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New developments in the Meta-Configured Genome theory

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If you have a question about this talk, please contact Hector Basevi.

More detailed overview:

Abstract: This joint presentation follows on from the presentation by AS of the idea of a Meta-Configured Genome (MCG) in his practice talk in CS on 2nd Sept 2019, for the Models of Consciousness conference in the Oxford Mathematical Institute September 9 – 12, 2019.

Recordings of the conference talks are available here:

Key MCG ideas, developed in collaboration with Jackie Chappell, are elaborated in some notes and a short video here:

Overview: Further details regarding the seminar can be found here: ati-sem.html

The central idea is that an organism’s interactions with its environment during early phases of gene expression can cause information to be collected that provides ‘parameters’ for later stages of gene expression that are only partly specified genetically. This pattern can be repeated at multiple stages of development in an individual. This has many interesting consequences

Peter later noticed connections between the MCG theory and aspects of gene transcription discovered by biologists that vastly enrich the ways in which proteins are produced based on genome codes, going far beyond the original “Central dogma of molecular biology”: genome-triplets of base pairs—> aminoacid—> protein.

Many variants have been discovered, including immune responses, as summarised here: After an encounter with a new pathogen, the adaptive immune system often “remembers” the pathogen, allowing for a faster response if the pathogen ever attacks again.

This is chemical, not neural, remembering. We suggest that, in a similar way, a shared meta-configured genome implemented chemically can produce dramatically different effects in members of the same species in different physical terrain requiring different forms of behaviour, or in different cultures, in the same species, though using far more complex mechanisms than antibodies. This may explain why neurons contain such varied and complicated chemical mechanisms, not required for the simple arithmetical oprations postulated by neural net learning mechanisms.

Perhaps the most striking example of what the MCG theory potentially explains is the ability of the a shared multi-layer human genome to produce multiple stages of development of competences in thousands of languages, that differ at all levels from the basic sounds used (or signs, in the case of sign-languages), up to modes of composition of phrases, sentences and beyond (e.g. However…, Therefore…, ... although…).

So, instead of all learning being based on abstractions and probabilistic generalisations from statistical data collected by individuals (as in currently proposed neural net models), we suggest that chemistry-based ‘compositional’ processes and mechanisms are involved in development of many forms of information processing, including linguistic competences and increasingly sophisticated types of mathematical spatial reasoning used by ancient geometers (Archimedes, Zeno, etc.) that don’t seem to be explicable using familiar logic-based, rule-based or neural net based AI mechanisms.

Standard statistics-based neural mechanisms cannot explain or even represent discovery of new impossiblities or necessary consequences (e.g. Pythagoras’ theorem, or the impossibility of a largest prime number).

These evolutionary products are examples of the metaphysical creativity of biological evolution, which continually produces new types of entity, illustrating the concept of metaphysical causation developed by Alastair Wilson, Birmingham philosopher.

Can digital computers ever replicate all the capabilities of sub-neural chemistry-based computations using a mixture of discrete and continuous processing? Perhaps Turing thought not, in 1952, when he published “The chemical basis of morphogenesis”.

Many details are still missing in this theory, and we invite collaboration to fill the gaps.

(Please report errors here to Aaron Sloman)

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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