Modelos dinámicos aplicados al aprendizaje de valores en inteligencia artificial

Autores/as

DOI:

https://doi.org/10.15448/1984-6746.2020.2.37439

Palabras clave:

Inteligencia Artificial., Aprendizaje de valores, Ciencia cognitiva, Sistemas dinámicos.

Resumen

los expertos en desarrollo de Inteligencia Artificial (IA) predicen que el progreso en el desarrollo de sistemas y agentes inteligentes remodelará áreas vitales en nuestra sociedad. Sin embargo, si ese progreso no se lleva a cabo de manera prudente y crítico-reflexiva, puede resultar en resultados negativos para la humanidad. Por esta razón, varios investigadores en el campo han desarrollado un diseño de IA robusta, beneficiosa y segura para la preservación de la humanidad y el medio ambiente. Actualmente, varios de los problemas abiertos en el campo de investigación de IA se derivan de la dificultad de evitar comportamientos no deseados de agentes y sistemas inteligentes, y al mismo tiempo especificar lo que realmente queremos que hagan estos sistemas, especialmente cuando prospectemos la posibilidad de que agentes inteligentes operen en varias áreas a largo plazo.  Es de suma importancia que los agentes inteligentes artificiales tengan sus valores alineados con los valores humanos, dado el hecho de que no podemos esperar que una IA desarrolle valores morales humanos debido a su inteligencia, como se discutió en la Tesis de Ortogonalidad. Tal vez esta dificultad proviene de la forma en que estamos abordando el problema de expresar objetivos, valores y objetivos, utilizando métodos cognitivos representativos. Una solución a este problema sería el enfoque dinámico propuesto por Dreyfus, que
basado en la filosofía fenomenológica muestra que la experiencia humana del ser-en-el-mundo en diversos aspectos no está bien representada por el método cognitivo simbólico o conexionista, especialmente en la cuestión del aprendizaje de valores. Un posible enfoque de este problema sería utilizar modelos teóricos como (SED) (situated embodied dynamics) para abordar el problema del los valores en la IA.

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Biografía del autor/a

Nicholas Kluge Corrêa, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, RS.

Mestre em Engenharia Elétrica pela Pontifícia Universidade Católica do Rio Grande do Sul (Escola Politécnica, PUCRS, Porto Alegre, RS, Brasil) e doutorando em Filosofia (PUCRS) Porto Alegre, RS, Brasil. Bolsista CAPES/PROEX.

Nythamar de Oliveira, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, RS.

Ph.D. in Philosophy (State University of New York). Professor titular da Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, RS.

Citas

ABADI, M. et al. Deep Learning with Differential Privacy. In: ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (ACM CCS). Proceedings […]. [S. l.]: ACM, 2016. p. 308-318. Disponível em: arxiv.org/abs/1607.00133, 2016. Acesso em: 02 mar. 2020. https://doi.org/10.1145/2976749.2978318. DOI: https://doi.org/10.1145/2976749.2978318

ALLISON, H. E. Idealism and Freedom: Essays on Kant’s Theoretical and Practical Philosophy. Cambridge: Cambridge University Press, 1996. https://doi.org/10.1017/CBO9781139172875. DOI: https://doi.org/10.1017/CBO9781139172875

AMODEI, D.; OLAH, C.; STEINHARDT, J.; CHRISTIANO, P.; SCHULMAN, J. MANÉ, D. Concrete problems in AI safety. arXiv preprint, [s. l.], 25 July 2016. Disponível em: https://arxiv.org/pdf/1606.06565.pdf. Acesso em: 02 mar. 2020.

ASIMOV, I. I, Robot. New York: Doubleday, 1950.

BARRETT, A. M.; BAUM, S. D. A Model of Pathways to Artificial Superintelligence Catastrophe for Risk and Decision Analysis. Journal of Experimental & Theoretical Artificial Intelligence, JETAI, London, v. 29, n. 2, p. 397 414, 2017. Disponível em: https://arxiv.org/pdf/1607.07730. Acesso em: 02 mar. 2020. https://doi.org/10.1080/0952813X.2016.1186228. DOI: https://doi.org/10.1080/0952813X.2016.1186228

BEER, R. D. Computational and dynamical languages for autonomous agents. In: It’s about time: An overview of the dynamical approach to cognition. Mind as motion: Explorations in the dynamics of cognition. Cambridge, Mass.: MIT Press, 1998. p. 121-147.

BEER, R. D. Dynamical approaches to cognitive science. Trends in Cognitive Sciences. 4. ed. [S. l.: s.n.], 2000. p. 91-99. https://doi.org/10.1016/S1364-6613(99)01440-0. DOI: https://doi.org/10.1016/S1364-6613(99)01440-0

BEER, R. D. The dynamics of active categorical perception in an evolved model agent (with commentary and response). Adaptive Behavior, Cambridge, Mass, v. 11, n. 4, p. 209-243, dez. 2003. https://doi.org/10.1177/1059712303114001. DOI: https://doi.org/10.1177/1059712303114001

BOSTROM, N. The Superintelligent Will: Motivation and Instrumental Rationality in Advance Artificial Agents. Minds and Machines, Dordrecht, Holanda, NL, v. 22, p. 71-85, 2012. https://doi.org/10.1007/s11023-012-9281-3. DOI: https://doi.org/10.1007/s11023-012-9281-3

BOSTROM, N. Superintelligence. Oxford University Press, 2014. Chapter 12.

BOSTROM, N.; ĆIRKOVIĆ, M. Introduction. In: Bostrom, N.;Ćirković, M. (ed.). Global Catastrophic Risks. New York: Oxford University Press, 2008. p. 1-30. DOI: https://doi.org/10.1093/oso/9780198570509.003.0004

BRYNJOLFSSON, E.; MCAFEE, A. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton Company, 2014.

CHIEL, H. J.; BEER, R. D. The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment. Trends in neurosciences, Amsterdam, NL, v. 20, n. 12, p. 553-557, 1 Dec. 2012. https://doi.org/10.1016/S0166-2236(97)01149-1. DOI: https://doi.org/10.1016/S0166-2236(97)01149-1

CHURCHLAND, P. S.; SEJNOWSKI, T. The computational brain. Cambridge, MA: MIT Press, 1992. https://doi.org/10.7551/mitpress/2010.001.0001. DOI: https://doi.org/10.7551/mitpress/2010.001.0001

CHURCHILL, R. R.; ULFSTEIN, G. Autonomous Institutional Arrangements in Multilateral Environmental Agreements: A Little-Noticed Phenomenon in International Law. American Journal of International Law, Washington, US, v. 94, n. 4, p. 623-659, 2000. Disponível em: dx.doi.org/10.2307/2589775. Acesso em 02 mar. 2020. DOI: https://doi.org/10.2307/2589775

CLARK, A.; CHALMERS, D. J. The extended mind. Analysis, Oxford, v. 58, n. 1, p. 7-19, Jan. 1998. https://doi.org/10.1093/analys/58.1.7. DOI: https://doi.org/10.1093/analys/58.1.7

DEWEY, D. Learning what to value. In: INTERNATIONAL CONFERENCE, AGI 4, August 3–6, 2011, Mountain View, CA, USA. Artificial General Intelligence: proceedings. [S. l: s. n.], 2011. p. 309-314. https://doi.org/10.1007/978-3-642-22887-2_35. DOI: https://doi.org/10.1007/978-3-642-22887-2_35

DIJKSTRA, E. W. The threats to computing science. In: ACM SOUTH CENTRAL REGIONAL CONFERENCE, Nov. 16-18, 1984, Austin, TX. Proceedings. [S. l: s. n.], 1984. p. 1-6.

DOCHERTY, B. L. Losing Humanity: The Case Against Killer Robots. New York: Human Rights Watch, 2012.

DREYFUS, H. L. What Computers Still Can’t Do: A critique of Artificial Reason. Cambridge: MIT Press, 1992.

DREYFUS, H. L. Why Heideggerian Artificial Intelligence failed and how fixing it would require making it more Heideggerian. Philosophical Psychology, Abingdon, Inglaterra, v. 20, n. 2, p. 247-268, 2007. https://doi.org/10.1080/09515080701239510. DOI: https://doi.org/10.1080/09515080701239510

ELIASMITH, C. The third contender: A critical examination of the dynamicist theory of cognition. Philosophical Psychology, Abingdon, v. 9, n. 4, p. 441-463, 1996. https://doi.org/10.1080/09515089608573194. DOI: https://doi.org/10.1080/09515089608573194

FRANKISH, K.; RAMSEY, W. N. The Cambridge handbook of artificial intelligence. Cambridge: Cambridge University Press. 2014. https://doi.org/10.1017/CBO9781139046855. DOI: https://doi.org/10.1017/CBO9781139046855

FREY, C.; OSBORNE, M. The Future of Employment: How Susceptible Are Jobs to Computerisation? Technical Report, Oxford Martin School. Oxford, UK: University of Oxford, 2013.. 6, p. 18-26, 1992.

GÄRDENFORS, P. Conceptual Spaces: The Geometry of Thought. [S. l.]: MIT Press. 2000. https://doi.org/10.7551/mitpress/2076.001.0001. DOI: https://doi.org/10.7551/mitpress/2076.001.0001

GIBSON, J. J. The Ecological Approach to Visual Perception. [S. l.]: Houghton Mifflin, 1979.

GRACE, K.; SALVATIER, J.; DAFOE, A.; ZHANG, B.; EVANS, O. When will AI exceed human performance? Evidence from AI experts. arXiv preprint, 3 May 2018. Disponível em: https://arxiv.org/pdf/1705.08807.pdf. Acesso em: 02 mar. 2020.

HADFIELD-MENELL, D.; RUSSELL, S. J.; ABBEEL, P.;DRAGAN, A. Cooperative inverse reinforcement learning. In: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS NEURAL INFORMATION PROCESSING SYSTEMS CONFERENCE (NIPS), 29., 2016, [S. l.]. Proceedings [S. l.]: NIPS, 2016. p. 3909-3917.

HEIDEGGER, M. Ser e Tempo. Tradução e edição bilíngue com notas de Fausto Castilho. Campinas: Editora Unicamp; Petrópolis: Vozes, 2012. (1927).

HIBBARD, B. The error in my 2001 VisFiles column. [S.l.: s. n.], 2012.

HUME, D. Tratado da Natureza Humana. Tradução de Débora Danowiski. 2.ª ed. São Paulo: Editora da UNESP, 2009. (1739).

JAYNES, E. T. Probability theory: The logic of science. Ed. G. Larry Bretthorst. New York: Cambridge University Press. 2003. Disponível em: doi:10.2277/0521592712. Acesso em: 02 Mar. 2020. https://doi.org/10.1017/CBO9780511790423. DOI: https://doi.org/10.1017/CBO9780511790423

KRIEGESKORTE, N.; KIEVIT, R. A. Representational geometry: integrating cognition, computation, and the brain. Trends in Cognitive Sciences, [s. l.], v.17, n.8, p. 401-12, 2013. https://doi.org/10.1016/j.tics.2013.06.007. DOI: https://doi.org/10.1016/j.tics.2013.06.007

KUZNETSOV, Y. A. Elements of Applied Bifurcation Theory. 3. ed. [S. l.]: Springer. 2004. https://doi.org/10.1007/978-1-4757-3978-7. DOI: https://doi.org/10.1007/978-1-4757-3978-7

LAKOFF, G.; JOHNSON, M. Philosophy in the Flesh. [S.l.]: Basic Books. 1999.

LEGG, S. Is there an elegant universal theory of prediction? In: INTERNATIONAL CONFERENCE ON ALGORITHMIC LEARNING THEORY, ALT 2006,Barcelona, Spain, October 7–10, 2006. Proceedings [...]. [Berlin: Springer], 2006. p. 274-287. https://doi.org/10.1007/11894841_23. DOI: https://doi.org/10.1007/11894841_23

LEIKE, J.; MARTIC, M.; KRAKOVNA, V.; ORTEGA, P.; EVERITT, T.; LEFRANCQ, A.; ORSEAU, L. AI Safety Gridworlds. arXiv preprint, 28 Nov. 2017. Disponível em: https://arxiv.org/pdf/1711.09883.pdf. Acesso em: 03 Mar. 2020.

LLOYD, S. Computational capacity of the universe. Physical Review Letters, New York, v. 88, n. 23, p. 237901, 2002. Disponível em: doi:10. 1103/PhysRevLett.88.237901. Acesso em: 03 Mar. 2020. DOI: https://doi.org/10.1103/PhysRevLett.88.237901

MERLEAU-PONTY, M. Phenomenology of Perception. New York: Humanities Press, 1962. (1945).

MORAVEC, H. P. When will computer hardware match the human brain? Journal of Evolution and Technology, [s. l.], v. 1, p. [1-12], 1998. Disponível em: http://www.transhumanist.com/volume1/moravec.htm. Acesso em: 03 Mar. 2020.

MORDVINTSEV, A.; OLAH, C.; TYKA, M. Inceptionism: Going deeper into neural networks. In: Google Research Blog, 17 June 2015. Disponível em: https://ai.googleblog.com/2015/06/inceptionism-going-deeper-intoneural.html. Acesso em: 02 Mar. 2020.

MÜLLER, V. C.; BOSTROM, N. Future progress in artificial intelligence: A survey of expert opinion. In: MÜLLER, V. (ed.). Fundamental issues of artificial intelligence. [S. l.]: Springer, 2016. p. 555-572. https://doi.org/10.1007/978-3-319-26485-1_33. DOI: https://doi.org/10.1007/978-3-319-26485-1_33

NEWELL, A. Unified theories of cognition. Cambridge, MA: Harvard University Press, 1990. NG, A. Y.; RUSSELL, S. J. Algorithms for inverse reinforcement learning. In: INTERNATIONAL CONFERENCE ON MACHINE LEARNING (ICML-’00), 17th,[S. l.]. Proceedings. ed. Pat Langley. [S. l: s. n.], 2000. p.663-670.

OMOHUNDRO, S. M. The Nature of Self-Improving Artificial Intelligence. Sept. 5, 2007, revised Jan. 21, 2008. Disponível em: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.137.1199&rep=rep1&-type=pdf. Acesso em: 11 de ago. 2019.

QUINE, W. V. O. Epistemology Naturalized. In: QUINE, W. V. Ontological Relativity and Other Essays. New York: Columbia University Press, 1969. p. 69-90.https://doi.org/10.7312/quin92204-004. DOI: https://doi.org/10.7312/quin92204-004

RAIBERT, M. H.; HODGINS, J.K. Biological Neural Networks. Invertebrate Neuroethology and Robotics. [S. l.]: Academic Press, 1993. p. 319-354.

REIMANN, M. W.; NOLTE, M.; SCOLAMIERO, M.;TURNER, K.; PERIN, R.; CHINDEMI, G.; DŁOTKO, P.; LEVI, R.; HESS, K.; MARKRAM, H. Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function. Frontiers in Computational Neuroscience, Lausanne, Switzerland, v. 11, Artigo 48, p. 1-16, 2017. Disponível em: doi: 10.3389/fncom.2017.0004. Acesso em: 02 Mar. 2020. https://doi.org/10.3389/fncom.2017.00048. DOI: https://doi.org/10.3389/fncom.2017.00048

ROCKWELL, T. Extended cognition and intrinsic properties. Philosophical Psychology, Abingdon, v. 23, n. 6, p.741-757, 2010. https://doi.org/10.1080/09515089.2010.529044. DOI: https://doi.org/10.1080/09515089.2010.529044

ROCKWELL, T. Neither Brain nor Ghost: A Non-Dualist Alternative to the Mind-Brain Identity Theory. Bradford Books: MIT press, 2005. https://doi.org/10.7551/mitpress/4910.001.0001. DOI: https://doi.org/10.7551/mitpress/4910.001.0001

SHULMAN, C. Omohundro’s Basic AI Drives and Catastrophic Risks. San Francisco, CA: The Singularity Institute, 2010. Disponível em: https://intelligence.org/files/BasicAIDrives.pdf. Acesso em: 05 Mar. 2020.

SMOLENSKY, P. On the proper treatment of connectionism. Behavioral and Brain Sciences, Cambridge, GB, v. 11, n. 1, p. 1-23, Mar. 1988. https://doi.org/10.1017/S0140525X00052432. DOI: https://doi.org/10.1017/S0140525X00052432

SOARES, N.; FALLENSTEIN, B. Aligning Superintelligence with Human Interests: A Technical Research Agenda. Technical Report. Berkeley, CA: Machine Intelligence Research Institute, 2014.

SOARES, N.; FALLENSTEIN, B.; YUDKOWSKY, E.; ARMSTRONG, S. Corrigibility. In: AAAI WORKSHOPS: WORKSHOPS AT THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, January 25–26, 2015 Austin, TX., Proceedinds […]. ed. T. Walsh. Palo Alto, CA: AAAI Press, 2015. p. 1-10. (AAAI Technical Report WS-15-02).

SOARES, N. Value Learning Problem. In: ETHICS FOR ARTIFICIAL INTELLIGENCE WORKSHOP AT 25TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-2016). New York, NY, USA 9-15 July, 2016. Proceedings […]. [S. l.: s. n.], 2016.

SOTALA, K. From mostly harmless to civilization-threatening: Pathways to dangerous artificial intelligences. In: ECAP10: VIII EUROPEAN CONFERENCE ON COMPUTING AND PHILOSOPHY, 2010, Munich. Proceedings […]. edited by Klaus Mainzer. [S. l.: s. n.], 2010.

SOTALA, K. Disjunctive scenarios of catastrophic AI risk. YAMPOLSKIY, R. V. (ed.). Artificial Intelligence Safety and Security. [S. l.]: Taylor & Francis, 2018. p. 315-337. DOI: https://doi.org/10.1201/9781351251389-22

SOTALA, K.; YAMPOLSKIY, R. V. Responses to Catastrophic AGI Risk: A Survey. Technical report. Berkeley, CA: Machine Intelligence Research Institute, 2013. https://doi.org/10.1088/0031-8949/90/1/018001. DOI: https://doi.org/10.1088/0031-8949/90/1/018001

THAGARD, P. Conceptual revolutions. Princeton: Princeton University Press, 1992. https://doi.org/10.1515/9780691186672. DOI: https://doi.org/10.1515/9780691186672

TVERSKY, A.; Science, New York, v. 211, n. 30, p. 453-458, 30 Jan. 1981. https://doi.org/10.1126/science.7455683. DOI: https://doi.org/10.1126/science.7455683

VAN GELDER, T. The dynamical hypothesis is cognitive science. Behavioral and Brain Sciences. Cambridge, v. 21, n. 5, p. 615- 628, Oct. 1998. https://doi.org/10.1017/S0140525X98001733. DOI: https://doi.org/10.1017/S0140525X98001733

VAN GELDER, T.; PORT, R. It’s about time: An overview of the dynamical approach to cognition. Mind as motion: Explorations in the dynamics of cognition. Cambridge, MA: MIT, 1998.

VON NEUMANN, J.; MORGENSTERN, O. Theory of games and economic behavior. Princeton, NJ: Princeton University Press, 1953.

YUDKOWSKY, E. Artificial Intelligence as a Positive and Negative Factor in Global Risk. In: BOSTROM, N.; ĆIRKOVIĆ, M. M. (ed.). Global Catastrophic Risks, New York: Oxford University Press, 2008. p. 308-45. Disponível em: https://intelligence.org/files/AIPosNegFactor.pdf. Acesso em: 02 Mar 2020. DOI: https://doi.org/10.1093/oso/9780198570509.003.0021

Publicado

2020-07-27

Cómo citar

Corrêa, N. K., & de Oliveira, N. (2020). Modelos dinámicos aplicados al aprendizaje de valores en inteligencia artificial. Veritas (Porto Alegre), 65(2), e37439. https://doi.org/10.15448/1984-6746.2020.2.37439

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