Machine Hyperconsciousness

Individual animal consciousness appears limited to a single giant component of interacting cognitive modules, instantiating a shifting, highly tunable, Global Workspace. Human institutions, by contrast, can support several, often many, such giant components simultaneously, although they generally function far more slowly than the minds of the individuals who compose them. Machines having multiple global workspaces -- hyperconscious machines -- should, however, be able to operate at the few hundred milliseconds characteistic of individual consciousness. Such multitasking -- machine or institutional -- while clearly limiting the phenomenon of inattentional blindness, does not eliminate it, and introduces characteristic failure modes involving the distortion of information sent between global workspaces. This suggests that machines explicitly designed along these principles, while highly efficient at certain sets of tasks, remain subject to canonical and idiosyncratic failure patterns analogous to, but more complicated than, those explored in Wallace (2006a). By contrast, institutions, facing similar challenges, are usually deeply embedded in a highly stabilizing cultural matrix of law, custom, and tradition which has evolved over many centuries. Parallel development of analogous engineering strategies, directed toward ensuring an 'ethical' device, would seem requisite to the sucessful application of any form of hyperconscious machine technology

Biologically inspired distributed machine cognition: a new formal approach to hyperparallel computation

The irresistable march toward multiple-core chip technology presents currently intractable pdrogramming challenges. High level mental processes in many animals, and their analogs for social structures, appear similarly massively parallel, and recent mathematical models addressing them may be adaptable to the multi-core programming problem

New mathematical foundations for AI and Alife: Are the necessary conditions for animal consciousness sufficient for the design of intelligent machines?

Rodney Brooks' call for 'new mathematics' to revitalize the disciplines of artificial intelligence and artificial life can be answered by adaptation of what Adams has called 'the informational turn in philosophy' and by the novel perspectives that program gives into empirical studies of animal cognition and consciousness. Going backward from the necessary conditions communication theory imposes on cognition and consciousness to sufficient conditions for machine design is, however, an extraordinarily difficult engineering task. The most likely use of the first generations of conscious machines will be to model the various forms of psychopathology, since we have little or no understanding of how consciousness is stabilized in humans or other animals

A Cognitive Science Based Machine Learning Architecture

In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or cognitive robot will be capable of three fundamental, continuously active, humanlike learning mechanisms:\ud 1) perceptual learning, the learning of new objects, categories, relations, etc.,\ud 2) episodic learning of events, the what, where, and when,\ud 3) procedural learning, the learning of new actions and action sequences with which to accomplish new tasks. The paper argues for the use of modular components, each specializing in implementing individual facets of human and animal cognition, as a viable approach towards achieving general intelligence

Can biological quantum networks solve NP-hard problems?

There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines. During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind. This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness and cognition. The purpose is to be able to identify and evaluate instances where quantum effects might play a significant role in cognitive processes. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. So, the conclusion is that biological quantum networks can only approximately solve small instances of NP-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to efficiently solve NP-hard problems approximately. In the end it is a question of precision - Nature is approximate.Comment: 38 page