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Task Muddiness, Intelligence Metrics, and the Necessity of Autonomous Mental Development

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Abstract

This paper introduces a concept called task muddiness as a metric for higher intelligence. Task muddiness is meant to be inclusive and expendable in nature. The intelligence required to execute a task is measured by the composite muddiness of the task described by multiple muddiness factors. The composite muddiness explains why many challenging tasks are muddy and why autonomous mental development is necessary for muddy tasks. It facilitates better understanding of intelligence, what the human adult mind can do, and how to build a machine to acquire higher intelligence. The task-muddiness indicates a major reason why a higher biological mind is autonomously developed from autonomous, simple-to-complex experience. The paper also discusses some key concepts that are necessary for understanding the mind and intelligence, such as intelligence metrics, the mode a task is conveyed to the task executor, a human and a machine being a joint task performer in the traditional artificial intelligence (AI), a developmental agent (human or machine) being a sole task performer, and the need for autonomy in task-nonexplicit learning.

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Notes

  1. Biologists and neuroscientists have no problem with calling it a program. Read, e.g., Reik and Dean (2002) and Sur and Rubersein (2005).

  2. The reader is referred to a special issue on autonomous mental development in the vol. 11, no. 2 issue of the IEEE Transactions on Evolutionary Computation, guest-edited by Jay McClelland, Kim Plunkett and Juyang Weng.

  3. See, e.g., an excellent textbook by Russell and Norvig (2003) and an excellent survey by Franklin and Graesser (1997).

References

  • Arthur, G. (1952). The Arthur adaptation of the Leither international performance scale. Washington, DC: The Psychological Service Center Press.

    Google Scholar 

  • Bayley, N. (1993). Bayley scales of infant development (2nd ed.). San Antonio, Texas: Psychological Corp.

    Google Scholar 

  • Brooks, R. A. (2002). Flesh and machines: How robots will change us. New York, NY: Pantheon Books.

    Google Scholar 

  • Cohen, P. R., & Howe, A. E. (1988). How evaluation guides AI research. AI Magazine, 9(4), 35–43.

    Google Scholar 

  • Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1997). Rethinking innateness: A connectionist perspective on development. Cambridge, Massachusetts: MIT Press.

    Google Scholar 

  • Enderle, J., Blanchard, S. M., & Bronzino, J. (2005). Introduction to biomedical engineering (2nd ed.). Burlington, Massachusetts: Elsevier Academic.

    Google Scholar 

  • Franklin, S., & Graesser, A. (1997). Is it an agent, or just a program?: A taxonomy for autonomous agents. In Intelligent Agents III, Lecture Notes on Artificial Intelligence (pp. 21–35). Berlin: Springer-Verlag.

  • Gardner, H. (1993). Multiple intelligences: The theory in practice. New York: Basic Books.

    Google Scholar 

  • Goleman, D. (1995). Emotional intelligence. New York: Bantam Books.

    Google Scholar 

  • Meystel, A. M., & Messina, E. R. (Eds.) Measuring the Performance and Intelligence of Systems: Proceedings of the 2000 PerMIS Workshop. Gaithersburg, Maryland: National Institute of Standards and Technology, August 14–16, 2000.

  • Michie, D. (1993). Turing’s test and conscious thought. Artificial Intelligence, 60, 1–22.

    Article  MathSciNet  Google Scholar 

  • Norman, D. A. (1991). Approaches to the study of intelligence. Artificial Intelligence, 47, 327–346.

    Article  MathSciNet  Google Scholar 

  • Reik, W., & Dean, W. (2002). Epigenetic reprogramming back to the beginning. Nature, 420(6912), 127.

    Google Scholar 

  • Russell, S., & Norvig, P. (1995). Artificial intelligence: A modern approach. New Jersey: Prentice-Hall, Upper Saddle River.

    MATH  Google Scholar 

  • Russell, S., & Norvig, P. (2003). Artificial intelligence: A modern approach (2nd ed.). New Jersey: Prentice-Hall, Upper Saddle River.

    Google Scholar 

  • Sur, M., & Rubenstein, J. L. R. (2005). Patterning and plasticity of the cerebral cortex. Science, 310, 805–810.

    Article  Google Scholar 

  • Tononi, G., & Edelman, G. M. (1998). Consciousness and complexity. Science, 282(5395), 1846–1851.

    Article  Google Scholar 

  • Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460.

    Article  MathSciNet  Google Scholar 

  • Weng, J. (2004). Developmental robotics: Theory and experiments. International Journal of Humanoid Robotics, 1(2), 199–235.

    Article  Google Scholar 

  • Weng, J., & Hwang, W. (2006). From neural networks to the brain: Autonomous mental development. IEEE Computational Intelligence Magazine, 1(3), 15–31.

    Article  Google Scholar 

  • Weng, J., Luwang, T., Lu, H., & Xue, X. (2008). Multilayer in-place learning networks for modeling functional layers in the laminar cortex. Neural Networks, 21, 150–159.

    Article  Google Scholar 

  • Weng, J., McClelland, J., Pentland, A., Sporns, O., Stockman, I., Sur, M., & Thelen, E. (2001). Autonomous mental development by robots and animals. Science, 291(5504), 599–600.

    Article  Google Scholar 

Download references

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  1. Michigan State University, East Lansing, MI, 48824, USA

    Juyang Weng

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  1. Juyang Weng
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Correspondence to Juyang Weng.

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Weng, J. Task Muddiness, Intelligence Metrics, and the Necessity of Autonomous Mental Development. Minds & Machines 19, 93–115 (2009). https://doi.org/10.1007/s11023-008-9127-1

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