Al fin y al cabo, la inteligencia artificial no es inteligente: en la búsqueda de una definición neurocientífica comprensible de la inteligencia

Sthéfano Divino | Biografía
Centro Universitário de Lavras - Unilavras

Resumen

Este trabajo explora una serie de reflexiones sobre el significado de la inteligencia en la neurociencia y la informática. El objetivo de este trabajo es presentar una definición comprensible que se ajuste a nuestro entorno contemporáneo de inteligencia artificial. Se analiza la relación entre la inteligencia y la neurociencia y presento la teoría de los mil cerebros de Hawkins, un enfoque para mostrar qué es un agente inteligente según la neurociencia. Aquí, el principal resultado se basa en la comprobación de que la inteligencia sólo es posible en el neocórtex. De acuerdo con este resultado, el estudio hace un segundo análisis crítico con el objetivo de demostrar por qué no existe la inteligencia artificial en la actualidad. La metodología de investigación de este ensayo se basa en las teorías existentes sobre la inteligencia artificial, centradas en la informática y la neurociencia.

Referencias

  1. Atkinson, J. (2021, July 26th). Tesla’s Autopilot Misunderstood the Moon For A Yellow Traffic Light. Video: Automatic. Swords Today. https://swordstoday.ie/teslas-autopilot-misunderstood-the-moon-for-a-yellowtraffic-light-video-automatic/
  2. Barton, R. A. (1996). Neocortex size and behavioural ecology in primates. Proceedings of the Royal Society of London, 263(1367), 173-177.
  3. Beer, J. M., Fisk, A. D. & Rogers, W. A. (2014). Toward a framework for levels of robot autonomy in human-robot interaction. Journal of human-robot interaction, 3(2), 74-99. https://doi.org/10.5898%2FJHRI.3.2.Beer
  4. Binder, J. R. (2017). Current controversies on Wernicke’s area and its role in language. Current neurology and neuroscience reports, 17(8), 1-10.
  5. Bostrom, N. & Yudkowsky, E. (2011). The ethics of artificial intelligence. In K. Frankish & W. M. Ramsey (eds.), The Cambridge Handbook of Artificial Intelligence (316-334). Cambridge University Press.
  6. Bostrom, N. (2014). Superintelligence. Oxford University Press.
  7. Brayne, S. (2020). Predict and surveil: Data, discretion, and the future of policing. Oxford University Press.
  8. Byrne, R. W., & Corp, N. (2004). Neocortex size predicts deception rate in primates. Proceedings of the Royal Society of London B, 271(1549), 1693-1699.
  9. Crawford, K. (2021). The Atlas of AI. Yale University Press.
  10. Crick, F. H. (1979). Thinking about the brain. Scientific American, 241(3), 219-233.
  11. Davis, R., Shrobe, H. & Szolovits, P. (1993). What is a knowledge representation? AI magazine, 14(1), 17-33. https://doi.org/10.1609/aimag.v14i1.1029
  12. Deaner, R. O., Isler, K., Burkart, J. & Van Schaik, C. (2007). Overall brain size, and not encephalization quotient, best predicts cognitive ability across non-human primates. Brain, behavior and evolution, 70(2), 115-124.
  13. Edelman, G. M. & Mountcastle, V. B. (1978). The mindful brain: cortical organization and the group-selective theory of higher brain function. MIT Press.
  14. Etard, O., Mellet, E., Papathanassiou, D., Benali, K., Houdé, O., Mazoyer, B. & Tzourio-Mazoyer, N. (2000). Picture naming without Broca’s and Wernicke’s area. Neuroreport, 11(3), 617-622.
  15. Felleman, D. J. & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral cortex, 1(1), 1-47. https://doi.org/10.1093/cercor/1.1.1-a
  16. Franklin, S. & Graesser, A. (1997). Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents. In J. P. Müller, M. J. Wooldridge & N. R. Jennings (eds), Intelligent Agents III Agent Theories, Architectures, and Languages (pp. 21-35). Springer. https://doi.org/10.1007/BFb0013570
  17. Gharbi, R. B., Elsharkawy, A. M. & Karkoub, M. (1999). Universal neural-network-based model for estimating the PVT properties of crude oil systems. Energy & fuels, 13(2), 454-458. https://doi.org/10.1021/ef980143v
  18. Haggard, P. (2017). Sense of agency in the human brain. Nature Reviews Neuroscience, 18(4), 196-207.
  19. Hawkins, J. & Ahmad, S. (2016). Why neurons have thousands of synapses, a theory of sequence memory in neocortex. Frontiers in Neural Circuits, 10. https://doi.org/10.3389/fncir.2016.00023
  20. Hawkins, J. (2021). A thousand brains: A new theory of intelligence. Basic Books.
  21. Hinton, G. F. (1981). A parallel computation that assigns canonical object-based frames of reference. In A. Drinan (ed.), Proceedings of the 7th international joint conference on Artificial intelligence-Volume 2 (pp. 683-685). Morgan Kaufmann Publishers Inc.
  22. Huang, H. M., Pavek, K., Ragon, M., Jones, J., Messina, E. & Albus, J. (2007). Characterizing unmanned system autonomy: Contextual autonomous capability and level of autonomy analyses. In G. R. Gerhart, D. W. Gage & C. M. Shoemaker (eds.), Unmanned Systems Technology IX. Proceedings Volume 6561. Defense and Security Symposium | 9-13 April 2007. International Society for Optics and Photonics.
  23. Gil, V., Nocentini, S. & Del Río, J. A. (2014). Historical first descriptions of Cajal–Retzius cells: from pioneer studies to current knowledge. Frontiers in neuroanatomy, 8, 32, 1-9.
  24. Kim, D., & Thayer, S. A. (2001). Cannabinoids inhibit the formation of new synapses between hippocampal neurons in culture. Journal of Neuroscience, 21(10), RC146. https://doi.org/10.1523/JNEUROSCI.21-10-j0004.2001
  25. Konflanz, D. M. (2019). Investigating hierarchical temporal memory networks applied to dynamic branch prediction [undergraduate thesis, Universidade Federal da Fronteira Sul]. Digital Repository. https://rd.uffs.edu.br/handle/prefix/3374
  26. Li, W., Ma, L., Yang, G. & Gan, W. B. (2017). REM sleep selectively prunes and maintains new synapses in development and learning. Nature neuroscience, 20(3), 427-437.
  27. Lindenfors, P. (2005). Neocortex evolution in primates: the ‘social brain’ is for females. Biology letters, 1(4), 407-410.
  28. Luck, M. & d’Inverno, M. (1995, June). A Formal Framework for Agency and Autonomy. In L. Gasser & V. Lesser (eds.), Proceedings of the First International Conference on Multiagent Systems (pp. 254-260). MIT Press.
  29. Luck, M. & d’Inverno, M. (2001). A conceptual framework for agent definition and development. The Computer Journal, 44(1), 1-20.
  30. Markman, A. B. (2013). Knowledge representation. Psychology Press.
  31. Michaud, A. (2016). Intelligence and Early Mastery of the Reading Skill. Journal of Biometrics & Biostatistics, 7(4). https://doi.org/10.4172/2155-6180.1000327
  32. Naeser, M. A., Helm-Estabrooks, N., Haas, G., Auerbach, S. & Srinivasan, M. (1987). Relationship between lesion extent in Wernicke’s area’ on computed tomographic scan and predicting recovery of comprehension in Wernicke’s aphasia. Archives of Neurology, 44(1), 73-82.
  33. Ogawa, M., Miyata, T., Nakajima, K., Yagyu, K., Seike, M., Ikenaka, K., Yamamoto, H. & Mikoshiba, K. (1995). The reeler gene-associated antigen on Cajal-Retzius neurons is a crucial molecule for laminar organization of cortical neurons. Neuron, 14(5), 899-912. https://doi.org/10.1016/0896-6273(95)90329-1
  34. Qiu, Y., Garg, D., Zhou, L., Kharangate, C. R., Kim, S. M. & Mudawar, I. (2020). An artificial neural network model to predict mini/micro-channels saturated flow boiling heat transfer coefficient based on universal consolidated data. International Journal of Heat and Mass Transfer, 149. https://doi.org/10.1016/j.ijheatmasstransfer.2019.119211
  35. Ramón Y Cajal, S. (1923). Recuerdos de mi vida. Imprenta de Juan Pueyo.
  36. Russell, S. J. & Norvig, P. (2010). Artificial Intelligence-A Modern Approach (3. internat. ed.) Pearson Education.
  37. Shrestha, Y. R., Ben-Menahem, S. M. & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83.
  38. Sun, Q. & Ertekin, T. (2015, April 27th-30th). The development of artificial-neural-network-based universal proxies to study steam assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS) processes [paper presented In Society of Petroleum Engineers —SPE— Western Regional Meeting]. Garden Grove, California, USA. https://doi.org/10.2118/174074-MS
  39. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: an introduction. The MIT Press.
  40. Tehovnik, E. J. & Slocum, W. M. (2004). Behavioural state affects saccades elicited electrically from neocortex. Neuroscience & Biobehavioral Reviews, 28(1), 13-25. https://doi.org/10.1016/j.neubiorev.2003.10.001
  41. Yokoi, A. & Diedrichsen, J. (2019). Neural organization of hierarchical motor sequence representations in the human neocortex. Neuron, 103(6), 1178-1190. https://doi.org/10.1016/j.neuron.2019.06.017
  42. Zemel, R. S., Mozer, M. C. & Hinton, G. E. (1989). TRAFFIC: Recognizing objects using hierarchical reference frame transformations. In D. S. Touretzky (ed.), Advances in neural information processing systems (pp. 266-273). Morgan Kaufmann Publishers Inc. https://dl.acm.org/doi/10.5555/2969830.2969863
Cómo citar
Divino, S. (2022). Al fin y al cabo, la inteligencia artificial no es inteligente: en la búsqueda de una definición neurocientífica comprensible de la inteligencia. Opinión Jurídica, 21(46), 1-21. https://doi.org/10.22395/ojum.v21n46a9

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