School of Computer Science THE UNIVERSITY OF BIRMINGHAM Ghost Machine

LIST OF CONTENTS

(ETERNAL DRAFT: CONTINUALLY BEING RECONSTRUCTED)
(like life on earth)

Major reorganisation August--September 2014.

The Meta-Morphogenesis (M-M) Project
(or Meta-Project?)

How can a cloud of dust give birth to a planet
full of living things as diverse as life on Earth?


A Protoplanetary Dust Cloud?
Protoplanetary disk

    [NASA artist's impression of a protoplanetary disk, from WikiMedia]

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CONTENTS

25 Aug 2014
Some of the contents have been moved to a file containing references:
Publications and references related to the Meta-Morphogenesis Project

KEY QUESTIONS

How can a cloud of dust give birth to a planet full of living things as diverse as life on Earth?

Old questions
Many have asked: what sorts of physical and chemical mechanisms could make that possible, at various stages in evolution, or various stages in individual development (epigenesis) in various types of organism, group or ecosystem. They have also asked: what sorts of morphology (physical structure) and behaviour are needed at various stages of evolution or development.

Newer questions
This project asks: what forms of information-processing (computation) and what information-processing mechanisms are required, to make the production and diversification of life possible; how do the mechanisms, the forms of representation (encodings), and uses of information all evolve and develop and what new forms of life do they support, or in some cases interfere with? What information contents were or are used by organisms, or parts of organisms, at various stages of evolution, at various stages of individual development, in various group interactions.

In short, what are the causal roles of information in living things, and how do the roles change over time, in individuals, in species, in groups and in larger systems?

An Example: from dinosaurs to birds
A recent news article announced that

"Scientists finally decode how dinosaurs turned into birds and learned how to fly"
http://www.techtimes.com/articles/16652/20140928/scientists-decode-dinosaurs-birds-learned-fly.htm -- a somewhat over-enthusiastic summary of this article:

S.L. Brusatte, G.T. Lloyd, S. C. Wang and M. A. Norell, (2014) Gradual Assembly of Avian Body Plan Culminated in Rapid Rates of Evolution across the Dinosaur-Bird Transition, Current Biology, 24, pp. 1--7,
http://dx.doi.org/10.1016/j.cub.2014.08.034},

The Current Biology paper says nothing about learning or indeed any development of information-processing, referring only to "a ready source of data for examining trends in anatomical evolution across the dinosaur-bird transition". If this project has its expected influence future research will emphasise the need to identify trends in information processing capabilities, including modifications of forms of representation and modes of processing of information involved in vision, in control of airborne motion, types of planning required for identifying possible trajectories and landing places, and in some cases construction of nests in trees and other high places. It is arguable (as Karl Popper suggested in 1976) that the formation of new sorts of motives and plans has to precede morphological and behavioural evolutionary changes. This could involve changes in the information processing architecture to cope with new complex high speed decision-making. See: Popper (1976) and Popper (1977)

As the figure below indicates: there were far older changes in requirements and mechanisms for information-processing in the much earlier evolutionary transitions between different kinds of microbes, and from microorganisms onwards.

Changing information processing
requirements and capabilities
evo-info

For more on the concept of information used here, and the varieties of information processing in biology, see below.

Mathematical questions
What mathematical possibilities and necessities enable, constrain and shape the options for natural selection, for epigenesis, for individual competences, for cultures, for ecosystems?

What mathematical constraints? -- Topological, geometrical, physical, chemical, biological, computational, epistemological, linguistic, motivational?

D'Arcy Thompson, Brian Goodwin, and researchers included in a book of tributes to Goodwin, focused mainly on geometric and topological changes and constraints in evolution and development of physical forms (though I have not yet read all the papers carefully).

Our concern is with mathematical structures and constraints relevant to types of information content, forms of representation of information, modes of reasoning, types of control of behaviour, forms of learning, and other uses of information -- which are much less visible, leave no fossil records and their study is still in its infancy. Far fewer researchers are equipped to think about them.
(Perhaps nobody is: if key ideas are yet to be discovered.)

In contrast with the majority of evolutionary research, investigation of evolution, development, discovery, learning, and communication of types of information-processing relevant to living things of all sorts is the core aim of this project.

Blind theorem-proving
Is evolution more of a blind theorem prover than a blind watchmaker -- proving theorems about what is possible?

Every time some new physical feature, behaviour, or mechanism arises in a living organism that constitutes an implicit discovery that that sort of thing is possible. The evolutionary or developmental history contains an implicit proof that it is possible, but extracting the proof at the right level of abstraction may require sophisticated mathematical abilities that do not evolve till much later. The meta-cognitive abilities even to notice that such discoveries have been made, which require a highly specialised form of information processing competence, did not evolve till very recently (using a mixture of biological and cultural evolution).

Yet evolution seems to have noticed implicitly, insofar as it discovered not only very particular solutions, but also generalised patterns that were then instantiated in diverse particular cases. The examples studied by Thompson and others illustrate this: A genome does not specify the precise shape and size of an organism or its parts, but rather a network of relationships between possibilities that can vary between individuals, but even more remarkably, can vary within each individual during that individual's growth and behavioural development (e.g. learning to control movements while size, shape, weight, and weight distribution all change). Another examples is use of a basic collection of learning abilities to bootstrap abilities to learn how to use increasingly sophisticated features of the prevailing language or languages. The evolved mechanisms in humans, somehow provide transitions between having various competences and becoming able (using late developing genetic mechanisms, or learning) to think about the competences and help others acquire them, one of the processes labelled "Representational Redescription" in Karmiloff-Smith (1992)

Blind mathematical composition
How do products of evolution combine with one another and with other environmental factors to form niches (sets of requirements) enabling and constraining future products of evolution (future designs partially matching the requirements) in multi-level dynamical systems constantly generating new dynamical systems, with new possible trajectories, and new feedback control mechanisms, in individuals, in social groups, in ecosystems, and now in multiple global villages?

How can the genotype available to a newly born or hatched animal make possible hugely (infinitely?) varied developmental trajectories in different environments, e.g. squirrels in different gardens with (mostly) shared genomes learning to defeat new "squirrel-proof" bird-feeders, and humans learning any one (or a few) of several thousand very different human languages, absorbing whatever culture the child grows up in, acquiring competences relevant to local geographical features, local fauna and flora, local sources of food, shelter and danger, personalities of local conspecifics, etc. and in some cases creatively extending those environments through new inventions, new discoveries, new works of art, new moral teachings, new mathematical proofs, etc. One common answer is that they all use some general purpose learning mechanism, but the general purpose mechanisms that I've heard of operate on compressing bit-strings, or symbol-streams, and don't seem to be capable of learning geometrical or topological facts or skills, including the skills of a squirrel or a mathematician. Compare the developmental model of Chappell and Sloman (2007) depicted below -- extending Waddington's epigenetic landscape idea.

This is why proposing a behavioural test for intelligence is misguided (as Turing understood): no bounded behavioural test can establish the presence of all that potential. A test that indicates lack of intelligence may simply have been unsuited to the individual's capabilities. See
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/turing-test-2014.html

Do we know enough about information-processing?
Are known forms of computation rich enough to provide such a genotype, or are there still secrets to be uncovered in products of evolution?

What are the (mathematical) properties of physics and chemistry that enable a protoplanetary dust cloud to produce machines that can ask questions like these?

Is there something about chemistry that we have not yet understood? Only with the properties of chemistry do we seem to combine three necessary features of life: energy storage and transformation, mechanical structures that can act on the environment and mechanisms for storing, using, copying, and transforming information Ganti (2003). Chemistry builds brains, at least in their early stages. Perhaps it does more than we know even after it has constructed neural mechanisms?

Related questions:
Have evolutionary and developmental processes produced biological machines that are intelligent enough to find the answers, or understand them if found? How?

Can schools and universities provide the sort of education required for researchers and teachers in this project?


The Meta-Morphogenesis project attempts to combine and extend Turing's ideas about morphogenesis and his earlier ideas about discrete computation, in the light of what we've learnt since 1952 from computer science, artificial intelligence, computer systems engineering, biology, neuroscience, linguistics, psychology, chemistry, physics, mathematics, and philosophy. Most philosophers and scientists are unaware of the deep significance of what we have learnt about many forms of virtual machinery.


NOTE (24 Aug 2014):
Most of the references have been moved to a separate file, which includes documents on this web site relevant to the M-M project:
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/m-m-related.html
Publications and references related to the Meta-Morphogenesis Project

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Stuart Wray's sketch of meta-morphogenesis
On 5th Jun 2012, Stuart Wray produced this sketch of the ideas in this project:

(CLICK TO EXPAND)
Wray-MM

after reading a draft paper on Meta-morphogenesis and the Creativity of Evolution: http://www.cs.bham.ac.uk/research/projects/cogaff//12.html#1203


The main focus of this project:
Transitions in biological information-processing

A vast amount of research has been and is being done on the production by natural selection of new physical and geometrical forms of organisms, of many sizes and types, and production of new behaviours (e.g. J. Maynard Smith and E. Szathmáry, (1995), (1999), and Pallen (2009), among many others mentioned on Gert Korthof's web site).

The Meta-Morphogenesis (M-M or MM) project focuses instead on production of new types of biological information processing, including information-based control mechanisms, whether used for reproduction, growth, development, metabolism, perception, motor control, learning (including creation of new ontologies and new forms of representation), motive formation, planning, planned or unplanned behaviours, meta-cognition, communication, daydreaming, explaining, theory change, mathematical discovery, mathematical proofs, enjoying and producing art, or anything else. All new forms of computation that arise during evolution, development or interaction with other organisms are included. This requires use of a very general notion of "computation", or "information processing", that is not restricted to use of bit-based computers.

The changes in information processing include (a) what is done (as indicated in the previous paragraph), (b) why it is done, e.g. what benefits, if any, result, (c) what the information used is about (e.g. what it refers to, which can include past, present, future, remote, and non-existent entities, events, etc.) and (d) how all that is done, which refers to types of information bearers, mechanisms for analysing, transforming, constructing, comparing, storing, retrieving information bearers, types of information processing architectures, combining different forms of information processing in larger wholes, types of self-monitoring, self-modulation, self-repair, self-extension, types of competition, types of conflict resolution, types of interrupt mechanism, use of virtual machinery, including multi-layer machines, distributed information-processing (involving several different individuals, or a whole community) and many more.

As explained below, the ability of natural selection to be a sort of "blind mathematician", discovering and using mathematical structures, seems to be crucial -- refuting philosophical claims that mathematics is a human creation.
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LIST OF CONTENTS
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Why Meta-Morphogenesis?

.... Because the changes produced by the mechanisms of development and change include modified mechanisms for producing new changes in the mechanisms producing development and change.

Natural selection (or the biosphere) is a bit like a young child that has begun to learn, but has no idea that it is learning, what it is learning, how it is learning, why it is learning, what it will do with what it has learnt, why what it has learnt works and why what it has learnt sometimes proves inadequate, either for individuals or for whole species.

A difference is that over billions of years natural selection modifies its information-processing abilities far more than any child can do in a human lifetime, and modifies them in parallel in different ways. Moreover, at least on one planet, it has recently produced some individuals that have begun to understand some of what the evolutionary mechanisms produce without understanding.

The reproductive mechanisms do not normally produce ready-made full understanders, but individuals empowered to grow their understanding guided by the environment and by what some of their forebears and peers have already understood.

Some of those evolutionary changes bear a high level resemblance to the processes in individual development in animals described as "Representational Redescription" in Karmiloff-Smith (1992). In particular, it seems that increases in competence both in evolution and in individual development involve mechanisms that partition discoveries into domains with mathematical structures that can be discovered by appropriate domain-related mechanisms (not merely the use of universally applicable statistical learning techniques as some have supposed). See also the quote from McCarthy below, and the Chappell-Sloman proposal (below).


The key idea (Added 8 Aug 2014)

The key idea: evolution changes evolutionary processes and mechanisms, development changes developmental processes and mechanisms, individual learning changes individual learning processes and mechanisms, cultural evolution changes cultural evolutionary processes and mechanisms. Moreover, each of these processes and mechanisms of change can impact on the others over appropriate time-scales. If all that is correct, attempts to characterise any of those processes or mechanisms in a uniform way will lead to erroneous theories.

For example, natural selection may seem to be a uniform process, but what it does depends both on the mechanisms generating options between which selections can be made, and the selection mechanisms, which in turn depend partly on external constraints and opportunities -- niches. The points summarised above imply that both the types of options and the selection mechanisms can change dramatically.

Those modifications include: changes in physical and chemical structures and processes (that require, and also make possible, more complex information processing), changes in reproductive machinery, changes in genome-driven or partly genome-driven patterns of individual development (epigenesis) both across generations and within an individual's development, changes in the relative contributions of genome and environment and the stages at which they interact in individual development, changes in forms of adaptation and learning by individuals, changes in forms of sensing, perceiving and acting, changes in modes of communication and control between subsystems in an organism, changes in information-processing architectures within which diverse subsystems can interact, communicate, cooperate, compete and develop, changes in modes of communication and control between organisms, changes in types of cooperative or symbiotic processing, changes in requirements for and forms of competition, changes in abilities to acquire and use information about oneself and about other individuals (requiring two different but related forms of meta-cognition), changes in how parents influence offspring in their learning and development, changes in how groups of individuals acquire, use and transmit information, changes in how societies and cultures interact, including interactions involving new technologies, changes in the ways in which the physical environment produces new challenges and opportunities for information-processing in organisms of different kinds, including humans (sometimes as a result of biological processes, or as a result of other processes, e.g. geological events, asteroid impacts, climate changes, etc.) and changes in the ways all these processes influence one another.

One of the most important discoveries of biological evolution was the power of "generative" forms of representation of information: e.g. encoding information using trees and networks of information, whose nodes can be either arbitrary non-decomposable objects, or structured (decomposable) objects composed of other objects, for example trees and networks. The need for such meaning structures is clear in connection with the contents of complex sentences, with parts that have parts that have parts, but also mathematical formulae and proofs, complex intentions and action plans.

The ability to create and operate on such structures has been a pervasive feature of AI programming languages, often described as symbolic programming languages, which typically also provide standard instructions for operating on numbers of various sorts. Without this sort of capability, human language, and, I suspect, powerful animal vision systems, could not have evolved. This is why widely used forms of representation using vectors of scalar values are inadequate for explaining how organisms work. That doesn't even suffice for representing chemical structures and processes.

This is not intended to be a complete list of information processing novelties produced by natural selection. Extending the list, filling in details, and testing ideas by empirical research into processes and products of evolution, building working models to check the feasibility of the theories, and addressing a variety of closely related philosophical problems, including problems about relations between mind and body, are all among the long term aims of the M-M project. (Yes -- it's potentially a huge, long term project.)

Achieving such goals will require, among other things, major advances in AI and robotics in order to be able to test theories of how organisms work, and may even require novel forms of physical computing machinery, for instance if some of the functions of chemical information processing, with their mixtures of continuous and discrete changes, cannot be replicated in digital computers; and new kinds of mathematics may be required, for reasoning about how some of the systems work.

In the process we can expect many old philosophical problems to be solved or dissolved and many new ones to emerge.

The remainder of this document expands on some of these points and provides links to other, related documents on this web site and to relevant publications. (A partial list)

Offers of collaboration welcome. I have no funds for this research, and do not intend to apply for funds. Others may do so.

Aaron Sloman (School of Computer Science, University of Birmingham.)
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Are babies born with empty minds plus a learning machine?

Some researchers regard the pinnacle of evolutionary design as a totally general, domain-independent learning mechanism, which allows individuals to learn in any environment by discovering statistical relationships between sensory inputs and motor outputs; whereas there seems to be plenty of evidence that humans have different kinds of learning capabilities, used at different stages of development or for different domains of structures and processes.

Compare the views of Neisser (2007) and John McCarthy who wrote:

"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." McCarthy (1996/2008)

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The link with Alan Turing

The idea of the Meta-Morphogenesis project arose from an invitation from Barry Cooper, co-editor of the award-winning book
"Alan Turing: His Work and Impact".
http://www.amazon.co.uk/dp/0123869803/
2013 PROSE Award announcements
Detailed list of contents and contributors.
After submitting my three promised papers I found that I was also expected to contribute to part IV (as a result of a misunderstanding). So I read Turing's 1952 paper on Morphogenesis, about which I previously had only very vague knowledge.

Turing's paper is not an easy read, especially for non-mathematicians, but there is a very readable introduction to the ideas in Margaret Boden's magnum opus Boden (2006). In particular, section 15.iv ("Turing's Biological Turn") gives a summary of Turing's work on chemistry-based morphogenesis (which she had read and admired decades earlier).

The previous section 15iii (Mathematical Biology Begins) summarising work by D'Arcy Thompson is also very relevant. E.g. she writes:

Accordingly, D'Arcy Thompson tried to relate morphology to physics, and to the dynamical processes involved in bodily growth. He suggested that very general physical (as opposed to specific chemical or genetic) constraints could interact to make some biological forms possible, or even necessary, while others are impossible.
Boden (2006) Vol 2, 15.iii.a: "Of growth and form" pp 1256
That is closely connected with the view of evolution as a "blind theorem prover", explained below.

Turing's 1952 paper made a deep impression on me, and led me to wonder what Turing might have done if he had lived longer. My tentative (presumptious?) answer was that he might have worked on what I've called The Meta-Morphogenesis project, summarised here. The proposal for a Meta-Morphogenesis project, was first presented as a chapter (written in 2011) published as part of the Turing volume (published in 2013):

A. Sloman, Virtual Machinery and Evolution of Mind (Part 3) Meta-Morphogenesis: Evolution of Information-Processing Machinery, in
Alan Turing - His Work and Impact, Eds. S. B. Cooper and J. van Leeuwen, Elsevier, Amsterdam, 2013, pp. 849-856,
http://www.cs.bham.ac.uk/research/projects/cogaff/11.html#1106d

A piece of evidence that Turing might have been interested: According to his mother, he had always been interested in living things, as depicted by her in this famous drawing: https://www.commondreams.org/sites/commondreams.org/files/imce-images/turing_mother_drawing.jpg

Margaret Boden's commentary on Turing's work on morphogenesis provides this additional piece of evidence

For the last few years of his life, Turing's energy went primarily into what he called "my mathematical theory of embryology". Indeed, after writing the first Manchester programming manual in 1950, he neglected his duties in the computing laboratory there as a result of his new interest.
Boden (2006) section "15.iv. Turing's Biological Turn" (page 1261)

Perhaps he would have moved (by analogy with some of his earlier moves) from studying embryology to studying the origins of embryology deep in the evolutionary past of the project: the basis of the M-M project. (Later I'll discuss another link with Boden's work: her ideas on creativity and the varieties of creativity in natural selection (including ontological creativity, required for production of new types of virtual machinery) mentioned briefly below.

This is a complex, multi-faceted project, and could take several decades, or even much longer. Some of the main ideas are elaborated below, and in other web pages referred to on a separate page. But at present everything is provisional.
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How could all life, and products of life, on Earth come out of a cloud of dust that converged to form a planet?

Alan Turing wrote, in a comment that is almost universally ignored, though it occurred in one of his most widely cited papers:
"In the nervous system chemical phenomena are at least as important as electrical." in 'Computing machinery and intelligence', Mind, 59, 1950, pp. 433--460
I wonder if he had thought about the significance of chemistry for evolution of information processing mechanisms rich enough to support minds in a physical universe.

Steps towards an answer
Evolved information-processing -- in animals and machines.
(A huge, long-term multi-disciplinary project.)

Core questions and ideas
How can natural selection produce minds on a lifeless planet? To fully understand our origins we must combine familiar ideas about natural selection with ideas unavailable to Darwin and Wallace, about evolution of information processing functions and mechanisms, since the simplest organisms in chemical soups billions of years ago.

Many research fields can contribute, including: genetics, microbiology, ethology, developmental psychology, neuroscience, linguistics, anthropology, philosophy of science, philosophy of mind, computer science, Artificial Intelligence and robotics, raising new questions about what evolution achieved and how it did so.

Explanation by natural selection is not enough

Graham Bell writes in his book Selection: The Mechanism of Evolution

    Living complexity cannot be explained except through selection and does
    not require any other category of explanation whatsoever.
No: adequate explanations need to mention both selection mechanisms and enabling mechanisms, as I am sure Bell is aware.

Without enabling mechanisms, selection processes will not have a supply of new working/viable options to choose from. In that case the selection mechanisms cannot select new viable options.

Both the selection mechanisms and the enabling mechanisms can change during evolution (partly by influencing each other).

There is a useful web site listing common misconceptions about evolution here: http://evolution.berkeley.edu/evolibrary/misconceptions_teacherfaq.php
It does not bring out (or try to bring out) the full variety of types of explanation of evolutionary phenomena. E.g. Computer systems engineers have been discovering or inventing new types of information processing for over half a century -- especially new types of virtual machinery. There are good reasons for thinking that biological evolution made use of a similar discovery very much earlier, for good reasons, some of them summarised here.

Systems biologists are constantly discovering new biological types of informed control (information-based control). However, there may be types of biological enabling mechanisms (e.g. forms of chemical or biological computation) that we have not yet learnt about - and that may prevent us understanding some of the transitions in evolution, e.g. some changes in reasoning powers in our ancestors including changes from which we benefited.

Familiar ideas about natural selection need to be expanded to show how small changes can build up to create increasingly complex mechanisms involved in the processes that repeatedly produce:

  1. new physical and chemical structures and processes supporting reproduction, metabolism, growth, immune responses, neural mechanisms, etc.;
  2. new physical forms and new physical behaviours of organisms, including new types of sensing and acting;
  3. (Added 7 Aug 2014): New information-processing challenges, e.g. to deal with more complex physical phenomena, or more intelligent predators or prey, or to meet new demands on parents because of more sophisticated learning capabilities in offspring. (Challenges or requirements can evolve also, not only solutions. Challenges can come not only from new prey, new predators, new competitors, new physical environments, but also from new learning potential of offspring, or from new capabilities that are not easy to use.)
  4. new information-processing capabilities and mechanisms, including sensory interpretation, motivation, learning, planning, decision making, interrupting, self-monitoring, teaching, etc.;
  5. new evolutionary mechanisms, including new drivers of variation and new selection mechanisms.
Point (e) involves 'recursion': evolutionary morphogenesis changes mechanisms of evolutionary morphogenesis -- hence the label 'meta-morphogenesis'.

The project investigates how increasingly complex products of evolution produce increasingly complex forms of information processing including new mechanisms of evolution (generalising ideas in Turing's 1952 paper on chemical morphogenesis and also the theory of meta-configured individual cognitive development presented in

Jackie Chappell and Aaron Sloman,
Natural and artificial meta-configured altricial information-processing systems,
International Journal of Unconventional Computing, 3, 3, 2007, pp. 211--239,
http://www.cs.bham.ac.uk/research/projects/cogaff/07.html#717
which includes an earlier version of this diagram, showing different levels at which information from the genome and from the environment combine (after varying developmental delays):

meta-configured
(Chris Miall helped with the original diagram.)

That theory (and diagram) referred to processes of development in an individual -- processes that change some of the mechanisms of later development in that individual. The M-M project extends that idea to evolution, so that in this new context instead of the diagram referring only to development of individual organisms, it can also refer (loosely) to evolution of a species, or even of a whole ecosystem whose main features, including features affecting further evolution, change over time.

The M-M project has begun to identify many changes in forms of biological information processing, including transitions in mechanisms of reproduction, mechanisms of learning and development, and inter-individual and inter-species forms of information-processing. Examples of distinct types of transition in biological information-processing are being collected here.

An important under-studied transition is evolution of capabilities that led to proofs in Euclidean geometry long before modern mathematics, one of the most important extensions of human minds in the last few millennia. How did abilities to think philosophically evolve? Were the cognitive mechanisms unique to humans or did unnoticed subsets develop in other species? When will our robots begin to acquire these abilities?

The questions raised in the M-M project require long term multi-disciplinary collaborative research, perhaps comparable in scale to the Human Genome project. The relevance to philosophy of mathematics is discussed in a related web page. ____________________________________________________________________________

"Information" -- a key idea for this project
(And for Jane Austen.)

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 his is the only concept of "information" we have. But there is a much older one, used in everyday life.

The older concept refers to information that has causal roles in evolution, in animal perception, learning, motivation, acting, interacting, thinking, asking, wondering, being puzzled, finding answers (etc.) This ancient concept was often used explicitly by Jane Austen over a century before Shannon's work, and by 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 (contrasted with Claude Shannon's).
However, I am not claiming that Jane Austen had considered all the uses of information relevant to biology. 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, ...

In particular, "information-processing" here does not refer only to bit manipulation, or symbol manipulation, the operation of computers, or the sending and receiving of messages: those are all special sub-cases. In particular, the kind of information we are talking about does not need a sender and a receiver every time there is a user.

Acquiring information is finding out about something that the information refers to (or purports to refer to: it could be false information). Information contents used by an organism can come from any different sources outside or inside the organism, and can play different roles: in questions, intentions, instructions, multi-step branching plans, conditions for doing something, theories, and many more. All organisms, and many parts of organisms, including cells, use information -- and not just for reproduction. Working out a plan for achieving a goal uses information about the desired state of affairs to create a new complex information structure whose parts refer to possible actions, possible contents of perception, conditions for doing things, sources of missing information, and many more.

Biological information is of many kinds, with many types of complexity, using many kinds of mechanism, for many types of purpose or function. For more in the concept of "information" used here see Sloman (2010) [in a separate web page].

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Related Videos (Moved to another file 24 Aug 2014)

Long slide presentation introducing the Meta-Morphogenesis project ____________________________________________________________________________

Return to list of contents
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Introductory material (some repeated)
Including evolution and mathematics.

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?

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.

More on connections between natural selection and mathematical discovery:
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/bio-math-phil.html
    Biology, Mathematics, Philosophy, and Evolution of Information Processing

Additional mathematics-related material in this directory

http://www.cs.bham.ac.uk/research/projects/cogaff/misc/math-ai-robotics-bio-papers.html
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Return to list of contents
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  • Creativity in evolution/natural selection
    Although the processes start off "blind", the achievements are of a kind that would require highly creative processes of design, implementation, testing, development, debugging, and re-design, if produced by human engineers. In some of the later stages, when animal cognition begins to play a role in evolution, this is a form of conscious, but not yet self-conscious, creativity. Similar remarks can be made about varieties of creativity in development of individuals, discussed further in connection with "toddler theorems". (Compare Margaret Boden on creativity.)

    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://www.cs.bham.ac.uk/research/projects/cogaff/misc/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: For a messy, still growing, collection of examples relating to learning and development see this web page on "Toddler theorems": http://www.cs.bham.ac.uk/research/projects/cogaff/misc/toddler-theorems.html (including an introduction to the idea of a "Domain").
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    What is Meta-Morphogenesis? Draft answer (last revised: Aug 2013):

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


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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 PUBLICATIONS Most moved to separate document.

    1. Rodney Brooks on layered architectures and evolution In the mid 1980s, after apparently becoming dissatisfied with the state of AI, Brooks wrote a series of very influential papers that recommended a 'layered' approach to AI design, namely build systems that have relatively simple capabilities and then add new more sophisticated capabilities, that run in parallel with and make use of the older capabilities. He also related this to suggestions about biological evolution and the relative information-processing complexity of evolutionarily very old organisms, suggesting that what was added more recently to provide human functionality added relatively little. His ideas overlap with (and probably helped to influence) those presented in this project, but there are also deep differences. E.g. I don't claim that we can start building simple organisms with our current technology that provide suitable old, 'lower level' layers on which to add newer, more sophisticated, layers of competence. I am suggesting that what is old in the evolutionary history of existing organisms may have many unobvious features that the M-M project should attempt to uncover.

      I also don't propose that it will suffice to start from multi-cellular organisms like insects, that have already evolved capacities to move around in rich and complex environments, foraging, feeding, mating, building nests, etc. Instead I consider the possibility that even at the single-celled level there may have been forms of information processing that underpin some of the types of information processing that interest us in humans and other animals.

      Brooks' suggestion that the importance of internal representations has been over-rated because the best representation of the world is the world itself, has been highly influential, but is at most relevant to what I've called 'online intelligence' involved in control of movements and manipulations using feedback mechanisms of various sorts. (H.A.Simon made similar points.) For deliberative and meta-semantic competences the slogan is not merely wrong: it has been positively harmful.

      Also the ideas in the CogAff project and the CogAff architecture schema allow for a richer variety of types of architecture than the type of layered subsumption architecture proposed by Brooks, though it's possible that each could be modified to cover more of the features of the other.

      His work had enormous influence in many research and teaching centres. Unfortunately the people influenced were often much less intelligent and less subtle than Brooks, and as a result much of the influence has been bad. Hence my critique.

      David Kirsh wrote a critical review of Brooks' ideas around 1986, published in 1991 (here.). Brooks wrote a reply ('From earwigs to humans') published in Brooks (1997). I wrote a somewhat different critical commentary much later, partly based on the unpublished note on requirements, cited here.
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      Appendix: Schematic (partial) 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, including extensions of empirical learning to include non-empirical, e.g. mathematical learning (making use of new meta-cognitive capabilities).
      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 (M-M): 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.
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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://www.cs.bham.ac.uk/research/projects/cogaff/misc/m-m.html
      Most of the references have been moved to a separate file:
      http://www.cs.bham.ac.uk/research/projects/cogaff/misc/m-m-related.html

      A slightly messy PDF version is also available:
      http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.pdf

      This is one of a set of documents on the meta-morphogenesis project listed below.

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

      INSTALLED:
      This version installed: 21 Oct 2012
      Previous (longer) version installed: 19 Oct 2011 now here.
      UPDATED:
      17 Sep 2014: Added more structure to the introduction, with subheadings
      14 Sep 2014: New experimental top section. Is it too confusing? Does it sound like clap-trap to the uninitiated?
      8 Sep 2014: slight rearrangement. Some new references.
      24-5 Aug 2014: considerable reorganisation, with most references moved to here.
      7 Aug 2014: minor changes; 15 Aug 2014 added Birner's paper on Hayek and Popper;
      30 Jul 2014: added link to Strawson and meta-descriptive metaphysics moved to another file
      5 Apr 2014 (Doyle and Popper links); 17 May 2014; 12 Jun 2014
      31 Jan 2014: added new introduction and reorganised a bit; 10 Feb 2014: Minor eds;
      12 Nov 2013 (Added section on comparison with ideas of Rodney Brooks.) ;19 Nov 2013
      2 Aug 2013; 16 Aug 2013; 24 Aug 2013 (some re-formatting); 6 Sep 2013; 29 Sep 2013; 31 Oct 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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      Maintained by Aaron Sloman
      School of Computer Science
      The University of Birmingham

      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
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