Abstract
When Kurt Gödel layed the foundations of theoretical computer science in 1931, he also introduced essential concepts of the theory of Artificial Intelligence (AI). Although much of subsequent AI research has focused on heuristics, which still play a major role in many practical AI applications, in the new millennium AI theory has finally become a full-fledged formal science, with important optimality results for embodied agents living in unknown environments, obtained through a combination of theory à la Gödel and probability theory. Here we look back at important milestones of AI history, mention essential recent results, and speculate about what we may expect from the next 25 years, emphasizing the significance of the ongoing dramatic hardware speedups, and discussing Gödel-inspired, self-referential, self-improving universal problem solvers.
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References
Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, Oxford (1995)
Brooks, R.A.: Intelligence without reason. In: Proceedings of the Twelveth Internationl Joint Conference on Artificial Intelligence, pp. 569–595 (1991)
Dickmanns, E.D., Behringer, R., Dickmanns, D., Hildebrandt, T., Maurer, M., Thomanek, F., Schiehlen, J.: The seeing passenger car ’VaMoRs-P’. In: Proc. Int. Symp. on Intelligent Vehicles 1994, Paris, pp. 68–73 (1994)
Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(2), 137–172 (1999)
Gödel, K.: Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I. Monatshefte für Mathematik und Physik 38, 173–198 (1931)
Gomez, F., Schmidhuber, J., Miikkulainen, R.: Efficient non-linear control through neuroevolution. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, Springer, Heidelberg (2006)
Gomez, F.J., Miikkulainen, R.: Active guidance for a finless rocket using neuroevolution. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, Springer, Heidelberg (2003)
Graves, A., Fernandez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural nets. In: ICML 2006. Proceedings of the International Conference on Machine Learning (2006)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Hutter, M.: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer, Berlin (2004) (On J. Schmidhuber’s SNF grant 20-61847)
Kohonen, T.: Self-Organization and Associative Memory, 2nd edn. Springer, Heidelberg (1988)
Kolmogorov, A.N.: Grundbegriffe der Wahrscheinlichkeitsrechnung. Springer, Berlin (1933)
Kolmogorov, A.N.: Three approaches to the quantitative definition of information. Problems of Information Transmission 1, 1–11 (1965)
Levin, L.A.: Universal sequential search problems. Problems of Information Transmission 9(3), 265–266 (1973)
Lohmeier, S., Loeffler, K., Gienger, M., Ulbrich, H., Pfeiffer, F.: Sensor system and trajectory control of a biped robot. In: AMC 2004. Proc. 8th IEEE International Workshop on Advanced Motion Control, Kawasaki, Japan, pp. 393–398. IEEE Computer Society Press, Los Alamitos (2004)
Minsky, M., Papert, S.: Perceptrons. MIT Press, Cambridge, MA (1969)
Nilsson, N.J.: Principles of artificial intelligence. Morgan Kaufmann, San Francisco (1980)
Pearlmutter, B.A.: Gradient calculations for dynamic recurrent neural networks: A survey. IEEE Transactions on Neural Networks 6(5), 1212–1228 (1995)
Pfeifer, R., Scheier, C.: Understanding Intelligence. MIT Press, Cambridge (2001)
Popper, K.R.: All Life Is Problem Solving. Routledge, London (1999)
Rechenberg, I.: Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Dissertation, 1971. Fromman-Holzboog (1973)
Rissanen, J.: Modeling by shortest data description. Automatica 14, 465–471 (1978)
Rosenbloom, P.S., Laird, J.E., Newell, A.: The SOAR Papers. MIT Press, Cambridge (1993)
Schmidhuber, J.: Curious model-building control systems. In: Proceedings of the International Joint Conference on Neural Networks, Singapore, vol. 2, pp. 1458–1463. IEEE press, Los Alamitos (1991)
Schmidhuber, J.: The Speed Prior: a new simplicity measure yielding near-optimal computable predictions. In: Kivinen, J., Sloan, R.H. (eds.) COLT 2002. LNCS (LNAI), vol. 2375, pp. 216–228. Springer, Heidelberg (2002)
Schmidhuber, J.: Artificial Intelligence - history highlights and outlook: AI maturing and becoming a real formal science (2006), http://www.idsia.ch/~juergen/ai.html
Schmidhuber, J.: Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science 18(2), 173–187 (2006)
Schmidhuber, J.: Gödel machines: fully self-referential optimal universal problem solvers. In: Goertzel, B., Pennachin, C. (eds.) Artificial General Intelligence, pp. 201–228. Springer, Heidelberg (2006)
Schmidhuber, J.: Is history converging? Again? (2006), http://www.idsia.ch/~juergen/history.html
Schmidhuber, J.: New millennium AI and the convergence of history. In: Duch, W., Mandziuk, J. (eds.) Challenges to Computational Intelligence, Springer, Heidelberg (in press, 2006) Also available as TR IDSIA-04-03, cs.AI/0302012
Schmidhuber, J., Wierstra, D., Gagliolo, M., Gomez, F.: Training recurrent networks by EVOLINO. Neural Computation 19(3), 757–779 (2007)
Shannon, C.E.: A mathematical theory of communication (parts I and II). Bell System Technical Journal XXVII, 379–423 (1948)
Smil, V.: Detonator of the population explosion. Nature 400, 415 (1999)
Solomonoff, R.J.: Complexity-based induction systems. IEEE Transactions on Information Theory IT-24(5), 422–432 (1978)
Sutton, R., Barto, A.: Reinforcement learning: An introduction. MIT Press, Cambridge, MA (1998)
Turing, A.M.: On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, Series 2 41, 230–267 (1936)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vinge, V.: The coming technological singularity. In: VISION-21 Symposium sponsored by NASA Lewis Research Center, and Whole Earth Review, Winter issue (1993)
Werbos, P.J.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis, Harvard University (1974)
Yao, X.: A review of evolutionary artificial neural networks. International Journal of Intelligent Systems 4, 203–222 (1993)
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Schmidhuber, J. (2007). 2006: Celebrating 75 Years of AI - History and Outlook: The Next 25 Years. In: Lungarella, M., Iida, F., Bongard, J., Pfeifer, R. (eds) 50 Years of Artificial Intelligence. Lecture Notes in Computer Science(), vol 4850. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77296-5_4
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DOI: https://doi.org/10.1007/978-3-540-77296-5_4
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