturation phase” in its attempt to add incremental complexity to technologic systems. We humans simply aren’t that smart, and the universe is showing us a much more powerful way to create complexity than by trying to develop or deduce it from logical first principles.

Thus we should not be surprised that on a human scale the handful of researchers working on systems to encode some kind of “general intelligence” in AI, after a surge of early and uneconomical attempts in the 1950’s to 1970’s, now pale in comparison to the 50,000 or so computer scientists who are investigating various forms of evolutionary computation. Over the last decade we have seen a growing number of real theoretical and commercial successes with genetic algorithms, genetic programming, evolutionary strategies, evolutionary programming, and other intrinsically chaotic and interdependent evolutionary computational approaches, even given their current primitive encapsulation of the critical evolutionary developmental aspects of genetic and neural computational systems and their currently severe hardware and software complexity limitations.

We may therefore expect that the numbers of those funded investigators who currently engage in this new evolutionary developmental paradigm will continue to swell exponentially in coming decades, as they are following what appears to be the most universally-permissive path to increasing adaptive computational complexity.

[via AccelerationWatch and HAL]