No canal da Inteligência Artificial – Nova temporada de desgrenhados e empertigados

Autores

DOI:

https://doi.org/10.1590/s0103-4014.2021.35101.002

Palavras-chave:

Inteligência Artificial, Lógica, Representação de conhecimento, Aprendizado profundo

Resumo

O estudo de Inteligência Artificial (IA) tem sido perseguido, desde seu início, segundo dois estilos diferentes, jocosamente referidos como scruffy (desgrenhado) e neat (empertigado). Esses estilos na verdade refletem distintas visões sobre a disciplina e seus objetivos. Neste artigo revisamos a tensão entre desgrenhados e empertigados ao longo da história da IA. Analisamos o impacto do atual desempenho de métodos de aprendizado profundo nesse debate, sugerindo que o desenvolvimento de arquiteturas computacionais amplas é um caminho particularmente promissor para a IA.

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Referências

ABELSON, R. P. Constraint, construal, and cognitive science. Third Annual Conference of the Cognitive Science Society, p.1-9, 1981.

BERGER, A. L. et al. The Candide system for machine translation. Workshop on Human Language Technology, p.157-62, 1994.

BROOKS, R. Elephants don’t play chess. Robotics and Autonomous Systems, v.6, p.3-15,

CHEESEMAN. P. In defense of probability. International Joint Conference on Artificial Intelligence, p.1002-9, 1985.

DARPA. Explainable Artificial Intelligence (XAI), DARPA-BAA-16-53, 2016.

DAVIS, R. et al. What is a knowledge representation? AI Magazine, v.14, n.1, p.17-33,

DEVLIN, J. et al. BERT: Pre-Training of Deep Bidirectional Transformers for Language

Understanding. arXiV:1810.04805, 2019.

GRAVES, A. et al. Neural Turing Machines, arXiV:1410.5401, 2014.

HALEVY, A. et al. The unreasonable effectiveness of data. IEEE Intelligent Systems, p.8-12, 2009.

HASTIE, T. et al. The Elements of Statistical Learning. Springer, 2009.

HAYES, P. In defence of logic. International Joint Conference on Artificial Intelligence, p.559-65, 1977.

HUDSON, M. IA researchers allege that machine learning is alchemy. Science, 2018.

KRIZHEVSKY, A. et al. ImageNet classification with deep convolutional neural networks. Neural Information Processing Systems, p.1106-14, 2012.

MARCUS, G.; DAVIS, E. Rebooting AI – Building Artificial Intelligence We Can Trust. Pantheon, 2019.

MCCARTHY, J. Programs with common sense. Mechanisation of Thought Processes

(Volume I), p.75-84, 1959.

MCCARTHY, J. Review of “Artificial Intelligence”A General Survey”. Artificial Intelligence,

v.5, n.3, p.371-22, 1974.

MCCARTHY, J. . Generality in artificial intelligence. Communications of the ACM, p.257-67,

MCCORDUCK, P. Machines Who Think. s.l.: CRC Press, 2004.

MINSKY, M. Form and contente in computer science. Journal of the Association for

Computing Machinery, v.17, n.2, p.197-215, 1970.

MINSKY, M. The Society of Mind. s.l.: Simon & Schuster, 1986.

MITCHELL, T. Machine Learning. s. l.: McGraw Hill, 1997.

NEWELL, A. Unified Theories of Cognition. s. l.: Harvard University Press, 1990.

NEWELL, A.; SIMON, H. A. Computer Science as Empirical Enquiry: Symbols and Search. Communications of the ACM, v.19, n.3, p.113-26, 1976.

NILSSON, N. J. Artificial intelligence prepares for 2001. AI Magazine, v.4, p.7-14, 1983.

NILSSON, N. J.. The Quest for Artificial Intelligence. s. l.: Cambridge University Press, 2009.

PEARL, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. s. l.: Morgan Kauffman, 1988.

RICH, E.; KNIGHT, K. Artificial Intelligence. s. l.: McGraw Hill, 1991.

RICH, E. Artificial Intelligence. 3.ed. s. l.: McGraw Hill, 2010.

RUMELHART, D. E.; HINTON, G. E.; WILLIAMS, R. J. Learning representations by back-propagating error. Nature, v.323, n.6088, p.533-536, 1986.

RUSSELL, S.; NORVIG, P. Artificial Intelligence: A Modern Approach. s. l.: Prentice Hall, 1995.

SERGIOVANNI, T. Mystics, neats and scruffies: Informing professional practice in educational administration. Journal of Educational Administration, v.27, n.2, p.7-21, 2007.

SLOMAN, A. Must inteligente systems be scruffy? Evolving Knowledge in Natural Science and Artificial Intelligence, Pitman, 1990.

TAN, M.; QUOC Le. Efficientnet: Rethinking model scaling for convolutional neural networks. International Conference on Machine Learning, p.6105-14, 2019.

WILKS, Y. An Artificial Intelligence Approach to Machine Translation. Tech report AD0741199, Stanford University, 1972.

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Publicado

2021-04-30

Edição

Seção

Inteligência Artificial

Como Citar

Cozman, F. G. (2021). No canal da Inteligência Artificial – Nova temporada de desgrenhados e empertigados. Estudos Avançados, 35(101), 7-20. https://doi.org/10.1590/s0103-4014.2021.35101.002