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An Introduction to Artificial Intelligence

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Artificial Intelligence

Abstract

While computers are excellent at performing computationally intensive tasks, the ability to match human intelligence and intuition with computer algorithms has always been an aspirational goal. Nevertheless, significant progress has been made on algorithms that can perform predictive tasks that would have been considered unimaginable a few decades back.

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Notes

  1. 1.

    This is not the case for some deductive methods. An example is fuzzy logic.

  2. 2.

    The Michaelson-Morley experiments on the speed of light played a key role as observations that could not be explained by Newtonian physics.

  3. 3.

    https://mathworld.wolfram.com/TowerofHanoi.html

  4. 4.

    It can also play Go and Shogi.

References

  1. C. Aggarwal. Recommender systems: The textbook. Springer, 2016.

    Book  Google Scholar 

  2. C. Aggarwal. Neural networks and deep learning: A textbook. Springer, 2018.

    Book  MATH  Google Scholar 

  3. C. Aggarwal. Machine learning for text. Springer, 2018.

    Book  MATH  Google Scholar 

  4. C. Aggarwal. Linear algebra and optimization for machine learning: A textbook, Springer, 2020.

    Book  MATH  Google Scholar 

  5. C. M. Bishop. Pattern recognition and machine learning. Springer, 2007.

    MATH  Google Scholar 

  6. C. M. Bishop. Neural networks for pattern recognition. Oxford University Press, 1995.

    Book  MATH  Google Scholar 

  7. A. Bryson. A gradient method for optimizing multi-stage allocation processes. Harvard University Symposium on Digital Computers and their Applications, 1961.

    Google Scholar 

  8. M. Campbell, A. J. Hoane Jr., and F. H. Hsu. Deep blue. Artificial Intelligence, 134(1–2), pp. 57–83, 2002.

    Article  MATH  Google Scholar 

  9. W. Clocksin and C. Mellish. Programming in Prolog: Using the ISO standard. Springer, 2012.

    MATH  Google Scholar 

  10. C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3), pp. 273–297, 1995.

    Article  MATH  Google Scholar 

  11. M. Deisenroth, A. Faisal, and C. Ong. Mathematics for Machine Learning, Cambridge University Press, 2019.

    MATH  Google Scholar 

  12. M. Garey and D. Johnson. Computers and Intractability, Freeman, 2002.

    Google Scholar 

  13. I. Gent, C. Jefferson, and P. Nightingale. Complexity of n-queens completion. Journal of Artificial Intelligence Research, 59, 815–848, 2017.

    Article  MathSciNet  MATH  Google Scholar 

  14. I. Goodfellow, Y. Bengio, and A. Courville. Deep learning. MIT Press, 2016.

    MATH  Google Scholar 

  15. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning. Springer, 2009.

    Book  MATH  Google Scholar 

  16. K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. IEEE International Conference on Computer Vision, pp. 1026–1034, 2015.

    Google Scholar 

  17. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778, 2016.

    Google Scholar 

  18. R. High. The era of cognitive systems: An inside look at IBM Watson and how it works. IBM Corporation, Redbooks, 2012.

    Google Scholar 

  19. G. Hinton. Connectionist learning procedures. Artificial Intelligence, 40(1–3), pp. 185–234, 1989.

    Article  Google Scholar 

  20. J. Holland. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, 1992.

    Book  Google Scholar 

  21. T. Kohonen. The self-organizing map. Neurocomputing, 21(1), pp. 1–6, 1998.

    Article  MathSciNet  MATH  Google Scholar 

  22. J. Koza. Genetic programming. MIT Press, 1994.

    MATH  Google Scholar 

  23. A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. NIPS Conference, pp. 1097–1105. 2012.

    Google Scholar 

  24. D. Lenat. CYC: A large-scale investment in knowledge infrastructure. Communications of the ACM, 38(11), pp. 33–38, 1995.

    Article  Google Scholar 

  25. P. McCullagh and J. Nelder. Generalized linear models CRC Press, 1989.

    Google Scholar 

  26. M. Minsky and S. Papert. Perceptrons. An Introduction to Computational Geometry, MIT Press, 1969.

    MATH  Google Scholar 

  27. A. Newell, J. Shaw, and H. Simon. Report on a general problem solving program. IFIP Congress, 256, pp. 64, 1959.

    Google Scholar 

  28. P. Norvig. Paradigms in Artificial Intelligence Programming: Case Studies in Common LISP, Morgan Kaufmann, 1881.

    Google Scholar 

  29. F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386, 1958.

    Google Scholar 

  30. D. Rumelhart, G. Hinton, and R. Williams. Learning internal representations by back-propagating errors. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 318–362, 1986.

    Google Scholar 

  31. S. Russell, and P. Norvig. Artificial intelligence: a modern approach. Pearson Education Limited, 2011.

    Google Scholar 

  32. A. Samuel. Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3, pp. 210–229, 1959.

    Article  MathSciNet  Google Scholar 

  33. E. Shortliffe. Computer-based medical consultations: MYCIN. Elsevier, 2002.

    Google Scholar 

  34. P. Seibel. Practical common LISP. Apress, 2006.

    Google Scholar 

  35. D. Silver et al. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv, 2017. https://arxiv.org/abs/1712.01815

  36. G. Strang. Linear algebra and learning from data. Wellesley-Cambridge Press, 2019.

    MATH  Google Scholar 

  37. P. Werbos. The roots of backpropagation: from ordered derivatives to neural networks and political forecasting (Vol. 1). John Wiley and Sons, 1994.

    Google Scholar 

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Aggarwal, C.C. (2021). An Introduction to Artificial Intelligence. In: Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-72357-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-72357-6_1

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