The changes comprise both what is done (as indicated in the previous
paragraph) and how it 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 that can occur, 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.
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. And, eventually, at least on one planet,
it has produced some individuals that have begun to understand some of
what the evolutionary mechanisms can produce, without understanding.
Interestingly, 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.[*]
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, 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, tides, 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. using trees and
networks whose nodes can be structured objects including trees and networks.
Without this, 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. Extending it, 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 MM project.
That will require 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 to reason 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. (Mostly not yet inserted.)
Offers of collaboration welcome. I have no funds for this research, and do not intend
to apply for funds. Others may do so.
School of Computer Science, University of Birmingham.
[*]NOTE (Added 17 May 2014)
Some of those changes seem to bear a high level resemblance to the processes in
individual development in animals described as "Representational Redescription" in
Moreover, 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 McCarthy (1996/2008):
"Evolution solved a different problem than that of starting a baby with no a____________________________________________________________________________
"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."
The idea of a Meta-Morphogenesis project arose from a misunderstanding I had with
the editors of the award-winning book "Alan Turing: His Work and Impact"
Detailed list of contents and contributors.
2013 PROSE Award announcements
The proposal for a Meta-Morphogenesis project, partly inspired by Turing's 1952 paper
on Chemical Morphogenesis, was first presented as a chapter in that book:
Aaron 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#1106dThe ideas are elaborated below, and in other web pages referred to below.
This version installed: 21 Oct 2012
Previous (longer) version installed: 19 Oct 2011 now here.
5 Jun 2014 (expanded introduction -- above);
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;
"In the nervous system chemical phenomena are at least as important as electrical."I wonder if he thought about the significance of chemistry for evolution of mind in a physical universe.
in 'Computing machinery and intelligence', Mind, 59, 1950, pp. 433--460
A Protoplanetary Dust Cloud?
[NASA artist's impression of a protoplanetary disk, from WikiMedia]
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
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:
faq.php http://evolution.berkeley.edu/evolibrary/misconceptions faq.php
However 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
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#717which includes this diagram, showing different levels at which information from
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
Additional overview materials
A 57 minute video interview at AGI 2012 in Oxford introduces some of the ideas.
With a transcript here (thanks to Dylan Holmes):
One 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:
A draft speculative paper on the nature of mathematics and
evolution of mathematicians (Sept 2013):
And an extended abstract for a seminar on this topic on 1st Nov 2013:
"From Molecules to Mathematicians"
A partial index of a wider collection of discussion notes is in
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
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
At: AGI 2012 -- Dec 11th Oxford
St Anne's College Oxford
A growing collection of related papers and discussion notes:
The universe is made up of matter, energy and information,
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?).
The "proofs" of discovered possibilities are implicit in evolutionary and/or developmental
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 mixThese processes produce new forms of representation, new ontologies and information
A growing list of transitions in types of biological information-processing:
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':
An attempt to identify a major type of mathematical reasoning with precursors in
perception and reasoning about affordances, not yet replicated in AI systems:
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
What changes occurred to meet that need?
More examples to be collected here:
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:
For a (very) compressed history of information processing on our
Evolution, Life and Mind: Some Startling Facts
For a messy, still growing, collection of examples relating to learning and development
see this web page on "Toddler theorems":
(including an introduction to the idea of a "Domain").
Related talks (PDF) can be found here: http://tinyurl.com/BhamCog/talks/
Meta-Morphogenesis: of virtual machinery with "physically indefinable" functions
(Slides for presentation given at the Workshop "The Incomputable" (superseded)
Royal Society Kavli Centre: 11-15 June 2012)
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
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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PAPERS WITH FURTHER DETAILS
A collection of examples extracted from the papers and presentations, along with
some new examples based on things I have read and conversations with friends and
colleagues. Some of the examples illustrate portions of the process of
information re-organisation (perhaps instances of what Karmiloff-Smith means by
The list of examples is a tiny sample. I shall go on extending it.
PAPERS ON META-MORPHOGENESIS
RELEVANT PRESENTATIONS (PDF)
CLOSELY RELATED (To be expanded and re-ordered)
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.
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.
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
(ref below). Brooks wrote a reply, cited below, published in 1997. I wrote a somewhat
different critical commentary much later, partly based on the unpublished note on
requirements, cited below.
David Kirsh, Today the earwig, tomorrow man?, in
Artificial Intelligence, 47, 1, 1991, pp. 161--184,
Rodney A. Brooks, From earwigs to humans, in
Robotics and Autonomous Systems, 20, 1997, pp. 291 - 304
Some Requirements for Human-like Robots:
Why the recent over-emphasis on embodiment has held up progress, in
Creating Brain-like Intelligence,
Eds. B. Sendhoff, E. Koerner, O. Sporns, H. Ritter and K. Doya, Springer-Verlag, 2009, pp. 248--277,
Aaron Sloman, 2011,
What's information, for an organism or intelligent machine?
How can a machine or organism mean?,
In, Information and Computation, Eds. G. Dodig-Crnkovic and M. Burgin,
World Scientific, New Jersey, pp.393--438,
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.
Kauffman's 1995 book is very approachable:
At home in the universe: The search for laws of complexity
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 1987Like Kant, he had deep observations but lacked an understanding of information
"... 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."
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.
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
In particular, much of what Merlin Donald has written about evolution of consciousness
is relevant to this project, though it is not clear that he appreciates the
importance of virtual machinery, as outlined in
How Virtual Machinery Can Bridge the ``Explanatory Gap'', In Natural and Artificial Systems,
Invited talk at SAB 2010, Paris, in
Proc. SAB 2010, LNAI 6226, Eds. S. Doncieux and et al., Springer, 2010, pp. 13--24,
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".
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.
e.g. organisms that can discover what they have learnt.In particular, most forms of biological information processing that exist now are
organisms that make and use mathematical discoveries.
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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School of Computer Science
The University of Birmingham