THE TURING CENTENARY RESEARCH PROJECT:
Turing Centenary Research Fellowship and
- Rodney Brooks
- Sir Roger Penrose
How to apply (click here)||
- First call for proposals - April 2011
- Submission deadline - December 21, 2011 (extended)
- Award Notification - March 31, 2012 (expected April 2)
- Award Ceremony - Turing Centenary day, June 23, 2012
- Commencement of the research project - July 1, 2012
- Completion of the research project - June 30, 2015
More than any other figure, Turing has left a
coherent scientific agenda
related to many of the
concerning the relationship between the human mind, mechanism in
and the mathematics required to
clarify and answer these questions.
The very breadth and fundamental nature of Turing's impact makes
the centenary celebration a hugely opportune period in
which to reassert the role of basic thinking in relation to
deep and intractable problems facing science.
'The Turing Centenary Research Project - Mind, Mechanism and Mathematics',
supported by a major grant from
the John Templeton Foundation, arises
from the above-mentioned scientific agenda, and
is aimed at researchers still within ten years of receiving their Ph.D.
The participants in this 3-year research project will be the winners
'Mind, Mechanism and
designed to provide significant funding support
for eight young researchers. Five of the winners
will become JTF 'Turing Research
Fellows'; and three of the
awards will be for
JTF 'Turing Research Scholars'
in the 16 to 25 age-group, coming under the JTF 'Exceptional Cognitive Talent and Genius'
programme - see the JTF page on supporting
Cognitive Talent and Genius.
The competition is
organised in conjunction with the major
Centenary Conference, to be held June 22-25, 2012,
at the Manchester City Hall
and the University of Manchester. The award winners will be
duly honoured on the June 23, 2012 centenary of Turing's birth. This
meeting, also funded by the JTF, is organised by
Professors Andrei Voronkov and Matthias Baaz.
The Big Questions
work and scientific legacy inhabits a world at the interface between
the physical universe and the abstractions of mathematics;
between the computational, the predictable, and the uncertainties
manifest in human creativity, and in the emergence of form from
randomness and chaotic environments; and a world at the
interface between science, reason, reductionism and the mysteries
of intuition, incomputability, and the failures of foundational thinking.
There are many deep and intractable questions arise in such a
context. We list below four main
around which the research project will be built:
- The Mathematics of Emergence:
The Mysteries of Morphogenesis
Does emergence exist as something new in nature,
and is it something which transcends classical models of computation?
(Develop a sufficiently fundamental model of emergence to
answer this question.)
To what extent do existing models of natural processes accommodate
(Characterize the mathematical characteristics of such models,
and the extent of their representational and recursive capacities.)
One of the less familiar but most innovative of Turing's
contributions was his successful capturing of emergence of various
patterns in nature in mathematical equations. This key innovation
now forms the basis of an active and important research field
There are still general questions concerning both the computational
content of emergence, of the unifying features extractable from
different contexts, and the nature of good mathematical models
applicable in different environments. John Holland, in his popular
1998 book on Emergence: From Chaos to Order
sets out the extent of our ignorance of the precise mathematical
character of emergence:
"It is unlikely that a topic as complicated as emergence will submit
meekly to a concise definition, and I have no such definition to offer."
Issues of self-organization, emergence of form from chaotic or
turbulent environments, non-locality in physics, and the
relevance of classical models of computability such as Turing
machines, cellular automata, neural nets, among others, are all
in need of further investigation. It is hoped that a more coherent
and mathematically-based viewpoint will shed light on some of the
issues arising under the following three headings.
- Possibility of Building a Brain:
Intelligent Machines, Practice and Theory
Is there a successful mathematical model of intelligent thought?
(Develop more basic models, capable of pinning down the exact
relationship between the human brain and standard paradigms
from classical computability and computer science.)
Is intelligent thought essentially experimental and inexact?
Are mistakes an essential aspect of intelligence?
What is the extent of application of the notion of a 'virtual
machine'? Is human thinking essentially embodied, and the
Turing paradigm of a universal machine inapplicable in this context?
Turing's seminal role in artificial intelligence - for instance,
his formulation of the Turing
Test and his interest in connectionist models - has given rise to
many different and contrasting approaches to building intelligent
machines. There is a basic dichotomy between experimental, ad hoc
approaches and more logic-based theoretical ones. There is a
general agreement that we need to understand more about the
relationship between these, and to obtain a better theoretical
grasp of the practicalities, and of the underlying theoretical
obstacles. The extent of the challenges facing researchers is
summed up by Rodney Brooks, in Nature in 2001:
"neither AI nor Alife has produced artifacts that could be
confused with a living organism for more than an instant."
The question of embodiment of intelligent computation is a key one,
and is related to the problems surrounding the character of
mental causation. As the Brown University philosopher Jaegwon Kim
puts it, in relation to the brain, in his book Physicalism,
or Something Near Enough (2005):
"... the problem of mental causation is solvable only if
mentality is physically reducible; however, phenomenal consciousness
resists physical reduction, putting its causal efficacy in peril."
Turing himself refers to the role of mistakes in human intelligence
(a feature without a positive role in current machine-hosted
"if a machine is expected to be infallible, it cannot also be
intelligent. There are several theorems which say almost exactly that"
(talk to the London Mathematical Society, February 20,
1947, quoted by Andrew Hodges in Alan Turing - the enigma,
One will be looking for new thinking in this area, or more insightful
unifying of existing knowledge. This is an area in which it is easy
to produce speculation detached from clearly formulated models, and
there will be an emphasis on rigor, clarity and mathematical content.
- Nature of Information:
Complexity, Randomness, Hiddenness of Information
To what extent is complexity practically computable?
(Elucidate the distinctions and identities pertaining to the
numerous computational complexity classes.)
How much mathematical randomness occurs in nature?
(Characterize quantum 'randomness',
and answer questions in the mathematical theory relating to
information theory, cryptology and physics.)
This is an area more concerned with practical issues of computation,
and the extraction of information from less favorable contexts.
The focus is more on what is possible in the way of information
processing. Cryptology, where this topic has its origins, has
advanced hugely since the time of Turing and his Bletchley Park
contemporaries, and there is a rich and developing mathematical
theory. There are basic issues of complexity,
also going back to the time of Gödel
and Turing, where progress depends on solutions to fundamental
own interest in computational efficiency, and by implication
complexity of programs, goes back to his work with the
at Bletchley Park in the 1940s. In her article on
Computing over the Reals: Where Turing Meets Newton
(Notices of the AMS, 2004), Lenore Blum traces Turing's
early contribution to the study of complexity in the context of
numerical analysis in Rounding-off Errors in Matrix Processes
in The Quarterly Journal of Mechanics and Applied Mathematics,
vol. I, 1948, pp. 287-308, where she quotes Turing:
"It is convenient to have a measure of the amount of work involved in a
computing process, even though it be a very crude one ...
We might, for instance, count the number of additions, subtractions,
multiplications, divisions, recordings of numbers, ... "
There are connections to the fast-developing body of work
concerned with randomness and hidden information, and to
questions of the extent to which randomness occurs in
nature, particularly at the quantum level.
- How should we compute?
New Models of Logic and Computation
Working at the interface between science and mathematics,
how do classical models from logic and computability theory
model real-world processes?
(Develop the mathematical theories of relative computability,
and solve long-standing open problems relevant to
higher-order features of the real universe.)
What are the inadequacies of classical models, and what are
the new computational models which capture more of the essentials
of natural and physical computation?
(Develop new computational models, and identify the extent of
their real-world application.)
This theme would concern the extraction of mathematical models
of computation from nature, and the investigation of their
theoretical properties, a key concern of Turing himself.
Even for those who adhere to classical models, there are still
questions relating to modeling and underlying theory.
Here is David Deutsch quoted in the New Scientist
"I am sure we will have [conscious computers], I expect they will
be purely classical, and I expect that it will be a long time
in the future. Significant advances in our philosophical
understanding of what consciousness is, will be needed."
So topics can range from classical Turing analyses of the incomputable,
to new computational paradigms in bio-informatics, to quantum or
relativistic models from physics. Contributions relating to a
wide spectrum of different computational models, both new and
established ones, would be welcome. However, both modeling and
theory should be motivated by the drive to say something new and
fundamental about the nature of computation in the real universe.
The real contexts within which the models originate can also be
very diverse, but a unifying viewpoint will be encouraged.