Colin Hales

The approach the majority of neuroscientists take to the question of how consciousness is generated, it is probably fair to say, is to ignore it. Although there are active research programs looking at correlates of consciousness, and explorations of informational properties of what might be relevant neural ensembles, the tacitly implied mechanism of consciousness in these approaches is that it somehow just happens. This reliance on a “magical emergence” of consciousness does not address the “objectively unreasonable” proposition that elements that have no attributes or properties that can be said to relate to consciousness somehow aggregate to produce it. Neuroscience has furnished evidence that neurons are fundamental to consciousness; at the fine and gross scale, aspects of our conscious experience depend on specific patterns of neural activity – in some way, the connectivity of neurons computes the features of our experience. So how do we get from knowing that some specific configurations of cells produce consciousness to understanding why this would be the case? Behind the voltages and currents electrophysiologists measure is a staggeringly complex system of electromagnetic fields – these are the fundamental physics of neurons and glia in the brain. The brain is entirely made of electromagnetism phenomena from the level of the atoms up. The EM field literally manifests the computations, or signaling, or information processing/activities performed by connected cellular ensembles that generate a 1st-person perspective. An investigation into the EM field at the cellular scale provides the possibility of identifying the outward signs of a mechanism in fundamental terms, as opposed to merely describing the correlates of our mental abstractions of it.

Our chronically impoverished explanatory capacity in respect of P-consciousness is highly suggestive of a problem with science itself, rather than its lack of acquisition of some particular knowledge. The hidden assumption built into science is that science itself is a completed human behaviour. Removal of this assumption is achieved through a simple revision to our science model which is constructed, outlined and named ‘dual aspect science’ (DAS). It is constructed with reference to existing science being ‘single aspect science’. DAS is consistent with and predictive of the very explanatory poverty that generated it and is simultaneously a seamless upgrade; no existing law of nature is altered or lost. The framework is completely empirically self-consistent and is validated empirically. DAS eliminates the behavioural inconsistencies currently inhabiting a world in which single aspect science has been inherited rather than chosen and in which its presuppositions are implemented through habit rather than by scientific examination of options by the scientists actually carrying out science. The proposed DAS framework provides a working vantage point from which an explanation of P-consciousness becomes expected and meaningful. The framework requires that we rediscover what we scientists do and then discover something new about ourselves: that how we have been doing science is not the entire story. Dual aspect science shows us what we have not been doing.

Scientific behavior is used as a benchmark to examine the truth status of computationalism (COMP) as a law of nature. A COMP-based artificial scientist is examined from three simple perspectives to see if they shed light on the truth or falsehood of COMP through its ability or otherwise, to deliver authentic original science on the a priori unknown like humans do. The first perspective (A) looks at the handling of ignorance and supports a claim that COMP is "trivially true" or "pragmatically false" in the sense that you can simulate a scientist if you already know everything, which is a state that renders the simulation possible but pointless. The second scenario (B) is more conclusive and unusual in that it reveals that the COMP scientist can never propose/debate that COMP is a law of nature. This marked difference between the human and the artificial scientist in this single, very specific circumstance, means that COMP cannot be true as a general claim. The third scenario (C) examines the artificial scientist's ability to do science on itself/humans to uncover the "law of nature" which results in itself. This scenario reveals that a successful test for scientific behavior by a COMP-based artificial scientist supports a claim that COMP is true. Such a test is quite practical and can be applied to an artificial scientist based on any design principle, not merely COMP. Scenario (C) also reveals a practical example of the COMP scientist's inability to handle informal systems (in the form of liars), which further undermines COMP. Overall, the result is that COMP is false, with certainty in one very specific, critical place. This lends support to the claims (i) that artificial general intelligence will not succeed based on COMP principles, and (ii) computationally enacted abstract models of human cognition will never create a mind.

This book discusses two main cultural problems behind the failure of machine consciousness and artificial general intelligence projects over many decades. The first problem recognizes that building a conscious AGI means building an artificial scientist. The book identifies the responsible pitfalls in mainstream scientific behavior and eliminates them by proposing a new operational framework for scientists called “Dual Aspect Science”. The second problem arises because scholars involved in machine consciousness and AGI essentially aim to replicate brains with computers. They are demonstrably not doing this, and this failure has been prevalent since the rise of computers. Instead, the book discusses the possibility of doing real empirical neuroscience by means of artificial materials that literally do what the brain does. Inspired by Thomas Kuhn, one of the most influential philosophers of science of the twentieth century, this compendium proposes a fresh perspective on machine consciousness, on AGI and, more generally, on how the machinery of science might need to change to accommodate it. Contents:PreambleIntroductionConsciousnessThe Route to Normal Science‘Normal’ ScienceThe Great BlockageCultural Learning Theory for ScientistsThe ‘Law of Scientific Behaviour’The Biology of Belief: Statement FormationHierarchy, Emergence and CausalityDual Aspect ScienceScientifically Testing for ConsciousnessThe Kuhnian Take: Wrapping UpMachine Consciousness and DAS Readership: Researchers, academics, professionals and graduate students in artificial intelligence, robotics, neuroscience, computational physics and nonlinear science. Keywords:Science;Neuroscience;Science of Consciousness;Machine Consciousness.

The ASSC has challenged student members to encounter and respond to a number of questions, one of which is: “ What kind of experiences are qualia? Qualia are usually described as the redness of red or the painfulness of pain. While most people would agree that qualia refer to the quality of subjective experiences, it is often difficult to judge whether less sensory aspects of experiences should be taken to accompany specific qualia. In order for the concept to be useful for driving neuroscientific research, it is important to determine the fundamental conditions for an experience to count as a quale in a meaningful way. Are there any critical experimental protocols to determine whether a certain experience counts as a quale?” For this student, it is a short walk from a request for elaboration of putative empirical method to the revealing of paradoxical behavior by scientists. The chronic intractability of a physics of qualia is not merely a problem for scientists. The problem can be shown to arise from scientists and from our implicit, inadequately examined behavioral options handed to us by our scientific forebears as underperforming conventions in respect of what constitutes scientific evidence and what we can do with it