School of Computer Science THE UNIVERSITY OF BIRMINGHAM CoSy project CogX project

(DRAFT: BEING RECONSTRUCTED)

The Meta-Morphogenesis (MM) Project (or Meta-Project?)
(Combining and extending Turing's ideas about morphogenesis
and his earlier ideas about computation.)

Aaron Sloman
School of Computer Science, University of Birmingham.

Offers of collaboration welcome. I do not plan to apply for research funding, as that
would obstruct the research too much.

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Document history

This web site is
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html
Also accessible as: http://tinyurl.com/M-M-Gen

http://www.cs.bham.ac.uk/research/projects/cogaff/misc/m-m.html
A slightly messy PDF version is also available: http://tinyurl.com/BhamCog/misc/meta-morphogenesis.pdf
This is one of a set of documents on the meta-morphogenesis project listed below.

The latest addition is a draft speculative paper on the nature of mathematics and evolution of mathematicians (Sept 2013):
http://www.cs.bham.ac.uk/research/projects/cogaff/maths-evol-sloman.pdf

A partial index of a wider collection of discussion notes is in
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/AREADME.html

This (shortened) version installed: 21 Oct 2012
Previous (longer) version installed: 19 Oct 2011 now here.
Updated:
2 Aug 2013; 16 Aug 2013; 24 Aug 2013 (some re-formatting); 6 Sep 2013; 29 Sep 2013
2 Feb 2013; 24 Apr 2013; 4 May 2013; 20 May 2013; 17 Jun 2013; (Video fixed) 24 June 2013;
6 Dec 2012 19 Dec 2012; 21 Oct 2012 (Split in two: other part here.);
10 May 2012; 22 May 2012; 19 Jun 2012; 29 Jun 2012; 7 Jul 2012; 24 Aug 2012; 13 Oct 2012; 14 Nov 2012;
20 Oct 2011; 22 Nov 2011; 21 Feb 2012 (Appendix);5 Mar 2012; 19 Mar 2012; 23 Apr 2012;
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CONTENTS

Introductory/Overview Materials

The main concept of information used for this project
The concept of "information used by organisms or machines or biological processes
for various purposes" is central to this project. But it is not the concept unfortunately
labelled "information" by the great Claude Shannon and his many admirers. He understood
the differences but too many researchers ignore them. In fact many researchers think
that is the only concept of "information" we have. But there is a much older one.

The concept of information whose role in evolution, in animal perception, learning,
motivation, acting, interacting, thinking, asking, wondering, being puzzled, finding
answers (etc.) I am referring to, was already known to Jane Austen over a century before
Shannon's work, and to many others long before her. Several examples from her novel
'Pride and Prejudice' published in 1813, are presented here: 
    http://www.cs.bham.ac.uk/research/projects/cogaff/misc/austen-info.html
    Jane Austen's concept of information (As opposed to Claude Shannon's)
Readers may find it useful to try making a list of the kinds of information they use
in a typical day, and what they use those kinds for -- or, more realistically, in a
typical hour, such as the first hour after waking, including information used getting
light (if needed), deciding whether to get up, getting out of bed, getting dressed, ...

Further information about the Meta-Morphogenesis project:
Long PDF slide presentation introducing the Meta-Morphogenesis project
(Also flash version on slideshare.net.)

See also: Abstract for Meta-Morphogenesis tutorial
At: AGI 2012 -- Dec 11th Oxford
St Anne's College Oxford

Related Videos

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A growing collection of related papers and discussion notes:

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Return to list of contents
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Introductory material

Meta-Morphogenesis: Evolution and Development of Information-Processing Machinery
(Including (recursively) mechanisms for changing the mechanisms)

The universe is made up of matter, energy and information, interacting with
each other and producing new kinds of matter, energy, information and interaction.

How? How did all this come out of a cloud of dust?

A Protoplanetary Dust Cloud?
Protoplanetary disk
[NASA artist's impression of a protoplanetary disk, from WikiMedia]

In order to find explanations we first need much better descriptions of
what needs to be explained.

This is a multi-disciplinary project attempting to describe and explain the variety
of biological information-processing mechanisms involved in the production of new
biological information-processing mechanisms, on many time scales, between the
earliest days of the planet with no life, only physical and chemical structures,
including volcanic eruptions, asteroid impacts, solar and stellar radiation, and
many other physical/chemical processes (or perhaps starting even earlier, when
there was only a dust cloud in this part of the solar system?).

Evolution can be thought of as a (blind) Theorem Prover
(or theorem discoverer).

The "proofs" of discovered possibilities are implicit in evolutionary and/or developmental
trajectories.

The proofs demonstrate the possibility of 

    development of new forms of development
    evolution of new types of evolution
    learning new ways to learn
    evolution of new types of learning
        (including mathematical learning: by working things out
        without requiring empirical evidence)
    evolution of new forms of development
    development of new forms of learning
        (why can't a toddler learn quantum mechanics?)
    how new forms of learning support new forms of evolution
    how new forms of development support new forms of evolution
        (e.g. postponing sexual maturity until mate-selection mating
        and nurturing can be influenced by much learning)
    ....
    .... and ways in which social cultural evolution add to the mix
These processes produce new forms of representation, new ontologies and information
contents, new information-processing mechanisms, new sensory-motor morphologies, new
forms of control, new forms of social interaction, new forms of creativity, ... and
more. Some may even accelerate evolution.

A growing list of transitions in types of biological information-processing:
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/evolution-info-transitions.html
   Biology, Mathematics, Philosophy, and Evolution of Information Processing
Mathematics is at root a biological, not an anthropological, phenomenon
(as suggested by Wittgenstein).
But its possibility depends on deep features of the universe, some of which
evolution had to 'discover':
http://tinyurl.com/CogMisc/bio-math-phil.html
  An attempt to identify a major type of mathematical reasoning with precursors in
 perception and reasoning about affordances, not yet replicated in AI systems:
 http://tinyurl.com/CogMisc/triangle-theorem.html

Even in microbes
I suspect there's much still to be learnt about the varying challenges and opportunities
faced by microbes at various stages in their evolution, including new challenges produced
by environmental changes and new opportunities (e.g. for control) produced by previous
evolved features and competences -- and the mechanisms that evolved in response to those
challenges and opportunities.

Example: which organisms were first able to learn about an enduring spatial configuration
of resources, obstacles and dangers, only a tiny fragment of which can be sensed at any
one time?
What changes occurred to meet that need?

More examples to be collected here:
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/evolution-info-transitions.html
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NOTE:
Stuart Wray produced this sketch of some of these ideas on 5th Jun 2012, after reading
a draft workshop paper on Meta-morphogenesis and the Creativity of Evolution:
http://tinyurl.com/BhamCog/12.html#1203

For a (very) compressed history of information processing on our planet see
Evolution, Life and Mind: Some Startling Facts
http://tinyurl.com/BhamCog/misc/evolution-life-mind.html

For a messy, still growing, collection of examples relating to learning and development
see this web page on "Toddler theorems":
http://tinyurl.com/BhamCog/misc/toddler-theorems.html
(including an introduction to the idea of a "Domain").
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Related Talks
Related talks (PDF) can be found here: http://tinyurl.com/BhamCog/talks/
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What is Meta-Morphogenesis?
Draft answer (last revised: Aug 2013):

The study of meta-morphogenesis (MM) is the study of

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Presentation By Penrose, Manchester 2012
Added 12 Aug 2012
Roger Penrose seems to partially agree with one of the ideas here

At the Alan Turing centenary conference in Manchester (June 2012)
http://www.turing100.manchester.ac.uk/,
Roger Penrose gave the final keynote lecture, which was open to the public. His
lecture (The Problem of Modelling the Mathematical Mind) was recorded on video
and is now available online:
http://videolectures.net/turing100_penrose_mathematical_mind/

Questions from the audience were also recorded. Near the end of the video (at
approximately 1 hour 26 minutes from the start) I had a chance to suggest that
what he was trying to say about human consciousness and its role in mathematical
discovery might be expressed (perhaps more clearly) in terms of the kinds of
meta-cognitive functions required in animals, children, and future robots, as
well as mathematicians. The common process is first gaining expertise in some
domain (or micro-domain!) of experience and then using meta-cognitive mechanisms
that inspect the knowledge acquired so far and discover the possibility of
reorganising the information gained into a deeper, more powerful, generative
form. The best known example of this sort of transition is the transition in
human language development to use of a generative syntax. (At one point I
mistakenly referred to a "generative theorem" when I meant "generative theory".)

I suggested that something similar must have happened when early humans made the
discoveries, without the aid of mathematics teachers, that provided the basis of
Euclidean geometry (later systematised through social processes). I have
proposed that there are many examples, that have mostly gone unnoticed, of young
children discovering what I call "Toddler theorems", some of them probably also
discovered by other animals, as discussed in
http://tinyurl.com/BhamCog/misc/toddler-theorems.html.

This is also related to the ideas about "Representational Re-description" in the
work of Annette Karmiloff-Smith, presented in her 1992 book Beyond Modularity
discussed in http://tinyurl.com/BhamCog/misc/beyond-modularity.html

Penrose seemed to agree with my suggestion, and to accept that it might also
explain why the basis of some mathematical competences are biologically
valuable, which he had previously said he was doubtful about. I don't know
whether he realised he was agreeing to a proposal that instead of thinking of
consciousness as part of the explanation of human mathematics, we can switch to
thinking of the biological requirement for mathematical thinking as part of the
explanation of important kinds of human (and animal) consciousness.

This is also connected with the need to extend J.J.Gibson's theory
of perception of affordances discussed in http://tinyurl.com/BhamCog/talks/#gibson
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Return to list of contents
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PAPERS WITH FURTHER DETAILS

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EXISTING PAPERS AND PRESENTATIONS

Example papers and presentations I have written on this topic over the last five
decades (DPhil Thesis was in 1962), especially since the early 1990s.
(Currently this list duplicates the list in the Toddler theorems paper.)

PAPERS ON META-MORPHOGENESIS

RELEVANT PRESENTATIONS (PDF)

CLOSELY RELATED (To be expanded and re-ordered)

  1. Richard Dawkins, 'The Evolution of Evolvability',
    in Artificial Life: Proceedings of an Interdisciplinary Workshop on the Synthesis
    and Simulation of Living Systems,
    Ed. Chris G. Langton, Addison-Wesley, 1988, pp. 201--220. 
    Dawkins' paper is entirely about evolution of physical form, and of procedures
for producing physical forms. The idea of meta-morphogenesis includes evolution
of behaviours, evolution of information processing (including mechanisms for
producing and controlling behaviour), evolution of forms of learning, learning,
evolution of mechanisms of development of new information-processing
capabilities, evolution of abilities to alter the evolvability of all of those.
Dawkins paper is a useful introduction to the basic idea, with informative toy
examples.
  2. The Only Way is Up
    On A Tower of Abstractions for Biology
    Jasmin Fisher, Nir Piterman, and Moshe Y. Vardi
    17th International Symposium on Formal Methods, LNCS 6664, pp. 3-11, 2011
    http://www.cs.rice.edu/~vardi/papers/fm11a.pdf 
    Abstract:
 We draw an analogy between biology and computer hardware systems and argue for
 the need of a tower of abstractions to tame complexity of living systems. Just
 like in hardware design, where engineers use a tower of abstractions to produce
 the most complex man-made systems, we stress that in reverse engineering of
 biological systems; only by using a tower of abstractions we would be able to
 understand the "program of life".
  3. Beyond Modularity, by Annette Karmiloff-Smith MIT Press (1992)

  4. Kenneth Craik's 1943 book (The Nature of Explanation), written nearly
    70 years ago makes some major contributions to the meta-morphogenesis project by
    drawing attention to previously unnoticed problems about biological information
    processing in intelligent animals.
    For a draft incomplete discussion of his contribution, see
    http://tinyurl.com/CogMisc/kenneth-craik.html

  5. Natural and artificial meta-configured altricial information-processing systems
    Jackie Chappell and Aaron Sloman
    International Journal of Unconventional Computing, 3, 3, 2007, pp. 211--239,
    http://www.cs.bham.ac.uk/research/projects/cosy/papers/#tr0609 
    Abstract:
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,
  (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.
  6. Margaret Boden's work
    There is much relevant content in Margaret Boden's work, e.g. on purposive
    explanation in psychology, on achievements and limitations of AI, on creativity,
    her theoretical work on biology (especially the relations between life and mind)
    and her outstanding historical analyses of various aspects of the development of
    Cognitive Science:
    Mind As Machine: A history of Cognitive Science (Vols 1--2) (2006)
    http://www.cs.bham.ac.uk/research/projects/cogaff/misc/boden-mindasmachine.html
  7. Brian Goodwin, whom I met and talked to occasionally at Sussex University
    http://en.wikipedia.org/wiki/Brian_Goodwin
    expressed ideas in conversation (and in his publications which I did not read,
    mainly because I could not keep up with the mathematical details), had ideas
    about natural selection being only part of the story of how evolution works: he
    used to talk about "Laws of Form" constraining the possibilities for growth in
    ways that did not require genetic control. In retrospect I think some of the
    ideas behind the M-M project may have come from him, and before him from D'Arcy
    Thompson, Goethe and others. See Boden (2006)
    Sections 15x(b-d), Vol 2

    However, some of the "laws of form" are concerned with forms of information
    processing and how possibilities are enabled and constrained by (a) the physical
    mechanisms in which the information processing machinery (even virtual
    machinery) has to be implemented and (b) the environments with which organisms
    need to interact in order to develop, learn, live their lives and reproduce --
    some of which include other information processors: friends, foes, food,
    playmates, and things to observe or be observed by.
  8. Stuart Kauffman's work, e.g. see this useful overview by Gert Korthof
    http://home.wxs.nl/~gkorthof/kortho32.htm

    Kauffman's 1995 book is very approachable:
    At home in the universe: The search for laws of complexity
    http://www.amazon.com/At-Home-Universe-Self-Organization-Complexity/dp/0195111303

  9. Ideas of David Deutsch. See his old and new web sites:
    http://193.189.74.53/~qubitor/people/david/David.html
    http://www.qubit.org/people/david/
    (Not working when I last looked)
  10. TWo books by Jack Cohen (biologist) and Ian Stewart (mathematician)
    The Collapse of Chaos (1994)
    Figments of Reality: The Evolution of the Curious Mind (1997)
  11. Immanuel Kant's Critique of Pure Reason (1781)
    has relevant ideas and questions, but he lacked our present understanding of
    information processing (which is still too limited)
    http://archive.org/details/immanuelkantscri032379mbp
  12. Much of Jean Piaget's work is also relevant, especially his last two
    (closely related) books written with his collaborators: 
      Possibility and Necessity
    Vol 1. The role of possibility in cognitive development (1981)
    Vol 2. The role of necessity in cognitive development (1983)
    Tr. by Helga Feider from French in 1987
    Like Kant, he had deep observations but lacked an understanding of information
    processing mechanisms, required for explanatory theories.
  13. John McCarthy's 1996 paper "The Well Designed Child" is very relevant:
    http://www-formal.stanford.edu/jmc/child.html
    (Later published in the AI Journal, 172, 18, pp 2003--2014, 2008) 
      "Evolution solved a different problem than that of starting a baby with no a
  priori assumptions."
  "Animal behavior, including human intelligence, evolved to survive and succeed
  in this complex, partially observable and very slightly controllable world. The
  main features of this world have existed for several billion years and should not
  have to be learned anew by each person or animal."
  14. Ulric Neisser wrote in Cognition and Reality, W.H. Freeman., 1976. 
     "... we may have been lavishing too much effort on hypothetical models of the
 mind and not enough on analyzing the environment that the mind has been shaped
 to meet."
  15. Steve Burbeck's web site:
    http://www.evolutionofcomputing.org/
  16. Daniel Dennett's very readable little book is very relevant:
    Kinds of minds: towards an understanding of consciousness,
    Weidenfeld and Nicholson, London, 1996,
    http://www.amazon.com/Kinds-Minds-Understanding-Consciousness-Science/dp/0465073514

    This book, like much of what Dennett has written is mostly consistent with my
    own emphasis on the need to understand "the space of possible minds" if we wish
    to understand human minds. Simply trying to study human minds while ignoring all
    others is as misguided as trying to do chemistry by studying one complex
    molecule (e.g. haemoglobin) and ignoring all others.
  17. Dennett and I have also written similar things about how to think about
    discussions of "free will" in the light of changes produced by Biological
    evolution. Dennett 
      D.C. Dennett,
  Elbow Room: the varieties of free will worth wanting,
  Oxford: The Clarendon Press, 1984,
  (See also his later book Freedom Evolves)
    Sloman 
      A. Sloman, 'How to Dispose of the Free-Will Issue,'
  In AISB Quarterly, No 82, 1992, pp. 31--32,
  http://www.cs.bham.ac.uk/research/projects/cogaff/81-95.html#8,
  (Originally posted to Usenet some time earlier.)

  Also used (with my permission) as the basis for Chapter 2 of
      Stan Franklin,
      Artificial Minds, MIT Press, 1995,
      (Franklin expanded my notes.)
    Our main difference is that I don't regard what Dennett calls "the intentional
    stance" as a requirement for a science of mind, since reference to mental states
    and processes is not merely a sort of useful explanatory fiction: those states
    and processes, and qualia exist and their existence can be explained in terms of
    the operation of virtual machinery that is a product of biological evolution
    rather than human engineering. However, Dennett sometimes also seems to
    hold that view.
  18. Noam Chomsky's early work deeply influenced my thinking, especially the idea of
    generative forms of representation able to cope with arbitrary
    (essentially infinite) variation in structure (not just values of a fixed size
    vector, so popular in much current AI). See his three notions of 'adequacy',
    observational, descriptive and explanatory adequacy, in Aspects of the theory
    of syntax     (1965)
  19. As mentioned above, I think much of what Merlin Donald has
    written about evolution of consciousness is relevant to this project.
I know there are lots more related books and papers -- most of them not yet read by
me. I would welcome a volunteer collaborator (or a group of collaborators) to help
setting up an annotated online bibliography of notes, books, papers, discussions,
videos, etc. relevant to meta-morphogenesis, whether the label is used or not,
especially freely available open access documents, for reasons given here.
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Appendix: Schematic Summary

Transitions can occur in parts of organisms, in whole organisms, within a species, in
interacting groups of species, in societies, and in environments (though organisms
are part of the environment for conspecifics and for others).

A sample list of types of transition produced by biological mechanisms
The mechanisms include evolution by natural selection, individual learning, cultural
development and transmission, including changes in genomes as well as changes in
factors affecting gene expression.

  1. Change of physical shape (in individual, in species)
  2. Change in physical behaviour (in individual, in species)
  3. Change in information processing (in individual, in species)
    (including control of growth, metabolism, immune system, processing of
    perception, motive formation, motive selection, action selection, action
    control, learning, reasoning, ...)
  4. Change in developmental trajectory (physical, non-physical)
  5. Change in what can be learnt (in individual, in species)
  6. Change in type of interaction between individuals
    (in same species, across species, within `family unit', prey, predators, others...)
  7. Change in type of social organisation
    (including forms of collaboration, forms of nurturing, forms of education, forms of competition)
  8. Changes in mechanisms of evolution (evolution of evolvability (Dawkins, 1988))
  9. Changes in mechanisms of development
  10. Changes in mechanisms of learning
  11. Changes in mechanisms of interaction
  12. Changes in mechanisms of self-monitoring, self-control
  13. Introduction of new virtual machines, new forms of representation, new
    ontologies, new architectures
Note added 23 Oct 2012 An expanded version of the above list of transitions is being created in
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/evolution-info-transitions.html

These changes can interact and influence one another...

Types of Meta-Morphogenesis:
For any of the above biological changes B1, B2, B3,.. etc. and for
any environmental states or changes E1, E2, E3,... there can be influences
of the following forms ... Meta-Morphogenesis (MM):
Things that cause changes can produce new things that cause changes.
Old phenomena may be produced in new ways
   e.g. information acquired and ways of acquiring and using information can change.
Often new mechanisms can produce new biological phenomena
   e.g. organisms that can discover what they have learnt.
   organisms that make and use mathematical discoveries.
In particular, most forms of biological information processing that exist now are
products of parallel trajectories of biological information processing over many
stages of evolution and development, including cultural evolution in the case of
humans.

This is quite unlike use of evolutionary computation (GA, GP, etc.) with a fixed
evaluation function, often used to solve engineering problems.
For example, evaluation in natural evolution keeps changing, as environments,
including competitors, prey, symbionts, diseases, etc. change.

Return to list of contents
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Maintained by Aaron Sloman
School of Computer Science
The University of Birmingham

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