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The humanness of artificial non-normative personalities

Published online by Cambridge University Press:  10 November 2017

Kevin B. Clark
Kevin B. Clark*
Affiliation:
Research and Development Service, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073; California NanoSystems Institute, University of California at Los Angeles, Los Angeles, CA 90095; Extreme Science and Engineering Discovery Environment (XSEDE), National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, Urbana, IL 61801; Biological Collaborative Research Environment (BioCoRE), Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801. kbclarkphd@yahoo.comwww.linkedin.com/pub/kevin-clark/58/67/19a
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Abstract

Technoscientific ambitions for perfecting human-like machines, by advancing state-of-the-art neuromorphic architectures and cognitive computing, may end in ironic regret without pondering the humanness of fallible artificial non-normative personalities. Self-organizing artificial personalities individualize machine performance and identity through fuzzy conscientiousness, emotionality, extraversion/introversion, and other traits, rendering insights into technology-assisted human evolution, robot ethology/pedagogy, and best practices against unwanted autonomous machine behavior.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e259
Copyright
Copyright © Cambridge University Press 2017 

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References

Arbib, M. A. & Fellous, J. M. (2004) Emotions: From brain to robot. Trends in Cognitive Science 8(12):554–61.CrossRefGoogle ScholarPubMed
Asada, M. (2015) Development of artificial empathy. Neuroscience Research 90:41–50.Google Scholar
Bengio, Y. (2016) Machines who learn. Scientific American 314(6):46–51.Google Scholar
Berdahl, C. H. (2010) A neural network model of Borderline Personality Disorder. Neural Networks 23(2):177–88.CrossRefGoogle ScholarPubMed
Bostrom, N. (2014) Superintelligence: Paths, dangers, strategies. Oxford University Press. ISBN 978-0199678112.Google Scholar
Briegel, H. J. (2012) On creative machines and the physical origins of freedom. Scientific Reports 2:522.Google Scholar
Calimera, A., Macii, E. & Poncino, M. (2013) The human brain project and neuromorphic computing. Functional Neurology 28(3):191–96.Google Scholar
Cardon, A. (2006) Artificial consciousness, artificial emotions, and autonomous robots. Cognitive Processes 7(4):245–67.Google Scholar
Clark, K. B. (2012) A statistical mechanics definition of insight. In: Computational intelligence, ed. Floares, A. G., pp. 139–62. Nova Science. ISBN 978-1-62081-901-2.Google Scholar
Clark, K. B. (2014) Basis for a neuronal version of Grover's quantum algorithm. Frontiers in Molecular Neuroscience 7:29.Google Scholar
Clark, K. B. (2015) Insight and analysis problem solving in microbes to machines. Progress in Biophysics and Molecular Biology 119:183–93.Google Scholar
Clark, K. B. (in press-a) Classical and quantum Hebbian learning in modeled cognitive processing. Frontiers in Psychology.Google Scholar
Clark, K. B. (in press-b) Neural field continuum limits and the partitioning of cognitive-emotional brain networks. Molecular and Cellular Neuroscience.Google Scholar
Clark, K. B. (in press-c) Psychometric “Turing test” of general intelligences in social robots. Information Sciences.Google Scholar
Clark, K. B. & Hassert, D. L. (2013) Undecidability and opacity of metacognition in animals and humans. Frontiers in Psychology 4:171.CrossRefGoogle Scholar
Davies, J. (2016) Program good ethics into artificial intelligence. Nature 538(7625). Available at: http://www.nature.com/news/program-good-ethics-into-artificial-intelligence-1.20821.Google ScholarPubMed
Di, G. Q. & Wu, S. X. (2015) Emotion recognition from sound stimuli based on back-projection neural networks and electroencephalograms. Journal of the Acoustics Society of America 138(2):994–1002.CrossRefGoogle Scholar
Fogel, D. B. & Fogel, L. J. (1995) Evolution and computational intelligence. IEEE Transactions on Neural Networks 4:1938–41.Google Scholar
Fung, P. (2015) Robots with heart. Scientific American 313(5):60–63.Google Scholar
Han, M. J., Lin, C. H. & Song, K. T. (2013) Robotic emotional expression generation based on mood transition and personality model. IEEE Transactions on Cybernetics 43(4):1290–303.Google Scholar
Hiolle, A., Lewis, M. & Cañamero, L. (2014) Arousal regulation and affective adaptation to human responsiveness by a robot that explores and learns a novel environment. Frontiers in Neurorobotics 8:17.CrossRefGoogle ScholarPubMed
Indiveri, G. & Liu, S.-C. (2015) Memory and information processing in neuromorphic systems. Proceedings of the IEEE 103(8):1379–97.Google Scholar
Kaipa, K. N., Bongard, J. C. & Meltzoff, A. N. (2010) Self discovery enables robot social cognition: Are you my teacher? Neural Networks 23(8–9):1113–24.Google Scholar
Lande, T. S., ed. (1998) Neuromorphic systems engineering: Neural networks in silicon. Kluwer International Series in Engineering and Computer Science, vol. 447. Kluwer Academic. ISBN 978-0-7923-8158-7.Google Scholar
McShea, D. W. (2013) Machine wanting. Studies on the History and Philosophy of Biological and Biomedical Sciences 44(4 pt B):679–87.Google Scholar
Meltzoff, A. N., Kuhl, P. M., Movellan, J. & Sejnowski, T. J. (2009) Foundations for a new science of learning. Science 325(5938):284–88.Google Scholar
Nisbett, R. E. & Ross, L. (1980) Human inference: Strategies and shortcomings of social judgment. Prentice-Hall. ISBN 0-13-445073-6.Google Scholar
Parker, S. T. & McKinney, M. L. (1999) Origins of intelligence: The evolution of cognitive development in monkeys, apes and humans. Johns Hopkins University Press. ISBN 0-8018-6012-1.Google Scholar
Read, S. J., Monroe, B. M., Brownstein, A. L., Yang, Y., Chopra, G. & Miller, L. C. (2010) A neural network model of the structure and dynamics of human personality. Psychological Reviews 117(1):61–92.Google Scholar
Romanes, G. J. (1884) Animal intelligence. Appleton.Google Scholar
Schuller, I. K., Stevens, R. & Committee Chairs (2015) Neuromorphic computing: From materials to architectures. Report of a roundtable convened to consider neuromorphic computing basic research needs. Office of Science, U.S. Department of Energy.Google Scholar
Thomaz, A. L. & Cakmak, M. (2013) Active social learning in humans and robots. In: Social learning theory: Phylogenetic considerations across animal, plant, and microbial taxa, ed. Clark, K. B., pp. 113–28. Nova Science. ISBN 978-1-62618-268-4.Google Scholar
Wallach, W., Franklin, S. & Allen, C. (2010) A conceptual and computational model of moral decision making in human and artificial agents. Topics in Cognitive Science 2:454–85.Google Scholar
Weigmann, K. (2006) Robots emulating children. EMBO Reports 7(5):474–76.Google Scholar
Wolfram, S. (2002) A new kind of science. Wolfram Media. ISBN 1-57955-008-8.Google Scholar
Youyou, W., Kosinski, M. & Stillwell, D. (2015) Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences of the United States of America 112(4):1036–40.Google Scholar
Zentall, T. R. (2013) Observational learning in animals. In: Social learning theory: Phylogenetic considerations across animal, plant, and microbial taxa, ed. Clark, K. B., pp. 3–33. Nova Science. ISBN 978-1-62618-268-4.Google Scholar