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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 2/2011

01-06-2011 | Original Article

From foundational issues in artificial intelligence to intelligent memristive nano-devices

Authors: Alfons Schuster, Yoko Yamaguchi

Published in: International Journal of Machine Learning and Cybernetics | Issue 2/2011

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Abstract

Since its conception in the mid 1950s, artificial intelligence has always been a hugely exciting, cross-disciplinary research endeavor. As the technology-driven modern world creates artifacts demonstrating increasing levels of sophistication and, perhaps, intelligence, it is necessary for practitioners in several fields to be familiar, to some degree, with some of the fundamental problems and challenges in the field of artificial intelligence. The goal in this paper is to highlight some of the foundational issues in the field of artificial intelligence and to reflect on the role of artificial intelligence in the context of a rapidly advancing modern world where breakthroughs such as memristive nano-devices enthuse practitioners beyond the realms of science fiction.
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Literature
1.
Adleman L (1994) Molecular computation of solutions to combinatorial problems. Science 266:1021–1024CrossRef
2.
Albus JS, Bekey GA, Holland JH, Kanwisher NG, Krichmar JL, Mishkin M, Modha DS, Raichle ME, Shepherd GM, Tononi G (2007) A proposal for a decade of the mind initiative. Science 317(5843):1321
3.
Anderson M, Anderson L (2010) Robot be good: a call for ethical autonomous machines. Sci Am October:15–24
4.
Aono M, Hara M, Aihara K, Munakata T (2010) Amoeba-based emergent computing: combinatorial optimization and autonomous meta-problem solving. Int J Unconv Comput 6:89–108
5.
Baruchi I, Ben-Jacob E (2007) Towards neuro-memory-chip: imprinting multiple memories in cultured neural networks. Phys Rev E 75. Article ID 050901
6.
7.
Brighton H, Selina H (2003) Introducing artificial intelligence. Icon Books, London
8.
9.
Chua L (1971) Memristor—the missing circuit element. IEEE Trans Circuit Theory 18(5):507–519CrossRef
10.
11.
Cohen DI (1996) Introduction to computer theory, 2nd edn. Wiley, New York
12.
Desai S, Rolston J, Guo L, Potter S (2010) Improving impedance of implantable microwire multielectrode arrays by ultrasonic electroplating of durable plantinum black. Front Neuroeng 3(5). doi:10.​3389/​fneng.​2010.​00005
13.
Feser E (2006) Philosophy of mind: a beginner’s guide. Oneworld Publications
14.
Friedman R (2009) Reverse engineering the brain. Biomed Comput Rev 5(2):10–17
15.
Grover L (1996) A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th annual ACM symposium on the theory of computing (STOC), pp 212–219
16.
17.
Kotchetkov IS, Hwang BY, Appelboom G, Kellner CP, Connolly Jr ES (2010) Brain-computer interfaces: military, neurosurgical, and ethical perspective. Neurosurg Focus 28(5:E25) (available online)
18.
Lipton J (1995) DNA solution to hard computational problems. Science 268:542–545CrossRef
19.
Mallon E, Pratt S, Franks N (2001) Individual and collective decision-making during nest site selection by the ant Leptothorax albipennis. Behav Ecol Sociobiol 50:352–359CrossRef
20.
Mermin DN (2007) Quantum computer science: an introduction. Cambridge University Press, CambridgeMATH
21.
Negnevitsky M (2004) Artificial intelligence—a guide to intelligent systems, 2nd edn. Addison Wesley, Harlow
22.
23.
Pershin Y, La Fontaine S, Di Ventra M (2008) Memristive model of amoeba’s learning. arXiv:0810.4179v2 [q-bio.CB]
24.
Pfeifer R, Scheier C (2000) Understanding intelligence. The MIT Press, Cambridge
25.
Pironio S, Acin A, Massar S, Boyer de la Giroday A, Matsukevich D, Maunz P, Olmschenk S (2010) Random numbers certified by Bell’s theorem. Nature 464:1021–1024
26.
Schank R, Abelson R (1977) Scripts, plans, goals and understanding: inquiry into human knowledge structures. Psychology Press
27.
28.
Schuster A (2007) Intelligent computing everywhere. Springer-Verlag, LondonCrossRef
29.
30.
Schuster A (2008) Robustness in nature as a design principle for artificial intelligence. In: Schuster A (ed) Robust intelligent systems. Springer, New York, pp 165–188
31.
Schuster A, Shapcott M, Adamson K, Bell DA (2000) A multiple approach to data analysis and uncertainty management in knowledge-based systems. Int J Intell Syst 15(2):93–116
32.
Schuster A, Yamaguchi Y (2009) The survival of the fittest and the reign of the most robust—in biology and elsewhere. Minds Mach 19(3):361–389
33.
34.
Shor P (1994) Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. In: Proceedings of the 35th annual symposium on foundations of computer science, Santa Fe, NM, pp 20–22
35.
Snider G, Williams R (2007) Nano/CMOS architectures using a field-programmable nanowire interconnect. Nanotechnology 18, 035204
36.
Snider GS (2008) Cortical computing with memristive nanodevices. Sci-DAC Rev 10:58–65
37.
Stix G (2005) Best-kept secrets. Sci Am 292:65–69
38.
Strukov DB, Snider GS, Stewart DR, Williams RS (2008) The missing memristor found. Nat Biotechnol 453:80–83
39.
Tagkopoulos I, Liu Y-C, Tavazoie S (2008) Anticipatory behavior within microbial genetic networks. Science 320(5881):1313–1317
40.
Metadata
Title
From foundational issues in artificial intelligence to intelligent memristive nano-devices
Authors
Alfons Schuster
Yoko Yamaguchi
Publication date
01-06-2011
Publisher
Springer-Verlag
Published in
International Journal of Machine Learning and Cybernetics / Issue 2/2011
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-011-0016-1

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