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      What is consciousness? Artificial intelligence, real intelligence, quantum mind and qualia

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      Biological Journal of the Linnean Society
      Oxford University Press (OUP)

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          Abstract

          We approach the question ‘What is consciousness?’ in a new way, not as Descartes’ ‘systematic doubt’, but as how organisms find their way in their world. Finding one’s way involves finding possible uses of features of the world that might be beneficial or avoiding those that might be harmful. ‘Possible uses of X to accomplish Y’ are ‘affordances’. The number of uses of X is indefinite (or unknown), the different uses are unordered, are not listable, and are not deducible from one another. All biological adaptations are either affordances seized by heritable variation and selection or, far faster, by the organism acting in its world finding uses of X to accomplish Y. Based on this, we reach rather astonishing conclusions:

          1. Artificial general intelligence based on universal Turing machines (UTMs) is not possible, since UTMs cannot ‘find’ novel affordances.

          2. Brain-mind is not purely classical physics for no classical physics system can be an analogue computer whose dynamical behaviour can be isomorphic to ‘possible uses’.

          3. Brain-mind must be partly quantum—supported by increasing evidence at 6.0 to 7.3 sigma.

          4. Based on Heisenberg’s interpretation of the quantum state as ‘potentia’ converted to ‘actuals’ by measurement, where this interpretation is not a substance dualism, a natural hypothesis is that mind actualizes potentia. This is supported at 5.2 sigma. Then mind’s actualizations of entangled brain-mind-world states are experienced as qualia and allow ‘seeing’ or ‘perceiving’ of uses of X to accomplish Y. We can and do jury-rig. Computers cannot.

          5. Beyond familiar quantum computers, we discuss the potentialities of trans-Turing systems.

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          Most cited references68

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          I.—COMPUTING MACHINERY AND INTELLIGENCE

          A Turing (1950)
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            The criticality hypothesis: how local cortical networks might optimize information processing.

            John Beggs (2008)
            Early theoretical and simulation work independently undertaken by Packard, Langton and Kauffman suggested that adaptability and computational power would be optimized in systems at the 'edge of chaos', at a critical point in a phase transition between total randomness and boring order. This provocative hypothesis has received much attention, but biological experiments supporting it have been relatively few. Here, we review recent experiments on networks of cortical neurons, showing that they appear to be operating near the critical point. Simulation studies capture the main features of these data and suggest that criticality may allow cortical networks to optimize information processing. These simulations lead to predictions that could be tested in the near future, possibly providing further experimental evidence for the criticality hypothesis.
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              The symbol grounding problem

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                Author and article information

                Journal
                Biological Journal of the Linnean Society
                Oxford University Press (OUP)
                0024-4066
                1095-8312
                August 24 2022
                August 24 2022
                Article
                10.1093/biolinnean/blac092
                47d6c05b-0a7c-4e6e-9ed6-5d2051e4409a
                © 2022

                https://creativecommons.org/licenses/by-nc/4.0/

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