Notes for Tutorial Presentation at The AAAI 2011 Conference
(Likely to be revised and expanded.)
This file is
From time to time a (slightly messy) PDF version will be generated
(thanks to 'html2ps' and 'ps2pdf'), available here, suitable for printing:
The full list of tutorials is
The main conference web site is http://www.aaai.org/Conferences/AAAI/2011/
Philosophy as AI and AI as Philosophy
Those who are ignorant of philosophy are doomed to reinvent it -- badly.
Those who are also ignorant of computation will make an even worse mess of
Tutorial MP 4:
Monday, August 8, 2011, 2:00pm-6:00pm
(There will be a refreshment/discussion break for about 30 minutes,
probably starting around 4pm.)
Presenter Aaron Sloman
Honorary Professor of Artificial Intelligence and Cognitive Science
(But mainly a philosopher: See bio below.)
School of Computer Science, University of Birmingham.
NOTE ADDED AFTER THE TUTORIAL:
I was very surprised that nothing was said about AI and Philosophy by any of the
speakers at the "anniversary panel" at the beginning of the main conference.
Despite this, attendance at the Tutorial was quite good, and people even came
back after the refreshment break!
NOTE: because the presentation was fairly interactive (essential for philosophy)
it did not follow the set of slides prepared in advance. But most of the
ideas presented are included in the slides.
The very relevant Meta-Morphogenesis project was not mentioned in this tutorial
because the idea came a few months later. See
Last updated: 29 Apr 2011; 2 May 2011; 8 Jul 2011; 31 Jul 2011; 3
Aug 2011; 10 Sep 2014
Installed: 5 Feb 2011
Downloadable papers and presentations related to this tutorial (All
Prerequisites For Attendance:
There are no prerequisites, except interest in how AI and philosophy are
mutually relevant, and can provide insights into the nature of mind and
A book that provides a lot of illustrative empirical data that is relevant to
philosophical and engineering nature/nurture issues (e.g. for robot designers)
Beyond Modularity: A Developmental Perspective on Cognitive Science,
MIT Press, Cambridge, MA, 1992,
(I am writing a growing set of notes on that book here.)
Request to those thinking of attending
If you are planning to attend it will help me with planning if you send me the
to A.Sloman@cs.bham.ac.uk, with Subject [AAAI Philosophy Tutorial]
First name (for familiar address):
Status (independent, student, researcher, academic, retired, industrial
Have you studied philosophy formally? To what level?
Have you studied AI formally? To what level?
Institution (if appropriate):
Email address (will not be used except for this tutorial):
Have you already studied philosophy, and if so what, and how (e.g. university
course, private reading)?
Are there topics you would particularly like to have discussed in the tutorial?
(I make no promises!)
Any other information you think relevant.
More helpers may be required for the philosophical section of
If required would you be willing to help?
Although most AI research has engineering objectives, some
researchers are primarily interested in the scientific study of
minds, both natural and artificial. Some of the deep connections
between both scientific and applied AI are linked to old problems in
philosophy about the nature of mind and knowledge, what exists, how
minds are related to matter, about causation and free will, about
the nature of consciousness, about how language is possible, about
creativity, and about whether non-biological machines can have
minds. Such questions linking AI and philosophy motivated AI
pioneers such as Ada Lovelace, Alan Turing, Marvin Minsky, John
McCarthy and Herbert Simon, and are also addressed in the writings
of Margaret Boden, Andy Clark, David Chalmers, Daniel Dennett, John
Searle, and others. Yet many questions remain unanswered and some
philosophers and scientists think AI can contribute nothing except
solutions to engineering problems.
The tutorial is an attempt to explain how some largely unnoticed
relationships between AI, philosophy, biological evolution and
individual development, along with some advances in computer systems
engineering, provide the basis for major advances in several
disciplines, including AI and Philosophy.
It will also attempt to show how some philosophical confusions, e.g.
about "symbol grounding", about relations between embodiment and
cognition, and about how theories can be evaluated, can hold up
The presentation will be highly interactive and I hope provocative!
Those who are ignorant of philosophy are doomed to reinvent it -- badly.
(Apologies to Santayana.)
Welcome and brief survey of background and interests of participants
(To help select topics for discussion.).
Overview of main areas of philosophy and brief comments on relations to
AI, including how AI can aid conceptual analysis.
Philosophy of mind (including the nature of qualia and
the multifarious forms of consciousness)
The standpoint of this tutorial is that these are all to be understood
as forms of biological information processing.
Philosophy of science
Including the question -- do standard views of philosophy of science do
justice to AI as science?
(See Chapter 2 of Sloman 1978).
Philosophy of causation
Can standard philosophical theories of causation do justice to the
causal interactions within virtual machinery in complex information
Philosophy of mathematics -- links with requirements for future robots
and products of biological evolution.
Philosophy of information.
Consequences of the view of the universe as made up of matter, energy,
information, and processes involving them, in space-time.
What does information add?
Selection of a few aspects of AI/Computing and relationships to old
Links with biology and neuroscience
How to think about evolution of information processing: What were the
The roles of virtual machinery, and implications for mind/brain
relations and for philosophy of causation.
Relations with major transitions during individual development
and relationships with philosophy of mathematics.
Implications for nature of human languages used
internally (for perceiving, having desires, planning, control of
actions etc.) (Philosophy of language.)
Implications for nature of language learning: collaborative design
rather than data-mining in a corpus.
Confusions about embodiment.
Confusions about symbol grounding (concept empiricism re-invented)
How can a machine or animal acquire, create, derive and use semantic
Why do so many people (and not just John Searle) regard it as
obvious that computers cannot understand the symbols they
What enables a machine to understand?,
Proc 9th IJCAI,
Los Angeles, pp. 995--1001, 1985,
Varieties of functionalism
Atomic state functionalism (ASF) vs virtual machine functionalism (VMF)
Reward-based vs Architecture-based motivation.
Confusions about emotions.
Confusions about free will
Where to go next in AI and long term philosophical implications.
Request for help with AITopics web site.
A KEY IDEA
Many philosophers, especially so-called "analytical philosophers"
have been taught that philosophy is a special non-empirical
discipline, investigating eternal conceptual truths using methods
that do are distinct from and cannot be affected by results of
methods in other disciplines -- especially the sciences.
However those other disciplines can be the object of philosophical
investigation, e.g. philosophy of history, philosophy of art,
philosophy of physics, philosophy of biology, philosophy of
It has often been said that philosophy done in ignorance of the
other disciplines, especially the sciences, ends up being arid and
disconnected from the original problems that sparked philosophical
investigations. That's why many philosophy books and journals
include rich and detailed discussions of quantum mechanics, biology,
neuroscience, linguistics, et.
What has not been widely appreciated by philosophers is that the
science and technology of information can offer something radically
different providing entirely new ways of addressing some very old
problems, e.g. about the nature of consciousness, free will, the
mind body problem, the nature of emotions, the nature of language,
and metaphysical questions about what sorts of things can exist, and
what sorts of causes are possible.
Many think, mistakenly, if they have learnt about Turing machines,
incompleteness results, and perhaps written a simple arithmetical
program, they know all there is to know about computing. But they
don't know how different the computers we all use now are from what
they have learnt about.
The situation is changing but very slowly -- in part because very
few people understand what we have been learning in the last 60
years or so as a result of the science and technology of computing,
even though they use computers every day.
Here are some examples of topics that may be discussed.
The precise choice of topics will depend on who turns up for the
tutorial, their backgrounds and interests.
Up to 2nd Aug 35 persons had registered for this tutorial.
What exactly is special about computers, and computation, that makes
a computational approach to understanding mind not just the latest
a series of fashions for thinking about minds and brains in terms of
When I was a child it was fashionable to say, and write, and think
that the brain was a sort of telephone exchange. Telephones were
still relatively new then, and they were all connected by wires.
The philosophical significance of virtual machines composed of
interacting coexisting virtual machines, some of them connected to input and/or
Could biological evolution have "discovered" the need for virtual, as opposed
to physical, machinery long before engineers did?
Could self-monitoring virtual machines, including perceptual sub-systems linked
to sensory devices, provide the key to the nature of perceptual qualia,
including explaining all their philosophically puzzling features? (E.g. their
Title: Virtual Machines in Philosophy, Engineering & Biology
(And other items referred to there, including talks on virtual machinery.)
What implications do causes and effects within complex virtual
machinery have for philosophical theories of causation? Or for metaphysics more
A paper on this topic, written for a philosophy of science conference
held in July 2011 is online here:
Evolution of mind as a feat of computer systems engineering: Lessons
from decades of development of self-monitoring
Many animals (e.g. corvids, elephants, primates, squirrels) seem
to be able to work out solutions to new problems, instead of having to
use trial and error, or imitation, or explicit instruction, or genetically
programmed solutions. What mechanisms enable them to do solve such problems,
and how are they related to the abilities of humans to do mathematics?
Can designing robots with similar capabilities be a contribution to philosophy
of mathematics? E.g.:
can a computational theory of development of mathematical competences in young
humans, or young robots, shed light on philosophical questions about the nature
of mathematical knowledge?
Is research in philosophy of mathematics relevant to the task of
designing machines capable of doing or learning to do mathematics?
Under what conditions could a young robot begin to make mathematical
discoveries, e.g. about geometry, topology, sets, orderings, numbers, ...
Symbol-grounding theory is often taken as axiomatic by researchers in AI and
robotics. Yet it is just a new version of an old philosophical theory "concept
empiricism", first refuted by Immanuel Kant (around 1781) and more thoroughly
demolished by philosophers of science in the 10th Century. What alternative is
there? Can "theory tethering" provide the answer?
Many philosophers, psychologists, neuroscientists, biologists, and
AI/Robotics researchers think that causal learning processes are
captured by various forms of associative/statistical learning, e.g.
using Bayesian nets. This is essentially a Humean view of causation:
causation is just reliable association. Our feeling that we
understand something more, e.g. some kind of necessitation, is just
illusory, and ruled out by concept empiricism for we cannot
experience necessitation, only correlations, regularities,
statistical relations, etc.
Immanuel Kant (e.g. Critique of Pure Reason, 1781) argued that Hume
must be wrong and that since concept empiricism is wrong (since
concepts are needed for experience and therefore cannot all come
from experience) we can have a non-empirical concept
of causal necessitation. Several of his examples can be shown to be
similar to mathematical relationships - changing the height of a
triangle causes the area to change, and adding three coins to a jar
with five coins causes the number of coins in the jar to become
It is often assumed that all motivation must be based on
(positive or negative) rewards. I'll argue that that's a false assumption and
there are good reasons why evolution should have produced mechanisms for
"architecture-based motivation", which have consequences that the individual
concerned cannot possibly anticipate.
If this is correct, what are the implications for robotics? For philosophical
theories of motivation and affect?
Under what conditions could a young robot discover for itself some old
philosophical problems, e.g. about the nature of qualia, about relations
between mind and matter, about what knowledge is, about whether free will is
possible, about what words like "good", "right" and "ought" mean, and whether
there are objective moral values?
Could the same initial philosophical potential in young robots lead to
different "adult" philosophical theories in different robots with the
same initial design? E.g. could some end up thinking like John Searle,
others like Daniel Dennett, others like David Hume, others like Immanuel
In the past decade or two, there has been great enthusiasm among philosophers,
cognitive scientists and roboticists for the claim that cognition must be
embodied, and that acknowledging the role of embodiment revolutionizes theories
about mind and intelligence.
Is that claim correct, or does the emphasis on
embodiment ignore some important features of biological evolution and important
requirements for future robots?
Some Requirements for Human-like Robots:
Why the recent over-emphasis on embodiment has held up progress.
(Might include discussion of claims for "mirror neurons".)
What implications does AI have for debates about free will? What implications
do philosophical discussions of free will have for AI?
How do affective states and processes (e.g. desires, attitudes,
preferences, values, ideals, emotions, moods, interests, etc.)
differ from things like perception, belief, reasoning, planning, explaining?
How can thinking about architectures for minds help us answer this
question? Do information-processing theories provide a better alternative than
older philosophical answers, e.g. dualist theories, logical behaviourism, the
Could consciousness have been produced by biological evolution? If not, why
If so how?
If evolution can produce conscious animals does that have implications for
whether human engineers can produce conscious machines?
Similar questions can be asked about having desires, preferences, ideals, moral
values, etc. Can non-human animals have them, and if not why not, and if so
how? Does this help us understand whether robots could?
Are Asimov's "Laws of robotics" unethical towards intelligent machines?
See also the abstract for invited talk at AGI 2011
the week before AAAI 2011:
The biological bases of mathematical competences: a challenge for AGI
(Artificial General Intelligence)
There's more here:
Reading matter relevant to the tutorial. (To be extended)
(Please email me suggestions for additional items, or comments on
Introductions to AI
Bradford Book (MIT Press) 1995.
Mind Children: The Future of Robot and Human Intelligence,
Harvard University Press (Cambridge, Mass; London, England),
An elderly but still useful introduction to key ideas in AI for
Artificial Intelligence and Natural Man,
Margaret A. Boden, 1978, revised 1987.
Mike Sharples, et al. Computers and Thought, MIT Press, 1989.
(Available online at the
A short history of AI, by Pamela McCorduck
Machines Who Think: 25th anniversary edition,
Natick, MA: A K Peters, Ltd., 2004
Stanford AI Class taught by Sebastian Thrun and Peter Norvig offered online Free
Online enrollment ends Sept 10th, sign up early! The class
runs from Sept 26 through Dec 16, 2011.
Artificial Intelligence: A Modern Approach by Stuart Russell
and Peter Norvig.
Full table of contents:
As a supplement to the section on vision see my introduction to and
criticism of J.J. Gibson's ideas about perception:
What's vision for, and how does it work?
From Marr (and earlier) to Gibson and Beyond
(2nd edn), Elaine Rich & Kevin Knight McGraw
Artificial Intelligence: A new Synthesis
Nils J. Nilsson, Morgan Kaufmann, 1998,
Artificial Intelligence (3rd ed).
Patrick Henry Winston, Addison Wesley, 1992.
Artificial Intelligence, Structures and Strategies for Complex Problem Solving,
George F. Luger, William A, Stubblefield, Benjamin Cummings, 1993.
A no-longer-maintained but still useful list:
AI and Philosophy
Web pages of two AI pioneers who have both been very interested in
links between AI and philosophy, both with important papers online:
Daniel C. Dennett,
Kinds of minds: towards an understanding of consciousness,
Weidenfeld and Nicholson, London, 1996,
(And other books by Dennett.)
Daniel C. Dennett,
The Practical Requirements for Making a Conscious Robot
An optimistic account of the MIT "COG" project.
Margaret A. Boden,
The Creative Mind: Myths and Mechanisms,
Weidenfeld & Nicolson, 1990,
Margaret A. Boden (Ed)
The Philosophy of Artificial Intelligence,
"Oxford Readings in Philosophy" Series, Oxford University Press,
The Computer Revolution in Philosophy: Philosophy, Science and Models of Mind
Aaron Sloman, Harvester Press and Humanities press, 1978.
The Mythical Turing Test.
(Turing was too intelligent to propose a test for intelligence. Instead he was
doing something different, and more interesting.)
The "Philosophy" section of the AITopics web site. (Helpers needed to work on
Liz Stillwagon Swan's web site:
Includes some thoughtful papers and reviews -- with some
antagonism to AI-based models of mind.
There's lots more work on links between AI and Philosophy. If someone
has a good, comprehensive online bibliography, please send me a link.
Interest in relations between philosophy and AI seems to be growing
among philosophers, at long last.
E.g. see these recent announcements:
(Feel free to browse my previous presentations
Some of them are in 'flash' format on
my slideshare.net web site.)
AI and Biology
See books, journals, conferences on ALife and evolutionary
A. Sloman and J. Chappell,
The Altricial-Precocial Spectrum for Robots,
in Proceedings IJCAI'05,
Edinburgh, IJCAI, pp. 1187--1192, 2005
Jackie Chappell and Aaron Sloman,
Natural and artificial meta-configured altricial
International Journal of Unconventional Computing, 3, 3, pp. 211--239, 2007,
Symposium on AI-Inspired Biology (AIIB), 2010
There is a lot more material on the AITopics web site:
The wikipedia entry usefully summarises some aspects of AI, but
never believe any definition you read of AI, there or
anywhere else, as it is very unlikely to cover the full range of
research and teaching in the field.
Margaret A. Boden,
Mind As Machine: A history of Cognitive Science
Oxford University Press, 2006,
(Huge but enormously broad and deep.)
Herbert A. Simon,
Motivational and emotional controls of cognition,
in Models of Thought,,
Yale University Press,
Herbert A. Simon,
The Sciences of the Artificial, MIT Press, 1969.
D.R. Hofstadter and D.C. Dennett, editors
The Mind's I: Fantasies and Reflections on Self and Soul,
Penguin Books, London, 1981.
Important relevant work in Philosophy of Science
Falsification and the methodology of scientific research programmes,
Philosophical papers, Vol I,
Eds. J. Worrall and G. Currie,
Cambridge University Press, 1980, 0-521-28031-1, pp. 8--101,
(See if you can understand Lakatos' distinction between "progressive"
and "degenerative" research programmes.)
Much writing based on introspection gives information about what
needs to be explained by a theory of human consciousness, and
requirements for working models of human mental processes, or human
like intelligent robots.
First degree in mathematics and physics (CapeTown 1956),
DPhil in philosophy of mathematics (Oxford, 1962),
then worked in philosophy, cognitive science, AI and theoretical
Now officially retired, but doing
research full time.
"The Computer Revolution in Philosophy"
(1978) and many articles and book chapters,
contributor to the Poplog system for AI research and teaching.
Elected Fellow of AAAI,
of ECCAI and
Honorary DSc Sussex University (2006).
Online papers and presentations:
Some also on slideshare.net
Teaching and research support software
"Thinky" Programming for young learners
More examples and OVA download here
Videos of some talks
School of Computer Science
The University of Birmingham