Putting ourselves back i.., p.27

Putting Ourselves Back in the Equation, page 27

 

Putting Ourselves Back in the Equation
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  34.  Ian J. Goodfellow et al., “Generative Adversarial Networks” (preprint, submitted 10 June 2014).

  35.  David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams, “Learning Representations by Back-Propagating Errors,” Nature 323, no. 6088 (9 October 1986): 533–36.

  36.  Yann LeCun, “A Theoretical Framework for Back-Propagation,” in Proceedings of the 1988 Connectionist Models Summer School, ed. David S. Touretzky, Geoffrey E. Hinton, and Terrence J. Sejnowski (San Mateo, CA: Morgan Kaufmann, 1988), 21–28.

  37.  James A. Anderson and Edward Rosenfeld, “Geoffrey E. Hinton,” in Talking Nets: An Oral History of Neural Networks (Cambridge, MA: MIT Press, 2000), 379.

  38.  Ali Rahimi, “Back When We Were Young” (lecture, Conference on Neural Information Processing Systems, Long Beach, CA, 5 December 2017).

  39.  Lawrence M. Principe, “Reflections on Newton’s Alchemy in Light of the New Historiography of Alchemy,” in Newton and Newtonianism, ed. James E. Force and Sarah Hutton (Boston: Kluwer Academic, 2004), 2015–19.

  40.  Chris Olah, Alexander Mordvintsev, and Ludwig Schubert, “Feature Visualization,” Distill, 7 November 2017, https://distill.pub/2017/feature-visualization/.

  41.  Michael Hahn and Marco Baroni, “Tabula Nearly Rasa: Probing the Linguistic Knowledge of Character-Level Neural Language Models Trained on Unsegmented Text” (preprint, submitted 17 June 2019).

  42.  Yann LeCun et al., “Gradient-Based Learning Applied to Document Recognition,” Proceedings of the IEEE 86, no. 11 (November 1998): 2278–324.

  43.  Ashish Vaswani et al., “Attention Is All You Need” (preprint, submitted 12 June 2017).

  44.  Geoffrey E. Hinton, “Connectionist Learning Procedures,” Artificial Intelligence 40, no. 1–3 (September 1989): 185–234.

  45.  Terrence J. Sejnowski and Charles R. Rosenberg, “Parallel Networks That Learn to Pronounce English Text,” Complex Systems 1, no. 1 (February 1987): 145–68.

  46.  Chiyuan Zhang et al., “Understanding Deep Learning Requires Rethinking Generalization” (preprint, submitted 10 November 2016); Mikhail Belkin et al., “Reconciling Modern Machine-Learning Practice and the Classical Bias-Variance Trade-Off,” Proceedings of the National Academy of Sciences of the United States of America 116, no. 32 (6 August 2019): 15849–54.

  47.  Mario Geiger et al., “Jamming Transition as a Paradigm to Understand the Loss Landscape of Deep Neural Networks,” Physical Review E 100, no. 1 (11 July 2019): 012115.

  48.  Terrence J. Sejnowski, “The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence,” Proceedings of the National Academy of Sciences 117, no. 48 (1 December 2020): 30033–38.

  49.  Surya Ganguli and Haim Sompolinsky, “Compressed Sensing, Sparsity, and Dimensionality in Neuronal Information Processing and Data Analysis,” Annual Review of Neuroscience 35, no. 1 (July 2012): 485–508.

  50.  Daniel J. Amit, Hanoch Gutfreund, and Haim Sompolinsky, “Spin-glass Models of Neural Networks,” Physical Review A 32, no. 2 (August 1985): 1007–18; Radford M. Neal, Bayesian Learning for Neural Networks: Lecture Notes in Statistics (New York: Springer New York, 1996), chap 2.

  51.  Jaehoon Lee et al., “Deep Neural Networks as Gaussian Processes” (preprint, submitted 31 October 2017).

  52.  Carl Edward Rasmussen, “Gaussian Processes in Machine Learning,” in Advanced Lectures on Machine Learning: Lecture Notes in Computer Science, ed. Olivier Bousquet, Ulrike von Luxburg, and Gunnar Rätsch (Berlin, Heidelberg: Springer Berlin Heidelberg, 2004).

  53.  Samuel S. Schoenholz et al., “Deep Information Propagation” (preprint, submitted 4 November 2016).

  54.  Thierry Mora and William Bialek, “Are Biological Systems Poised at Criticality?,” Journal of Statistical Physics 144, no. 2 (2 June 2011): 268–302.

  55.  Sho Yaida, “Non-Gaussian Processes and Neural Networks at Finite Widths” (preprint, submitted 30 September 2019).

  56.  Richard P. Feynman, “Simulating Physics with Computers,” International Journal of Theoretical Physics 21, no. 6/7 (1982): 467–88.

  57.  Elizabeth C. Behrman et al., “A Quantum Dot Neural Network,” in Proceedings of the Fourth Workshop on Physics of Computation, ed. Tommaso Toffoli, Michael Biafore, and João Leão (Cambridge, MA: New England Complex Systems Institute, 1996), 22–24.

  58.  Hidetoshi Nishimori and Yoshihiko Nonomura, “Quantum Effects in Neural Networks,” Journal of the Physical Society of Japan 65, no. 12 (15 December 1996): 3780–96.

  59.  Tadashi Kadowaki and Hidetoshi Nishimori, “Quantum Annealing in the Transverse Ising Model,” Physical Review E 58, no. 5 (1 November 1998): 5355–63.

  60.  M. W. Johnson et al., “Quantum Annealing with Manufactured Spins,” Nature 473, no. 7346 (12 May 2011): 194–98.

  61.  Quinten Hardy, “A Strange Computer Promises Great Speed,” New York Times, 22 March 2013.

  62.  Jacob Biamonte et al., “Quantum Machine Learning,” Nature 549, no. 7671 (13 September 2017): 195–202.

  63.  Hartmut Neven, “Car Detector Trained with the Quantum Adiabatic Algorithm” (demonstration, Conference on Neural Information Processing Systems, Vancouver, Canada, 8 December 2009).

  64.  Alex Mott et al., “Solving a Higgs Optimization Problem with Quantum Annealing for Machine Learning,” Nature 550, no. 7676 (19 October 2017): 375–79.

  65.  Vasil S. Denchev et al., “What Is the Computational Value of Finite-Range Tunneling?,” Physical Review X 6, no. 3 (1 August 2016): 031015.

  66.  Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione, “Quantum Computing for Pattern Classification,” in Lecture Notes in Computer Science: PRICAI 2014: Trends in Artificial Intelligence, ed. Duc-Nghia Pham and Seong-Bae Park (Cham, Switzerland: Springer International, 2014), 208–20.

  67.  Elizabeth C. Behrman and James E. Steck, “Multiqubit Entanglement of a General Input State,” Quantum Information and Computation 13, no. 1/2 (2013): 36–53; Edward Farhi and Hartmut Neven, “Classification with Quantum Networks on Near Term Processors” (preprint, submitted 18 December 2017); A. V. Uvarov, A. S. Kardashin, and Jacob D. Biamonte, “Machine Learning Phase Transitions with a Quantum Processor,” Physical Review A 102, no. 1 (15 July 2020): 012415.

  68.  Gia Dvali, “Black Holes as Brains: Neural Networks with Area Law Entropy,” Fortschritt der Physik 66, no. 4 (27 March 2018): 1800007.

  69.  C. F. von Weizsäcker, “Physics and Philosophy,” in The Physicist’s Conception of Nature, ed. Jagdish Mehra (Dordrecht: Springer Netherlands, 1973), 737.

  3. PHYSICS OF THE MIND

    1   Jakob Hohwy, The Predictive Mind (New York: Oxford University Press, 2013), 5.

    2   Piotr Litwin and Marcin Miłkowski, “Unification by Fiat: Arrested Development of Predictive Processing,” Cognitive Science 44, no. 7 (July 2020): e12867.

    3   Johannes Kleiner and Erik P. Hoel, “Falsification and Consciousness,” Neuroscience of Consciousness 2021, no. 1 (2021): nniab001.

    4   David Cahan, Helmholtz: A Life in Science (Chicago: University of Chicago Press, 2018), 59.

    5   Cahan, Helmholtz, 66–70.

    6   Cahan, Helmholtz, 90–95, 327–30.

    7   David M. Eagleman, “How Does the Timing of Neural Signals Map onto the Timing of Perception?,” in Space and Time in Perception and Action, ed. Romi Nijhawan and Beena Khurana (Cambridge: Cambridge University Press, 2010): 216–31.

    8   Hermann von Helmholtz, Treatise on Physiological Optics, trans. James P. C. Southall (Rochester, NY: Optical Society of America, 1925), sec. 26.

    9   Helmholtz, Treatise on Physiological Optics, sec. 24.

  10.  Helmholtz, Treatise on Physiological Optics, sec. 32.

  11.  Hohwy, Predictive Mind, 217.

  12.  Karl J. Friston, “Hallucinations and Perceptual Inference,” Behavioral and Brain Sciences 28, no. 6 (22 December 2005): 764–66.

  13.  Cahan, Helmholtz, 310, 327–40.

  14.  Donald M. MacKay, “The Epistemological Problem for Automata,” in Automata Studies, ed. Claude E. Shannon and J. McCarthy (Princeton, NJ: Princeton University Press, 1956), 235–52.

  15.  Donald M. MacKay, “Mindlike Behaviour in Artefacts,” British Journal for the Philosophy of Science 2, no. 6 (October 1951): 105–21.

  16.  M. V. Srinivasan, S. B. Laughlin, and A. Dubs, “Predictive Coding: A Fresh View of Inhibition in the Retina,” Proceedings of the Royal Society B: Biological Sciences 216, no. 1205 (22 November 1982): 427–59.

  17.  Khalid Sayood, Introduction to Data Compression, 4th ed. (Waltham, MA: Morgan Kaufmann, 2012), chaps. 3, 7.

  18.  Peter Dayan et al., “The Helmholtz Machine,” Neural Computation 7, no. 5 (September 1995): 889–904.

  19.  Rajesh P. N. Rao and Dana H. Ballard, “Predictive Coding in the Visual Cortex: A Functional Interpretation of Some Extra-Classical Receptive-Field Effects,” Nature Neuroscience 2, no. 1 (January 1999): 79–87.

  20.  Benjamin Kuipers et al., “Shakey: From Conception to History,” AI Magazine 38, no. 1 (Spring 2017): 88–103.

  21.  Jun Tani, “Model-Based Learning for Mobile Robot Navigation from the Dynamical Systems Perspective,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 26, no. 3 (June 1996): 421–36.

  22.  Jun Tani, “Learning to Generate Articulated Behavior Through the Bottom-Up and the Top-Down Interaction Processes,” Neural Networks 16, no. 1 (January 2003): 11–23.

  23.  Jakob Hohwy, “Priors in Perception: Top-Down Modulation, Bayesian Perceptual Learning Rate, and Prediction Error Minimization,” Consciousness and Cognition 47 (January 2017): 75–85.

  24.  Julian Kiverstein, Mark Miller, and Erik Rietveld, “The Feeling of Grip: Novelty, Error Dynamics, and the Predictive Brain,” Synthese 196, no. 7 (23 October 2017): 2847–69.

  25.  George Musser, “How Autism May Stem from Problems with Prediction,” Spectrum News (7 March 2018), https://www.spectrumnews.org/features/deep-dive/autism-may-stem-problems-prediction/.

  26.  Karl J. Friston, “Learning and Inference in the Brain,” Neural Networks 16, no. 9 (November 2003): 1325–52.

  27.  William James, The Principles of Psychology (New York: Holt, 1890), chap. 26.

  28.  Sam Schramski, “Running Is Always Blind,” Nautilus 38 (30 June 2016), https://nautil.us/running-is-always-blind-236003/.

  29.  Karl J. Friston et al., “Dopamine, Affordance and Active Inference,” PLoS Computational Biology 8, no. 1 (January 2012): e1002327.

  30.  Karl J. Friston. “I Am Therefore I Think” (lecture, Foundational Questions Institute Sixth International Conference, Castelvecchio Pascoli, Italy, 23 July 2019).

  31.  Michael D. Kirchhoff and Tom Froese, “Where There Is Life There Is Mind: In Support of a Strong Life-Mind Continuity Thesis,” Entropy 19, no. 4 (14 April 2017): 169.

  32.  James Kasting, How to Find a Habitable Planet (Princeton, NJ: Princeton University Press, 2012), 49–56.

  33.  Sergio Rubin et al., “Future Climates: Markov Blankets and Active Inference in the Biosphere,” Journal of the Royal Society Interface 17, no. 172 (November 2020): 20200503.

  34.  Artemy Kolchinsky and David H. Wolpert, “Semantic Information, Autonomous Agency and Non-Equilibrium Statistical Physics,” Interface Focus 8, no. 6 (6 December 2018): 20180041.

  35.  Jun Tani, “An Interpretation of the ‘Self’ from the Dynamical Systems Perspective: A Constructivist Approach,” Journal of Consciousness Studies 5 (1 May 1998): 516–42.

  36.  Kelsey Klotz, “The Art of the Mistake,” The Common Reader 11 (Summer 2019), https://commonreader.wustl.edu/c/the-art-of-the-mistake/.

  37.  René Descartes, Meditations on First Philosophy, trans. Michael Moriarty (New York: Oxford University Press, 2008), 60–61.

  38.  Tim Bayne, “On the Axiomatic Foundations of the Integrated Information Theory of Consciousness,” Neuroscience of Consciousness 2018, no. 1 (29 June 2018): 159.

  39.  Pedro A. M. Mediano et al., “Integrated Information as a Common Signature of Dynamical and Information-Processing Complexity,” Chaos 32, no. 1 (January 2022): 013115.

  40.  Hyoungkyu Kim and UnCheol Lee, “Criticality as a Determinant of Integrated Information Φ in Human Brain Networks,” Entropy 21, no. 10 (October 2019): 981.

  41.  Max Tegmark, “Improved Measures of Integrated Information,” PLoS Computational Biology 12, no. 11 (21 November 2016): e1005123.

  42.  Tim Bayne, Jakob Hohwy, and Adrian M. Owen, “Are There Levels of Consciousness?,” Trends in Cognitive Sciences 20, no. 6 (June 2016): 405–13.

  43.  Mark S. George, “Stimulating the Brain,” Scientific American 289 (September 2003): 66–73.

  44.  Adenauer G. Casali et al., “A Theoretically Based Index of Consciousness Independent of Sensory Processing and Behavior,” Science Translational Medicine 5, no. 198 (14 August 2013): 198ra105.

  45.  A. Arena et al., “General Anesthesia Disrupts Complex Cortical Dynamics in Response to Intracranial Electrical Stimulation in Rats,” eNeuro 8, no. 4 (5 August 2021): ENEURO.0343–20.2021; Roberto N. Muñoz et al., “General Anesthesia Reduces Complexity and Temporal Asymmetry of the Informational Structures Derived from Neural Recordings in Drosophila,” Physical Review Research 2 (22 May 2020): 023219.

  46.  David Balduzzi and Giulio Tononi, “Qualia: The Geometry of Integrated Information,” PLoS Computational Biology 5, no. 8 (August 2009): e1000462.

  47.  Mélanie Boly, “Are the Neural Correlates of Consciousness (Mostly) in the Front or in the Back of the Cerebral Cortex?” (lecture, Association of the Scientific Study of Consciousness, Kraków, Poland, 18 June 2018).

  48.  Ryota Kanai, “Consciousness and A.I.” (lecture, Human-Level AI 2018, Prague, 25 August 2018); Jun Kitazono, Ryota Kanai, and Masafumi Oizumi, “Efficient Search for Informational Cores in Complex Systems: Application to Brain Networks,” Neural Networks 132 (December 2020): 232–44.

  49.  Brian Odegaard, Robert T. Knight, and Hakwan Lau, “Should a Few Null Findings Falsify Prefrontal Theories of Conscious Perception?,” Journal of Neuroscience 37, no. 40 (4 October 2017): 9593–602.

  50.  Kirchhoff and Froese, “Where There Is Life”; Philip Goff, Galileo’s Error: Foundations for a New Science of Consciousness (New York: Pantheon, 2019), 138–39.

  51.  Goff, Galileo’s Error, 164–69.

  52.  Karl J. Friston, Wanja Wiese, and J. Allan Hobson, “Sentience and the Origins of Consciousness: From Cartesian Duality to Markovian Monism,” Entropy 22, no. 5 (May 2020): 17–18.

  53.  Giulio Tononi and Christof Koch, “Consciousness: Here, There and Everywhere?,” Philosophical Transactions of the Royal Society B: Biological Sciences 370, no. 1668 (19 May 2015): 13.

  54.  Giulio Tononi et al., “Integrated Information Theory: From Consciousness to Its Physical Substrate,” Nature Reviews Neuroscience 17, no. 7 (July 2016): 455.

  55.  Tim Bayne, Anik K. Seth, and Marcello Massimini, “Are There Islands of Awareness?,” Trends in Neurosciences 43, no. 1 (January 2020): 6–16.

  56.  Hedda Hassel Mørch, “Is the Integrated Information Theory of Consciousness Compatible with Russellian Panpsychism?,” Erkenntnis 84, no. 5 (10 April 2018): 1065–85.

  57.  Daniel A. Friedman and Eirik Søvik, “The Ant Colony as a Test for Scientific Theories of Consciousness,” Synthese 198, no. 2 (12 February 2019): 1457–80.

  58.  Christian List, “What Is It Like to Be a Group Agent?,” Noûs 52, no. 2 (28 July 2016): 295–319.

  59.  Margaret A. Boden, Mind as Machine: A History of Cognitive Science, 2 vols. (New York: Oxford University Press, 2006), 1356–62.

  60.  David J. Chalmers, The Conscious Mind: In Search of a Fundamental Theory (New York: Oxford University Press, 1996), 84–88.

  61.  Larissa Albantakis et al., “Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity,” PLoS Computational Biology 10, no. 12 (18 December 2014): e1003966.

  62.  Tononi and Koch, “Consciousness,” 15.

  63.  Wanja Wiese and Karl J. Friston, “The Neural Correlates of Consciousness Under the Free Energy Principle: From Computational Correlates to Computational Explanation,” Philosophy and the Mind Sciences 2 (22 September 2021).

  64.  Katherine Elkins and Jon Chun, “Can GPT-3 Pass a Writer’s Turing Test?,” Journal of Cultural Analytics 5, no. 2 (14 September 2020).

 

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