Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Open Philosophy

Editor-in-Chief: Harman, Graham


Covered by:
DOAJ - Directory of Open Access Journals
ERIH PLUS

Open Access
Online
ISSN
2543-8875
See all formats and pricing
More options …

Using AI Methods to Evaluate a Minimal Model for Perception

Robert Prentner / Chris Fields
Published Online: 2019-11-04 | DOI: https://doi.org/10.1515/opphil-2019-0034

Abstract

The relationship between philosophy and research on artificial intelligence (AI) has been difficult since its beginning, with mutual misunderstanding and sometimes even hostility. By contrast, we show how an approach informed by both philosophy and AI can be productive. After reviewing some popular frameworks for computation and learning, we apply the AI methodology of “build it and see” to tackle the philosophical and psychological problem of characterizing perception as distinct from sensation. Our model comprises a network of very simple, but interacting agents which have binary experiences of the “yes/no”-type and communicate their experiences with each other. When does such a network refer to a single agent instead of a distributed network of entities? We apply machine learning techniques to address the following related questions: i) how can the model explain stability of compound entities, and ii) how could the model implement a single task such as perceptual inference? We thereby find consistency with previous work on “interface” strategies from perception research.

While this reflects some necessary conditions for the ascription of agency, we suggest that it is not sufficient. Here, AI research, if it is intended to contribute to conceptual understanding, would benefit from issues previously raised by philosophy. We thus conclude the article with a discussion of action-selection, the role of embodiment, and consciousness to make this more explicit. We conjecture that a combination of AI research and philosophy allows general principles of mind and being to emerge from a “quasi-empirical” investigation.

Keywords: Computer Modeling; Agents; Networks; Perception; Consciousness; Embodiment; Interfaces; Attractors; Learning; Genetic Algorithms

References

  • Acedo, Luis. “A cellular automaton model for collective neural dynamics.” Mathematical and Computer Modelling, 50: 5-6 (2009), 717–725.Google Scholar

  • Agrawal, Himanshu. “Extreme self-organization in networks constructed from gene expression data.” Physical Review Letters, 89: 268702 (2012).Google Scholar

  • Amari, Sun-ichi. Information Geometry and its Applications. Tokyo: Springer Japan, 2016.Google Scholar

  • Augustyn, Kenneth A. (ed). “Biological Mentality.”, Journal of Cognitive Science, 19:2 (2018), 99 – 284.Google Scholar

  • Baldassarre Gianluca, and Mirolli, Marco (eds). Intrinsically motivated learning in natural and artificial systems. Berlin, Heidelberg: Springer, 2013.Google Scholar

  • Baluška, František, and Levin, Michael. “On having no head: Cognition throughout biological systems.” Frontiers in Psychology, 7:902 (2016).Google Scholar

  • Bateson, Gregory. Mind and Nature: A Necessary Unity. New York: Duton, 1979.Google Scholar

  • Boden, Margaret A. “Is metabolism necessary?” British Journal for the Philosophy of Science, 50 (1999), 231–248.CrossrefGoogle Scholar

  • Boden, Margaret A. The Creative Mind: Myths and Mechanisms. London: Routledge, 2003.Google Scholar

  • Cangelosi Angelo, and Schlesinger, Matthew. Developmental robotics: from babies to robots. Cambridge, MA: MIT Press, 2015.Google Scholar

  • Chater, Nick. The Mind is Flat. London: Allen Lane, 2018.Google Scholar

  • Chemero, Anthony. Radical Embodied Cognitive Science. Cambridge MA: MIT Press, 2009.Google Scholar

  • Clark, A. and Chalmers, D. The extended mind. Analysis, 58:1 (1998), 7-19.Google Scholar

  • Copeland, B. Jack “Hypercomputation.” Minds and Machines, 12:4 (2002), 461–502.CrossrefGoogle Scholar

  • Copeland, B. Jack. Artificial Intelligence: A Philosophical Introduction. Hoboken, NJ: Wiley-Blackwell, 1993.Google Scholar

  • Dennett, Daniel C. The Intentional Stance. Cambridge MA: MIT Press, 1987.Google Scholar

  • Dretske, Fred I. Knowledge and the Flow of Information. Cambridge MA: MIT Press, 1981.Google Scholar

  • Dreyfus, Hubert. What Computers Can’t Do. Cambridge MA: MIT Press, 1972.Google Scholar

  • Faggin, Federico. “Requirements for a Mathematical Theory of Consciousness.” Journal of Consciousness, 18 (2015), 421.Google Scholar

  • Fields, Chris, Hoffman, Donald D., Prakash, Chetan, and Singh, Manish. “Conscious agent networks: Formal analysis and application to cognition.” Cognitive Systems Research, 47 (2018), 186–213.CrossrefGoogle Scholar

  • Fields, Chris. “Consequences of nonclassical measurement for the algorithmic description of continuous dynamical systems.” Journal of Experimental and Theoretical Artificial Intelligence, 1 (1989), 171-178.Google Scholar

  • Fields, Chris. “If physics is an information science, what is an observer?” Information, 3 (2012), 92-123.Google Scholar

  • Friston, Karl. “Life as we know it.” Journal of the Royal Society Interface, 10 (2013), 2013.0475.Google Scholar

  • Froese, Tom, and Ziemke, Tom. “Enactive artificial intelligence: Investigating the systemic organization of life and mind.” Artificial Intelligence, 173 (2009), 466-500.Google Scholar

  • Gardener, Martin. “The fantastic combinations of John Conway’s new solitaire game ‘life’”. Scientific American, 223 (1970), 120-123.Google Scholar

  • Gebali, Fayes. Analysis of Computer Networks, second edition, Cham: Springer, 2015.Google Scholar

  • Gibson, James J. The Ecological Approach to Visual Perception, Boston: Houghton Mifflin, 1979.Google Scholar

  • Goertzel B, Lian R, Arel I, de Garis H, and Chen S. “A world survey of artificial brain projects, part II: biologically inspired cognitive architectures.” Neurocomputing, 74 (2010), 30–49.CrossrefGoogle Scholar

  • Greil, Florian, and Drossel, Barbara. “Dynamics of Critical Kauffman Networks under Asynchronous Stochastic Update.” Physical Review Letters, 95:4 (2005), 177–4.Google Scholar

  • Grossberg, Stephen. “Nonlinear neural networks: Principles, mechanisms, and architectures”. Neural Networks, 1 (1988), 17-61.CrossrefGoogle Scholar

  • Hebb, Donald O. The Organization of Behavior. New York: Wiley, 1949.Google Scholar

  • Hegselmann, Rainer, Mueller, Ulrich, and Troitzsch, Klaus G. (eds.). Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View, Dordrecht: Kluwer, 1996.Google Scholar

  • Heidegger, Martin. Being and Time. Oxford: Basil Blackwell, 1965.Google Scholar

  • Hoffman, Donald D., and Prakash, Chetan. “Objects of consciousness.” Frontiers in Psychology, 5:77 (2014).Google Scholar

  • Hoffman, Donald D., Prakash, Chetan, and Singh, Manish. “The Interface Theory of Perception”, Psychonomic Bulletin and Review, 22 (2015), 1480-1506.Google Scholar

  • Hofstadter, Douglas R., and Mitchell, Melanie. “The copycat project: A model of mental fluidity and analogy-making.” In Fluid Concepts and Creative Analogies, edited by Douglas R. Hofstadter, and the Fluid Analogies Research Group, 205-26. Cambridge, MA: MIT/Basic Books, 1995.Google Scholar

  • Holland, John H. Adaptation in Natural and Artificial Systems. An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge MA: MIT Press, 1992.Google Scholar

  • Husserl, Edmund. Logical Investigations. London: Routledge, 2001.Google Scholar

  • Jordan, J. Scott. The wild ways of conscious will: what we do, how we do it, and why it has meaning, Frontiers of Psychology, 4:574 (2013).Google Scholar

  • Kauffman, Stuart. The Origins of Order. Oxford: Oxford University Press, 1993.Google Scholar

  • Keijzer, Fred A. “Evolutionary convergence and biologically embodied cognition.” Interface Focus, 7 (2017), 20160123.Google Scholar

  • Knill, David C. and Richards, Whitman A. Perception as Bayesian Inference. Cambridge, UK: Cambridge University Press, 1996.Google Scholar

  • Latour, Bruno. Reassembling the Social: An Introduction to Actor-Network-Theory, Oxford: Clarendon Press, 2005.Google Scholar

  • LeCun, Yann, Bengio, Yoshua, and Hinton, Geoffrey. “Deep learning”. Nature, 521: 7553 (2015), 436–444.Google Scholar

  • Lucas, John R. “Minds, Machines, and Gödel.” Philosophy, 36 (1961), 112-127.CrossrefGoogle Scholar

  • Macy, Michael W., and Willer, Robert. “From Factors to Factors: Computational Sociology and Agent-Based Modeling.” Annual Review of Sociology, 28:1 (2002), 143–166.CrossrefGoogle Scholar

  • Mamassian, Pascal, Landy, Michael, and Maloney, Laurence T. “Bayesian Modelling of Visual Perception.” in Probabilistic Models of the Brain: Perception and Neural Function, edited by R.P.N. Rao et al., 13–36. Cambridge MA: MIT Press, 2002.Google Scholar

  • McClelland, James L., Rumelhart, David E. and the PDP Research Group. Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 2: Psychological and biological models. Cambridge MA: MIT Press/Bradford Books, 1986.Google Scholar

  • McCulloch, Warren S., and Pitts, Walter H. “A logical calculus of ideas immanent in nervous activity.” Bulleting of American Biophysics, 5 (1943), 115-133.Google Scholar

  • Merleau-Ponty, Maurice. Phenomenology of Perception. London: Routledge & Kegan Paul, 1965.Google Scholar

  • Mitchell, Melanie. Complexity: A Guided Tour, Oxford: Oxford University Press, 2009.Google Scholar

  • Mumford, David. “Pattern theory: the mathematics of perception.” International Congress of Mathematicians. Vol 3, 1-3 (2002), 1-21.Google Scholar

  • Newell, Allan, and Simon, Herbert A. “The logic theory machine: A complex information processing system.” IRE Transactions on Information Theory 2:3 (1956), 61-79.CrossrefGoogle Scholar

  • Oudeyer, Pierre-Yves, and Kaplan, Frederic. “What is intrinsic motivation? A typology of computational approaches.” Frontiers in Neurorobotics 1:6 (2009).Google Scholar

  • Palmer, Stephen E. Vision Science: Photons to Phenomenology. Cambridge MA: MIT Press, 1999.Google Scholar

  • Parker, Lynne E. “Distributed intelligence: Overview of the field and its application in multi-robot systems.” Journal of Physical Agents, 2:2 (2008), 5-14.Google Scholar

  • Penrose, Roger. The Emperor’s New Mind: Concerning Computers, Minds and The Laws of Physics. Oxford: Oxford University Press, 1989.Google Scholar

  • Picard, Rosalind W. “Affective Computing: From laughter to IEEE.” IEEE Transactions on Affective Computing, 1:1 (2010), 11-17.Google Scholar

  • Prakash, Chetan. “On Invention of Structure in the World: Interfaces and Conscious Agents.” Foundations of Science, 134: 3 (2019), 105–16.Google Scholar

  • Réka, Albert, and Barabási, Albert-Lázsló. “Statistical mechanics of complex networks.” Reviews of Modern Physics, 74: 1 (2012), 47–97.Google Scholar

  • Rovelli, Carlo. “Relational quantum mechanics.” International Journal of Theoretical Physics, 35:8 (1996), 1637–1678.Google Scholar

  • Rumelhart, David E., Hinton, Geoffrey E., and Williams, Ronald J. “Learning representations by back-propagating errors.” Nature, 323 (1986), 533-536.Google Scholar

  • Russell, Stuart, and Norvig, Peter. Artificial Intelligence: A Modern Approach. Third Edition. Upper Saddle River NJ: Prentice Hall, 2010.Google Scholar

  • Samuelsson, Björn, and Troein, Carl. “Superpolynomial Growth in the Number of Attractors in Kauffman Networks.” Physical Review Letters, 90:9 (2002), 589–4.Google Scholar

  • Sartre, Jean-Paul. Being and Nothingness: An Essay on Phenomenological Ontology. New York: Washington Square Press 1984.Google Scholar

  • Schelling, Thomas C. “Dynamic models of segregation.” The Journal of Mathematical Sociology, 1:2 (1971), 143–186.Google Scholar

  • Schmidhuber, Jürgen: “Deep learning in neural networks: an overview.” Neural Networks 61 (2015), 85–117.Google Scholar

  • Searle, John R. “Minds, brains, and programs.” Behavioral and Brain Sciences, 3 (1980), 417–457.CrossrefGoogle Scholar

  • Searle, John R. “The causal powers of the brain: The necessity of sufficiency” Behavioral and Brain Sciences, 13:1 (1990),164CrossrefGoogle Scholar

  • Siegelmann, Hava T. “Neural and Super-Turing Computing.” Minds and Machines, 13 (2003), 103–114.CrossrefGoogle Scholar

  • Silver, David, et al. “Mastering the game of Go with deep neural networks and tree search.” Nature, 529: 7585 (2016), 484–489.Google Scholar

  • Sipper, Moshe. “Simple + parallel + local = cellular computing.” In Parallel Problem Solving from Nature – PPSN V, LNCS 1498 edited by A.E. Eiben et al., 653-662. Berlin, Heidelberg: Springer, 1998.Google Scholar

  • Sloman, Aaron: The Computer Revolution in Philosophy: Philosophy Science and Models of Mind. Harvester UK: Harvester Press, 1978.Google Scholar

  • Smolensky, Paul. “On the proper treatment of connectionism.” Behavioral and Brain Sciences, 11 (1988), 1 – 74.CrossrefGoogle Scholar

  • Sporns, Olaf, and Honey, Christopher J. “Small worlds inside big brains.” PNAS, 103: 51 (2006), 19219-19220.Google Scholar

  • Steels, Luc and Brooks, Rodney A. (eds). The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc., 1995.Google Scholar

  • Turing, Alan. The Essential Turing: Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life plus The Secrets of Enigma. Edited by B. Jack Copeland, Oxford: Clarendon Press, 2004.Google Scholar

  • Von Neumann, John, and Burks, Arthur. Theory of Self-reproducing Automata. Urbana Champaign IL: Illinois University Press, 1966.Google Scholar

  • Warren, Richard M. “Perceptual restoration of missing speech sounds.” Science, 167:3917 (1970), 392-393.Google Scholar

  • Watts, Duncan J., and Strogatz, Steven H. “Collective dynamics of ‘small-world’ networks.” Nature, 393:6684 (1998), 440–442.Google Scholar

  • Wolfram Research, Inc. Mathematica 11.2. Champaign IL, 2017.Google Scholar

  • Wolfram, Stephen. A New Kind of Science, Champaign IL: Wolfram Media, 2002.Google Scholar

About the article

Received: 2019-04-29

Accepted: 2019-10-04

Published Online: 2019-11-04

Published in Print: 2019-01-01


Citation Information: Open Philosophy, Volume 2, Issue 1, Pages 503–524, ISSN (Online) 2543-8875, DOI: https://doi.org/10.1515/opphil-2019-0034.

Export Citation

© 2019 Robert Prentner et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 Public License. BY 4.0

Comments (0)

Please log in or register to comment.
Log in