Advertisement

Conclusions and Next Steps

  • James A. Crowder
  • John Carbone
  • Shelli Friess
Chapter

Abstract

Although we feel we have made a great start and have covered much about how to test artificial intelligent systems at various levels, much research and development is needed. One major topic of research to be continued is to understand the variables that can be used to control learning performance and explicit knowledge in the context of human interaction with artificial intelligent entities [1]. The relationship between implicit and explicit modes of learning and implicit and explicit types of knowledge has not been established and must be explored before we put artificial intelligent systems into long-term service either within the Department of Defense or the private, commercial sector. The relationships between decision and action may be critically influenced by implicit learning and knowledge and we need to understand how implicit vs. implicit learning and knowledge affect the ability of systems to learn and act effectively and correctly.

Keywords

Implicit knowledge Explicit knowledge Integrated system health Artificial psychology 

References

  1. 1.
    Berry, D., & Broadbent, D. (1988). Interactive tasks and the implicit-explicit distinction. British Journal of Psychology, 79, 251–272.CrossRefGoogle Scholar
  2. 2.
    Crowder, J., & Friess, S. (2012). Artificial psychology: The psychology of AI. In International Multiconference on Complexity, Informatics and Cybernetics, Orlando, FL.Google Scholar
  3. 3.
    Clark, A., & Karmiloff-Smith, A. (1993). The cognizer’s innards: A psychological and philosophical perspective on the development of thought. Mind and Language, 8(4), 487–519.CrossRefGoogle Scholar
  4. 4.
    Cleeremans, A. (1994). Attention and awareness in sequence learning. In Proceedings of Cognitive Science Society Annual Conference (pp. 330–335). Google Scholar
  5. 5.
    Keele, S., Ivry, R., Hazeltine, E., Mayr, U., & Heuer, H. (1998). The cognitive and neural architecture of sequence representation (Technical report No. 98-03). University of Oregon.Google Scholar
  6. 6.
    Lewicki, M., & Hoffman, H. (1987). Unconscious acquisition of complex procedural knowledge. Journal of Experimental Psychology: Learning, Memory and Cognition, 13(4), 523–530.Google Scholar
  7. 7.
    Sun, R. (1997). Learning, action, and consciousness: A hybrid approach towards modeling consciousness. Neural Networks, 10(7), 1317–1331.CrossRefGoogle Scholar
  8. 8.
    Stone, P. (2016). Artificial intelligence and life in 2030. One hundred year study on artificial intelligence: Report of the 2015–2016 study panel. Stanford, CA: Stanford University.Google Scholar
  9. 9.
    Tchopp, M. (2018). Psychology of artificial intelligence: Foundations, range and implications from a humanities perspective. Medium Corporation. Retrieved from https://medium.com/womeninai/psychology-of-artificial-intelligence-ca0f0a9f3d7c.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • James A. Crowder
    • 1
  • John Carbone
    • 2
  • Shelli Friess
    • 3
  1. 1.Colorado Engineering Inc.Colorado SpringsUSA
  2. 2.ForcepointAustinUSA
  3. 3.Walden UniversityMinneapolisUSA

Personalised recommendations