Using AI Methods to Evaluate a Minimal Model for Perception

Robert Prentner 1  and Chris Fields 2
  • 1 University of California, , Irvine, United States of America
  • 2 , Caunes Minervois, France

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  • 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.

  • 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.

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

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

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

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

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

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

  • 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.

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

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

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

  • 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.

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

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

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

  • 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.

  • 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.

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

  • 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).

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

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

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

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

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

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

  • 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.

  • 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.

  • 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.

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

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

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

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

  • 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.

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

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

  • 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  • 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.

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

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

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

  • 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.

  • 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.

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

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

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

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

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

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