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8 - Guidelines for Artificial Intelligence Containment

from Part I - Technology

Published online by Cambridge University Press:  18 October 2019

By
James Babcock ,
János Krámar  and
Roman V. Yampolskiy
Edited by
Ali E. Abbas
Ali E. Abbas
Affiliation:
University of Southern California
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Summary

The past few years have seen a remarkable amount of attention on the long-term future of artificial intelligence (AI). Icons of science and technology such as Stephen Hawking (Cellan-Jones, 2014), Elon Musk (Musk, 2014), and Bill Gates (Gates, 2015) have expressed concern that superintelligent AI may wipe out humanity in the long run. Stuart Russell, coauthor of the most-cited textbook of AI (Russell & Norvig, 2003), recently began prolifically advocating (Dafoe & Russell, 2016) for the field of AI to take this possibility seriously. AI conferences now frequently have panels and workshops on the topic. There has been an outpouring of support from many leading AI researchers for an open letter calling for greatly increased research dedicated to ensuring that increasingly capable AI remains “robust and beneficial,” and gradually a field of “AI safety” is coming into being (Pistono & Yampolskiy, 2016; Yampolskiy, 2016, 2018; Yampolskiy & Spellchecker, 2016). Why all this attention?

Type
Chapter
Information
Next-Generation Ethics
Engineering a Better Society
 , pp. 90 - 112
Publisher: Cambridge University Press
Print publication year: 2019

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References

Abboud, G., Marean, J., & Yampolskiy, R. V. (2010). “Steganography and Visual Cryptography in Computer Forensics.” Paper presented at the Systematic Approaches to Digital Forensic Engineering (SADFE), 2010 5th IEEE International Workshop.Google Scholar
Ali, N., Hindi, M., & Yampolskiy, R. V. (2011). “Evaluation of Authorship Attribution Software on a Chat Bot Corpus.” Paper presented at the 23rd International Symposium on Information, Communication and Automation Technologies (ICAT2011), Sarajevo, Bosnia and Herzegovina.CrossRefGoogle Scholar
Ali, N., Schaeffer, D., & Yampolskiy, R. V. (2012). Linguistic profiling and behavioral drift in chat bots. In Proceedings of the Midwest Artificial Intelligence and Cognitive Science Conference, 27.Google Scholar
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.Google Scholar
Armstrong, S., Bostrom, N., & Shulman, C. (2016). Racing to the precipice: a model of artificial intelligence development. AI & Society, 31(2), 201–206.CrossRefGoogle Scholar
Armstrong, S., Sandberg, A., & Bostrom, N. (2012). Thinking inside the box: Controlling and using an oracle AI. Minds and Machines, 22(4), 299–324.Google Scholar
Armstrong, S., & Sotala, K. (2015). How we’re predicting AI–or failing to. In Romportl, J., Zackova, E., & Kelemen, J. (Eds.), Beyond Artificial Intelligence (pp. 11–29). Cham, Switzerland: Springer.Google Scholar
Babcock, J., Kramar, J., & Yampolskiy, R. (2016). “The AGI Containment Problem.” Paper presented at the 9th Conference on Artificial General Intelligence (AGI 2015), NYC, USA.Google Scholar
Beck, M. B., Rouchka, E. C., & Yampolskiy, R. V. (2013). Finding data in DNA: Computer forensic investigations of living organisms. In Gladyshev, P. & Rogers, M. K., Digital forensics and cyber crime (pp. 204–219). Berlin; Heidelberg: Springer.Google Scholar
Benthall, S. (2017). Don’t fear the reaper: Refuting Bostrom’s superintelligence argument. arXiv preprint arXiv:1702.08495.Google Scholar
Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford: Oxford University Press.Google Scholar
Cellan-Jones, R. (2014). Stephen Hawking warns artificial intelligence could end mankind. Paper presented at the BBC. Retrieved from www.bbc.com/news/technology-30290540Google Scholar
Collobert, R., Kavukcuoglu, K., & Farabet, C. (2012). Implementing neural networks efficiently. In Montavon, G., Orr, G. B., & Müller, K-R (Eds.), Neural networks: tricks of the trade, (pp. 537–557). New York, NY: Springer.Google Scholar
Dafoe, A., & Russell, S. (2016). Yes, we are worried about the existential risk of artificial intelligence. Retrieved from www.technologyreview.com/s/602776/yes-we-are-worried-about-the-existential-risk-of-artificial-intelligence/.Google Scholar
Danaher, J. (2015). Why AI doomsayers are like sceptical theists and why it matters. Minds and Machines, 25(3), 231–246.Google Scholar
Dietrich, E. (2007). After the humans are gone. Journal of Experimental & Theoretical Artificial Intelligence, 19 (1), 55–67.CrossRefGoogle Scholar
Garfinkel, T., & Rosenblum, M. (2003). A virtual machine introspection based architecture for intrusion detection. Paper presented at the NDSS.Google Scholar
Garis, H. D. (2005). The artilect war. Palm Spring, CA: ETC publications.Google Scholar
Gates, B. (2015). Reddit. Retrieved from www.reddit.com/r/IAmA/comments/2tzjp7/hi_reddit_im_bill_gates_and_im_back_for_my_third/Google Scholar
Good, I. J. (1966). Speculations concerning the first ultraintelligent machine. Advances in Computers, 6(1), 31–88.CrossRefGoogle Scholar
Guri, M., Kachlon, A., Hasson, O., Kedma, G., Mirsky, Y., & Elovici, Y. (2015). GSMem: Data exfiltration from air-gapped computers over GSM frequencies. Paper presented at the 24th USENIX Security Symposium (USENIX Security 15).Google Scholar
Kurzweil, R. (2005). The singularity is near: When humans transcend biology. New York, NY: Viking Press.Google Scholar
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.CrossRefGoogle ScholarPubMed
Legg, S. (2008, June). “Machine Super Intelligence.” Paper presented at the PhD Thesis, University of Lugano. Retrieved from www.vetta.org/documents/Machine_Super_Intelligence.pdfGoogle Scholar
Loosemore, R. (2014). The maverick nanny with a dopamine drip: Debunking fallacies in the theory of AI motivation. AAAI Spring Symposium Series 2014 (March).Google Scholar
Loosemore, R., & Goertzel, B. (2012). Why an intelligence explosion is probable. In Eden, A. H., Moor, J. H., Søraker, J. H., & Steinhart, E. (Eds.) Singularity hypotheses (pp. 83–98). New York, NY: Springer.CrossRefGoogle Scholar
Losavio, M. et al. (2009). Assessing the legal risks in network forensic probing. In Peterson, G. & Shenoi, S (Eds.) Advances in Digital Forensics V (pp. 255–266), IFIP AICT. Heidelberg: Berlin: SpringerCrossRefGoogle Scholar
Majot, A. M., & Yampolskiy, R. (2015). Global catastrophic risk and security implications of quantum computers. Futures, 72(1), 17–26.Google Scholar
Majot, A. M., & Yampolskiy, R. V. (2014). AI safety engineering through introduction of self-reference into felicific calculus via artificial pain and pleasure. Paper presented at the IEEE International Symposium on Ethics in Science, Technology and Engineering, Chicago, IL (May 23–24).Google Scholar
McDaniel, R., & Yampolskiy, R. V. (2011). “Embedded non-interactive CAPTCHA for Fischer Random Chess.” Paper presented at the 16th International Conference on Computer Games (CGAMES), Louisville, KY.CrossRefGoogle Scholar
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., & Ostrovski, G. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.Google Scholar
Modi, C., Patel, D., Borisaniya, B., Patel, H., Patel, A., & Rajarajan, M. (2013). A survey of intrusion detection techniques in cloud. Journal of Network and Computer Applications, 36(1), 42–57.Google Scholar
Mohamed, A., Baili, N., D’Souza, D., & Yampolskiy, R. V. (2011). “Avatar face recognition using Wavelet Transform and hierarchical multi-scale LBP.” Paper presented at the Tenth International Conference on Machine Learning and Applications (ICMLA’11), Honolulu, USA (December 18–21).CrossRefGoogle Scholar
Muehlhauser, L., & Yampolskiy, R. (2013, July 15). “Roman Yampolskiy on AI Safety Engineering.” Paper presented at the Machine Intelligence Research Institute. Retrieved from http://intelligence.org/2013/07/15/roman-interview/Google Scholar
Musk, E. (2014). A ‘potentially dangerous outcome’ with AI. Paper presented at the CNBC. Retrieved from www.cnbc.com/video/2014/06/20/elon-musk-a-potentially-dangerous-outcome-with-ai.htmlGoogle Scholar
Novikov, D., Yampolskiy, R. V., & Reznik, L. (2006a). “Anomaly Detection Based Intrusion Detection.” Paper presented at the 3rd International Conference on Information Technology: New Generations (ITNG 2006), Las Vegas, Nevada, USA (April 10–12).Google Scholar
Novikov, D., Yampolskiy, R. V., & Reznik, L. (2006b). “Artificial intelligence Approaches for Intrusion Detection.” Paper presented at the Long Island Systems Applications and Technology Conference (LISAT 2006). Long Island, New York (May 5).Google Scholar
Omohundro, S. M. (2007). The nature of self-improving artificial intelligence. Paper presented at the Singularity Summit, San Francisco, CA.Google Scholar
Omohundro, S. M. (2008). The basic AI drives. In Wang, P., Goertzel, B., & Franklin, S. (Eds.), Proceedings of the first agi conference, volume 171, frontiers in artificial intelligence and applications. Amsterdam: IOS Press.Google Scholar
Pistono, F., & Yampolskiy, R. V. (2016). “Unethical Research: How to Create a Malevolent Artificial Intelligence.” Paper presented at the 25th International Joint Conference on Artificial Intelligence (IJCAI-16). Ethics for Artificial Intelligence Workshop (AI-Ethics-2016).Google Scholar
Potter, B., & McGraw, G. (2004). Software security testing. IEEE Security & Privacy, 2(5), 81–85.Google Scholar
Russell, S., & Norvig, P. (2003). Artificial intelligence: a modern approach. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Soares, N. (2015). The value learning problem. Machine Intelligence Research Institute, Berkley, CA, USA.Google Scholar
Soares, N., Fallenstein, B., Armstrong, S., & Yudkowsky, E. (2015). “Corrigibility.” Paper presented at the Workshops at the 29th AAAI Conference on Artificial Intelligence, Austin, Texas, USA (January 25–30).Google Scholar
Solomonoff, R. J. (1964). A formal theory of inductive inference. Part I. Information and Control, 7(1), 1–22.Google Scholar
Solomonoff, R. J. (1985). The time scale of artificial intelligence: Reflections on social effects. North-Holland Human Systems Management, 5, 149–153.Google Scholar
Sotala, K. (2012). Advantages of artificial intelligences, uploads, and digital minds. International Journal of Machine Consciousness, 4(01), 275–291.Google Scholar
Sotala, K., & Yampolskiy, R. V. (2015). Responses to catastrophic AGI risk: a survey. Physica Scripta, 90(1), 018001.Google Scholar
Spitzner, L. (2005). Know your enemy: Honeynets. Honeynet Project.Google Scholar
Turing, A. M. (1996). Intelligent machinery: A heretical theory. Philosophia Mathematica, 4(3), 256–260.Google Scholar
Tuxedage, . (2013, September 5). I attempted the AI Box Experiment again! (And won – Twice!). Retrieved from http://lesswrong.com/lw/ij4/i_attempted_the_ai_box_experiment_again_and_won/.Google Scholar
Vinge, V. (1993). “The Coming Technological Singularity: How to Survive in the Post-Human Era.” Paper presented at the Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace, Cleveland, OH (March 30–31).Google Scholar
Wiener, N. (1961). Cybernetics or control and communication in the animal and the machine 25. Cambridge, MA: MIT Press.Google Scholar
Yampolskiy, R., Cho, G., Rosenthal, R., & Gavrilova, M. (2012). Experiments in artimetrics: avatar face recognition. Transactions on Computational Science XVI, 77–94.Google Scholar
Yampolskiy, R., & Fox, J. (2013). Safety engineering for artificial general intelligence. Topoi, 32(2), 217–226.Google Scholar
Yampolskiy, R. V. (2008a). Behavioral modeling: An overview. American Journal of Applied Sciences, 5(5), 496–503.Google Scholar
Yampolskiy, R. V. (2008b). Computer security: From passwords to behavioral biometrics. New York, NY:New Academic Publishing.Google Scholar
Yampolskiy, R. V. (2012). Leakproofing singularity – Artificial intelligence confinement problem. Journal of Consciousness Studies (JCS), 19(1–2), 194–214.Google Scholar
Yampolskiy, R. V. (2013a). Artificial intelligence safety engineering: Why machine ethics is a wrong approach. In Müller, V. C. (Ed.) Philosophy and theory of artificial intelligence (pp. 389–396), Berlin; Heidelberg: Springer.Google Scholar
Yampolskiy, R. V. (2013a). Turing test as a defining feature of AI-completeness. In Yang, Xin-She (Ed.), Artificial intelligence, evolutionary computation and metaheuristics - In the footsteps of Alan Turing (pp. 3–17). Berlin; Heidelberg: Springer.Google Scholar
Yampolskiy, R. V. (2013b). What to do with the singularity paradox? In Müller, V. (Ed.) Philosophy and theory of artificial intelligence (pp. 397–413). Berlin; Heidelberg: Springer.Google Scholar
Yampolskiy, R. V. (2014a). The universe of minds. arXiv preprint arXiv:1410.0369.Google Scholar
Yampolskiy, R. V. (2014b). Utility function security in artificially intelligent agents. Journal of Experimental & Theoretical Artificial Intelligence, 26(3), 373–389.Google Scholar
Yampolskiy, R. V. (2015). Artificial superintelligence: A futuristic approach. London: Chapman and Hall/CRC Press.Google Scholar
Yampolskiy, R. V. (2015). From seed AI to technological singularity via recursively self-improving software. arXiv preprint arXiv:1502.06512.Google Scholar
Yampolskiy, R. V. (2016). “Taxonomy of Pathways to Dangerous Artificial Intelligence.” Paper presented at the Workshops at the Thirtieth AAAI Conference on Artificial Intelligence.Google Scholar
Yampolskiy, R. V. (2018). Artificial intelligence safety and security. Boca Raton, FL: CRC Press.Google Scholar
Yampolskiy, R. V., & Gavrilova, M. L. (2012). Artimetrics: Biometrics for artificial entities. Robotics & Automation Magazine, IEEE, 19(4), 48–58.Google Scholar
Yampolskiy, R. V., & Govindaraju, V. (2008). Behavioural biometrics: a survey and classification. International Journal of Biometrics, 1(1), 81–113.Google Scholar
Yampolskiy, R. V., & Govindaraju, V. (2009). Strategy-based behavioural biometrics: a novel approach to automated identification. International Journal of Computer Applications in Technology, 35(1), 29–41.Google Scholar
Yampolskiy, R. V., Rebolledo-Mendez, J. D., & Hindi, M. M. (2014). Password protected Visual cryptography via cellular automaton Rule 30. Transactions on Data Hiding and Multimedia Security IX, 57–67.Google Scholar
Yampolskiy, R. V., & Spellchecker, M. (2016). Artificial intelligence safety and cybersecurity: A timeline of AI failures. arXiv preprint arXiv:1610.07997.Google Scholar
Yan, L.-K., Jayachandra, M., Zhang, M., & Yin, H. (2012). V2E: combining hardware virtualization and software emulation for transparent and extensible malware analysis. ACM Sigplan Notices, 47(7), 227–238.Google Scholar
Yudkowsky, E. S. (2002). The AI-Box experiment. Retrieved from http://yudkowsky.net/singularity/aiboxGoogle Scholar
Yudkowsky, E. S. (2008a). That Alien Message. Retrieved from www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien-message.Google Scholar
Yudkowsky, E. S. (2008b). Artificial intelligence as a positive and negative factor in global risk. In Bostrom, N. & Cirkovic, M. M. (Eds.), Global catastrophic risks (pp. 308–345). Oxford, UK: Oxford University Press.Google Scholar
Yudkowsky, E. S. (2011). Complex value systems in friendly AI. In Proceedings of the Artificial General Intelligence: 4th International Conference, AGI 2011 (pp. 388–393). Mountain View, CA: Springer.Google Scholar
Yudkowsky, E. S., & Hanson, R. (2008). “The Hanson-Yudkowsky AI-foom Debate.” Paper presented at the MIRI Technical Report. Retrieved from at: http://intelligence.org/files/AIFoomDebate.pdfGoogle Scholar

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