Notes for talk on
Evolved construction-kits for building minds
(Evolution's deep learning)

Date: Thursday 21 Jan 2016 Morning lecture followed by discussion

A tutorial introduction to the
Turing-Inspired Meta-Morphogenesis Project

What might Turing have done if he had lived several decades after publication of "The Chemical Basis of Morphogenesis" in 1952?

Would Ada have approved?

This is a contribution to The Ada Lovelace Bicentenary Lectures on Computability
20 December 2015 - 30 January 2016
Full Schedule for January talks:

General background information

(DRAFT: Liable to change)

Evolved construction-kits for building minds
(Evolution's deep learning.)


This will be a highly interactive tutorial introduction to the Turing-inspired Meta-Morphogenesis Project, which brings together a host of problems and ideas about evolution of information processing, how it started on a lifeless planet, how natural selection produced branching layers of construction kits (some physical, some abstract, and some hybrid), and how these made possible increasingly complex and varied morphologies and behaviours based on increasingly complex and varied forms of information processing. Among many topics to be discussed are the unknown evolutionary precursors to human abilities to make mathematical discoveries leading up to Euclid's Elements, and related aspects of human and animal visual abilities. Support for Kant's philosophy of mathematics will be presented, along with criticisms of the visual, mathematical, and linguistic competences of current AI systems. Some possible ways of overcoming those limitations will be considered, with implications for current theories of how brains function.

An extended abstract will be added to this file (below).
More information on the Meta-Morphogenesis project is available here:

Some examples of proto-mathematical perceptual capabilities that seem to use mechanisms that are precursors to the discoveries in Euclid's Elements are presented and discussed in
Some (Possibly) New Considerations Regarding Impossible Objects
Their significance for mathematical cognition,
and current serious limitations of AI vision systems.

A background paper on evolution of construction kits

This file is

For video recordings of the two presentations (morning and afternoon) see

Last updated: 18 Dec 2015; 19 Dec 2015; 24 Dec 2015; 30 Dec 2015; 12 Jan 2016; 28 Jul 2016;

Interactive Introduction to
The Turing-Inspired Meta-Morphogenesis Project

Aaron Sloman
School of Computer Science, University of Birmingham

The Meta-Morphogenesis (M-M) project asks:
How can a cloud of dust give birth to a planet
full of living things as diverse as life on Earth?

The presentation will be highly interactive.

A Protoplanetary Dust Cloud?
Protoplanetary disk

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

Keywords (A provisional list)
Architectures, Artificial Intelligence, Computation, Consciousness, Construction kits, Construction kits: Concrete, Abstract, Hybrid, Construction kits: Fundament and Derived, Construction kits: tools and scaffolding, Epigenesis, Evolution of language, Evolution's use of mathematical "discoveries", Evolution, Functions of vision, Geometry, Information in Biology, Metaphysics, Natural intelligence, Philosophy of mathematics, Philosophy of mind, Philosophy of science, Possibility and necessity, Robotics, Topology, Virtual machinery,

Chomsky, Piaget, Euclid's precursors(?), Euclid, Descartes, Gibson, Hume, Kant, Lakatos, Popper, Lovelace, Frege, Schrodinger, Turing, ... and many more.

Stuart Wray's sketch of meta-morphogenesis

On 5th Jun 2012, Stuart Wray, after reading a draft conference paper on Meta-morphogenesis and the Creativity of Evolution (before the ideas about construction kits had been added) produced a sketch of the ideas in the project, reproduced here, with his permission:



How much is known about evolution of information-processing?
What are the evolved information-processing capabilities that made it possible for humans to make mathematical discoveries, reflect on them, communicate about them, argue about them, and organise them into a system: Euclid's Elements?

Which features of those human competences had evolved earlier, making possible various forms of intelligence in non-human animals?

Despite all the successes of AI there remain deep gaps between what AI systems can do and the competences of humans and other animals, for example nest building birds, such as weaver birds, and squirrels that defeat "squirrel-proof" bird-feeders. Current AI language learning systems are nothing like the young deaf children who created a new sign language, as reported in . Humans don't merely learn languages: they create languages, collaboratively. But for that, there could be no human languages since initially there were none to learn.

There are many human competences that current AI systems are not even close to replicating, for example the processes that led to the mathematical (geometrical, topological, arithmetical) discoveries known to Euclid over two thousand years ago (long before modern logical notations and theories had been thought of), and the processes by which a young human who is unable to understand any such mathematical content can develop into a mathematical student who not only understands but who can also discover theorems and proofs without being told about them. Profoundly important discoveries in geometry, topology and arithmetic leading up to Euclid's Elements must have started before there were any mathematics teachers. How? How did the first engineers manage without teachers?

Modern AI theorem provers based on developments in logic since Boole, Peano, Cantor, Frege, Russell, etc. can certainly outperform most humans but only in finding theorems and proofs naturally expressible using logic. Deriving a theorem in a logical axiomatisation of geometry is completely different from making the original discoveries, including finding a way to extend Euclidean geometry so that arbitrary angles can be trisected -- impossible in standard Euclid, but already known to Archimedes and others:

I am not saying that replicating these biological achievements is beyond the scope of AI. The problem is to identify the biological competences (e.g. visual competences, mathematical competences, bootstrapping competences) in more detail, to help us work out what's missing in AI/Robotics, so that we can attempt to bridge the gaps. I don't think the gaps are easy to describe correctly.

For example, what would convince you that a robot sees these garden movies (or the original scenes) in something like the same way as you do:

Perhaps our understanding of possible forms of computation has serious gaps?

A conjecture about how to make progress

One way to try to bridge those gaps in our understanding is through the Turing-inspired Meta-Morphogenesis project, which aims to identify and understand the many transitions in information processing produced by biological evolution since the very simplest organisms or pre-biotic molecules came into existence on a lifeless planet.

A key hypothesis is that a major theme throughout biological evolution is production of new derived construction-kits (DCKs) all ultimately derived from the fundamental construction kit (FCK) provided by physics and chemistry.

In addition to production of new physical materials, new physical designs, and new physical behaviours, derived construction kits also provide ever more complex and varied forms of information processing.

An outline theory will be presented: concrete, abstract and hybrid (concrete+abstract) construction kits produced by evolution and development can help to explain the variety of types of information processing in living things, and help to draw attention to forms of information processing (computation) that have not yet been studied or replicated but which may play important roles in animal intelligence. Some preliminary ideas about the main features of the Fundamental Construction Kit provided by (Quantum) Physics and Chemistry and the Derived/Evolved construction kits are assembled in this draft book chapter:

The presentation will include an introduction to the ideas in this project and some preliminary results. One of the topics will be the inadequacy of current theories and models of visual perception, which cannot explain the role of vision in mathematical discoveries (especially topological and geometric discoveries) leading up to the monumental work by Euclid around 2500 years ago. Those discoveries seem to require abilities to use visual perception (and other forms of perception) to acquire information about possibilities for change in the environment and constraints on those possibilities (impossibilities). This generalises J.J. Gibson's theory of vision as primarily concerned with information about affordances, not information about structures in the environment, of sorts acquired by most current AI vision systems Gibson (1979).

Development and extension of Gibson's ideas seems to require forms of biological information processing and types of information-processing architectures (with layers of meta-cognition) that have so far not been developed in AI or Robotics. Perception of possibilities and impossibilities is totally different from acquisition of information about probabilities: the focus of much current research. Moreover, perception of possibilities and impossibilities is intimately connected with abilities to make the sorts of discoveries in geometry and topology that allowed proofs to be constructed and communicated long before modern logic-based ideas about proof had been developed. I suspect that those ancient mathematical discovery mechanisms are still part of the learning about spatial structures and processes that goes on in pre-verbal children (including many unrecorded discoveries of "toddler-theorems") concerning things that are possible and impossible in a child's environment:

Some examples are presented in these draft notes on perception of possibilities and impossibilities, extending Gibson's ideas about perception of affordances:

One of the many possible topics for discussion is the evolution of languages for expressing and manipulating structured information. Anyone interested will find a presentation on some challenges to to popular theories of the nature and evolution of vision and language here:

It is hoped that some of those attending will develop an interest in this large and complex (but currently unfunded) project and help to speed up progress.

The presentation will be highly interactive, with opportunities for participants to contribute ideas as well as questions.

Anyone interested in this project, whether planning to attend the event or not, is welcome to contribute comments and links to available online documents or presentations. Please send them to:
     a.sloman @

This project was partly inspired by Alan Turing's 1952 paper on
The Chemical Basis Of Morphogenesis,
Phil. Trans. Royal Soc. London B 237, 237, pp. 37--72, 1952,

This work is, in part, a sequel to my 1978 book, now available in a slightly revised free re-packaged electronic edition, with a link to a draft "Afterthoughts" paper:
Aaron Sloman The Computer Revolution in Philosophy, Philosophy, Science and Models of Mind, Harvester Press and Humanities Press, 1978
(The original edition is out of print).


(provisional list: to be updated)
   Aaron Sloman interviewed by Adam Ford at AGI 2013, St Anne's College Oxford.

Talk 111: Two Related Themes (intertwined)
What are the functions of vision? How did human language evolve?
(Languages are needed for internal information processing, including visual processing)

J. J. Gibson, 1966, The Senses Considered as Perceptual Systems, Houghton Mifflin, Boston,

J. J. Gibson, 1979 The Ecological Approach to Visual Perception, Houghton Mifflin, Boston, MA,
Some of the ideas in this project are closely related to the ideas on development and "representational re-description" presented by Annette Karmiloff-Smith, e.g. in Beyond Modularity: A Developmental Perspective on Cognitive Science, MIT Press, 1992,
What are the functions of vision? How did human language evolve?
(Languages are needed for internal information processing, including visual processing)

A partial index of related discussion notes is in

Maintained by Aaron Sloman
School of Computer Science
The University of Birmingham