Top

Published in:

2012 | OriginalPaper | Chapter

3. The Threat of a Reward-Driven Adversarial Artificial General Intelligence

Author : Itamar Arel

Published in: Singularity Hypotheses

Publisher: Springer Berlin Heidelberg

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Once introduced, Artificial General Intelligence (AGI) will undoubtedly become humanity’s most transformative technological force. However, the nature of such a force is unclear with many contemplating scenarios in which this novel form of intelligence will find humans an inevitable adversary. In this chapter, we argue that if one is to consider reinforcement learning principles as foundations for AGI, then an adversarial relationship with humans is in fact inevitable. We further conjecture that deep learning architectures for perception in concern with reinforcement learning for decision making pave a possible path for future AGI technology and raise the primary ethical and societal questions to be addressed if humanity is to evade catastrophic clashing with these AGI beings.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Intelligence Explosion: Evidence and Import

next chapter New Millennium AI and the Convergence of History: Update of 2012

Available only for authorised users

Arel, D. R., & Coop, R. (November, 2009). DeSTIN: A scalable deep learning architecture with application to high-dimensional robust pattern recognition. In Proceedings of the AAAI 2009 Fall Symposium on Biologically Inspired Cognitive Architectures.

Arel, I. (2013). The threat of a reward-driven adversarial artificial general intelligence. In A. H. Eden, J. H. Moor, J. H. Søraker, & E. Steinhart (Eds.), The singularity hypothesis: a scientific and philosophical analysis (pp. 43–58). Heidelberg: Springer.

Arel, I., Rose, D., & Karnowski, T. (2010). Deep machine learning—a new frontier in artificial intelligence research. IEEE Computational Intelligence Magazine, 14, 12–18.

Baillargeon, R. (1994). Physical Reasoning in young infants: seeking explanations for impossible events. British Journal of Developmental Psychology, 12, 9–33.CrossRef

Bellman, R. (1957). Dynamic programming. Princeton: Princeton University Press.MATH

Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 1–127.MathSciNetMATHCrossRef

Dietrich, E. (2001, October). Homo sapiens 2.0: why we should build the better robots of our nature. Journal of Experimental and Theoretical Artificial Intelligence, 13(4), 323–328.

Dietrich, E. (2007). After the humans are gone. Journal of Experimental and Theoretical Artificial Intelligence, 19(1), 55–67.MathSciNetCrossRef

Duda, R., Hart, P., & Stork, D. (2000). Pattern recognition (2nd edn ed.). New York: Wiley-Interscience.

Ginsburg, G. S., & Bronstein, P. (1993). Family factors related to children’s intrinsic/extrinsic motivational orientation and academic performance. Child Development, 64, 1461–1474.CrossRef

Gray, P., Hurst, P., Lewis, S., & Meyer, R. (2001). Analysis and design of analog integrated circuits. New York: Wiley.

Hasler, P., & Dugger, J. (2005). An analog floating-gate node for supervised learning. IEEE Transactions on Circuits and Systems I, 52(5), 834–845.CrossRef

Hasler, P., Diorio, C., Minch, B. A., & Mead, C. (1995). Single transistor learning synapse with long term storage. IEEE International Symposium on Circuits and Systems, 1660–1663.

Joel, D., Niv, Y., & Ruppin, E. (2002). Actor-critic models of the basal ganglia: new anatomical and computational perspectives. Neural Networks, 15, 535–547.CrossRef

Kelley, S. A., Brownell, C. A., & Campbell, S. B. (2000). Mastery motivation and self-evaluative affect in toddlers: longitudinal relations with maternal behavior. Child Development, 71, 1061–71.CrossRef

Lee, T. (2003). Hierarchical bayesian inference in the visual cortex. Journal of the Optical Society of America, 20(7), 1434–1448.CrossRef

Lee, T., Mumford, D., Romero, R., & Lamme, V. (1998). The role of the primary visual cortex in higher level vision. Vision Research, 38, 2429–2454.CrossRef

Millner, A. D., & Goodale, M. A. (1996). The visual brain in action. Oxford: Oxford University Press.

Mishkin, M., Ungerkeuder, L. G., & Macko, K. A. (1983). Object vision and spatial vision: two cortical pathways. Trends in Neuroscience, 6, 414–417.CrossRef

Rapaport, W. J. (2000). How to pass a turing test: syntactic semantics, natural—language understanding, and first-person cognition. Journal of Logic, Language, and Information, 9(4): 467–490. (Reprinted in The turing test: the elusive standard of artificial intelligence, pp. 161–14, by H. M. James, Ed., 2003, Dordrecht: Kluwer).

Rapaport, W. J. (2003). What did you mean by that? Misunderstanding, negotiation, and syntactic semantics. Minds and Machines, 13(3), 397–427.CrossRef

Schultz, W. (1998). Predictive reward signal of dopamine neurons. The Journal of Neurophysiology, 80(1), 1–27.

Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3, 417–457.CrossRef

Searle, J. R. (1982). The myth of the computer. New York Review of Books (29 April 1982): 3–6; cf. correspondence, same journal (24 June 1982): 56–57.

Suri, R. E., & Schultz, W. (1999). A neural network model with dopamine-like reinforcement signal that learns a spatial delayed response task. Neuroscience, 91(3), 871–890.CrossRef

Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.

Wallis, G., & Bülthoff, H. (1999). Learning to recognize objects. Trends in Cognitive Sciences, 3(1), 23–31.CrossRef

Wallis, G., & Rolls, E. (1997). Invariant face and object recognition in the visual system. Progress in Neurobiology, 51, 167–194.CrossRef

Winston, P. H. (1975). Learning structural descriptions from examples. in Patrick HenryWinston (ed.), The Psychology of Computer Vision (New York: McGraw-Hill): 157– 209. (Reprinted in Readings in knowledge representation, pp. 141–168, by J. B. Ronald & J. L. Hector, Eds., 1985, Los Altos, CA: Morgan Kaufmann).

Title: The Threat of a Reward-Driven Adversarial Artificial General Intelligence
Author: Itamar Arel
Publisher: Springer Berlin Heidelberg
Book: Singularity Hypotheses
Print ISBN: 978-3-642-32559-5

Electronic ISBN: 978-3-642-32560-1

Copyright Year: 2012
DOI: https://doi.org/10.1007/978-3-642-32560-1_3

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner