ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS THE LEGACY OF ALAN TURING AND JOHN VON NEUMANN
DOI:
https://doi.org/10.47839/ijc.5.3.405Keywords:
Artificial intelligence, neural networksAbstract
The work of Alan Turing and John von Neumann on machine intelligence and artificial automata is reviewed. Turing's proposal to create a child machine with the ability to learn is discussed. Von Neumann had doubts that with teacher based learning it will be possible to create artificial intelligence. He concentrated his research on the issue of complication, probabilistic logic, and self-reproducing automata. The problem of creating artificial intelligence is far from being solved. In the last sections of the paper I review the state of the art in probabilistic logic, complexity research, and transfer learning. These topics have been identified as essential components of artificial intelligence by Turing and von Neumann.References
D. Angluin. Computational learning theory: Survey and selected biography. In Proceedings of the 24th ACM Symposium on the Theory of Computing, pp. 351-369, New York, 1992. ACM Press.
S. Fahlman and C. Lebiere. The cascade-correlation learning algorithm. In D.S. Touretzky, editor, Advances in Neural Information Processingg, volume 2, San Mateo, Morgan Kaufman. 1990. pp. 524-532,
E.T. Jaynes. Information theory and statistical mechanics. Phys. Rev, 6. 1957. pp. 620-643
Th. K. Landauer. Estimates of the quantity of learned information in long-term memory. Cognitive Science, Vol. 10. 1986. pp. 477-493.
Ming Li and P. Vitanyi. An Introduction to Kolmogorov. Complexity and its Application. Springer, Heidelberg. 2002.
J. McCarthy. Programs with common sense. In Mechanisation of Thought Processes. Her Majesty's Stationery Office, London. 1959. pp. 75-84.
J. McCarthy. From here to human-level intelligence. Proceedings 5th Conference on Knowledge Representation and Reasoning. Morgan Kaufmann, San Mateo, 1996. pp. 640-646.
John McCarthy. The well-designed child. Technical report. Stanford University. 1999.
W.S. McCulloch and W. Pitts. A logical calculus of the ideas immanent un nervous activity. Bull. of Mathematical Biophysics. Vol. 5. 1943. pp 115-137.
H. Moravec. When will computer hardware match the human brain? Journal of Evolution and Technology. Vol. 1. 1948. pp. 1-14.
H. Muhlenbein. Evolution in time and space - the parallel genetic algorithm. G. Rawlins, editor, Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo. 1991. pp. 316-337.
H. Muhlenbein. Towards a theory of organisms and evolving automata. A. Menon, editor, Frontiers of Evolutionary Computation. Kluwer Academic Publishers, Boston, 2004. pp. 1-36.
H. Muhlenbein and R. Hons. The estimation of distributions and the minimum relative entropy principle. Evolutionary Computation. Vol. 13(1). 2005. pp. 1-27.
H. Muhlenbein. and J. Kindermann. The dynamics of evolution and learning - towards genetic neural networks. R. Pfeiffer, editor, Connectionism in Perspectives. North-Holland. 1989. pp. 173-198.
J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman, San Mateo. 1988.
T. R. Shultz and F. Rivest. Knowledge-based cascade-correlation: Using knowledge to speed learning. Connection Science. Vol. 13. 2002. pp. 1-30.
A. M. Turing. Computing machinery and intelligence. Mind. Vol. 59. 1950. pp. 433-460.
A. M. Turing. Intelligent machinery. B. Meltzer and D. Michie, editors, Machine Intelligence 6. Oxford University Press, Oxford, 1969. pp. 3-23.
A. M. Uttley. Conditional probability computing in a nervous system. Mechanisation of Thought Processes. Her Majesty's Stationery Office, London. 1959. pp. 119-152.
L. G. Valiant. A theory of the learnable. C. ACM, 27. 1984. pp. 1134-1142.
N. K. Vereshchagin and P. Vitanyi. Kolmogogorov's structure function and model selection. IEEE Transactions on Information Theory, 50. 2004. pp. 3265-3290.
J. von Neumann. The general and logical theory of automata. The world of mathematics. Simon and Schuster, New York, 1954. pp. 2070 -2101.
J. von Neumann. Probabilistic logics and the synthesis of reliable organs from unreliable components. Annals of Mathematics Studies 34. Princeton University Press, 1956. pp. 43--99.
J. von Neumann. Theory of Self-Reproducing Automata. University of Illinois Press, Urbana, 1966.
Byoung-Tak Zhang and H. Muhlenbein. Balancing accuracy and parsimony in genetic programming. Evolutionary Computation, 3. 1995. pp. 17-38.
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