Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T21:55:51.882Z Has data issue: false hasContentIssue false

The humanness of artificial non-normative personalities

Published online by Cambridge University Press:  10 November 2017

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. [email protected]/pub/kevin-clark/58/67/19a

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
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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 90:4150.Google Scholar
Bengio, Y. (2016) Machines who learn. Scientific American 314(6):4651.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):9941002.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):6063.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):6192.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. 333. Nova Science. ISBN 978-1-62618-268-4.Google Scholar