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
20 December 2015 - 30 January 2016
Full Schedule for January talks:
General background information
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
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;
School of Computer Science, University of Birmingham
The presentation will be highly interactive.
[NASA artist's impression of a protoplanetary disk, from WikiMedia]
Chomsky, Piaget, Euclid's precursors(?), Euclid, Descartes, Gibson, Hume, Kant, Lakatos, Popper, Lovelace, Frege, Schrodinger, Turing, ... and many more.
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 https://www.youtube.com/watch?v=pjtioIFuNf8 . 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 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
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 @ cs.bham.ac.uk
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 interviewed by Adam Ford at AGI 2013, St Anne's College Oxford.
School of Computer Science
The University of Birmingham