Natural and Artificial Meta-Configured Altricial Information-Processing Systems

The full variety of powerful information-processing mechanisms 'discovered' by evolution has not yet been re-discovered by scientists and engineers. By attending closely to the diversity of biological phenomena, we may gain new insights into (a) how evolution happens, (b) what sorts of mechanisms, forms of representation, types of learning and development and types of architectures have evolved, (c) how to explain ill-understood aspects of human and animal intelligence, and (d) new useful mechanisms for artificial systems. We analyse trade-offs common to both biological evolution and engineering design, and propose a kind of architecture that grows itself, using, among other things, genetically determined meta-competences that deploy powerful symbolic mechanisms to achieve various kinds of discontinuous learning, often through play and exploration, including development of an 'exosomatic' ontology, referring to things in the environment - in contrast with learning systems that discover only sensorimotor contingencies or adaptive mechanisms that make only minor modifications within a fixed architecture.