Acta Biotheoretica

, Volume 49, Issue 3, pp 171–189 | Cite as

Extrapolating a Hierarchy of Building Block Systems Towards Future Neural Network Organisms

  • Gerard Jagers op Akkerhuis


It is possible to predict future life forms? In this paper it is argued that the answer to this question may well be positive. As a basis for predictions a rationale is used that is derived from historical data, e.g. from a hierarchical classification that ranks all building block systems, that have evolved so far. This classification is based on specific emergent properties that allow stepwise transitions, from low level building blocks to higher level ones. This paper shows how this hierarchy can be used for predicting future life forms.

The extrapolations suggest several future neural network organisms. Major aspects of the structures of these organisms are predicted. The results can be considered of fundamental importance for several reasons. Firstly, assuming that the operator hierarchy is a proper basis for predictions, the result yields insight into the structure of future organisms. Secondly, the predictions are not extrapolations of presently observed trends, but are fully integrated with all historical system transitions in evolution. Thirdly, the extrapolations suggest the structures of intelligences that, one day, will possess more powerful brains than human beings.

This study ends with a discussion of possibilities for falsification of the present theory, the implications of the present predictions in relation to recent developments in artificial intelligence and the philosophical implications of the role of humanity in evolution with regard to the creation of future neural network organisms.

System hierarchy evolution AI operator hypothesis building blocks emergent properties Constrained Generating Procedures neural network organisms memes 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bertalanffy, L. von (1968). General System Theory. Braziller, New York.Google Scholar
  2. Close, F. (1983). The cosmic onion: Quarks and the nature of the universe. American Institute of Physics, USA.Google Scholar
  3. Dawkins, R. (1976). The selfish gene. Oxford University Press, Oxford.Google Scholar
  4. Eigen, M. and P. Schuster (1977). The hypercycle: a principle of natural self-organisation, Part A: The emergence of the hypercycle. Naturwissenschaften 64: 541.Google Scholar
  5. Feibleman, J.K. (1954). Theory of integrative levels. British Journal for the Philosophy of Science 5: 59-66.Google Scholar
  6. Happel, B.L.M. (1997). Principles of neural organisation: Modular neuro-dynamics. PhD thesis, 125 pp.Google Scholar
  7. Heylighen, F. (1995). Systems as constraints on variation. A classification and natural history of metasystem transitions. World Futures 45: 59-85.Google Scholar
  8. Holland, J.H. (1998). Emergence from chaos to order. Oxford University Press, Oxford.Google Scholar
  9. Jagers op Akkerhuis, G.A.J.M. and N.M. van Straalen (1998). Operators, the Lego bricks of nature: evolutionary transitions from fermions to neural networks. World Futures 53: 329-345.Google Scholar
  10. Kauffman, S.A. (1993). The origins of order: Self-organisation and selection in evolution. Oxford University Press, Oxford. 709 pp.Google Scholar
  11. Kelly, K. (1994). Out of control. The rise of neo-bological civilisation. A William Patric Book. Addison Wesley Publishing Company, Reading.Google Scholar
  12. Koestler, A. (1978). Janus, a summing up. Hutchinson & Co., London.Google Scholar
  13. Kurzweil, R. (1999). The age of the spiritual machines. When computers exceed human intelligence. Viking, New York.Google Scholar
  14. Laszlo, E. (1994). From GUT's to GET's: Prospects for a unified evolution theory. World Futures 42: 233-239.Google Scholar
  15. Murre, J.M.J., R.H. Phaf and G. Wolters (1989). Calm networks: a modular approach to supervised and unsupervised learning. Proceedings of the International Joint Conference on Neural Networks, Washington DC, New York: IEEE Press: 649-656Google Scholar
  16. Murre, J.M.J., R.H. Phaf and G. Wolters (1992). CALM: Categorising and learning module. Neural Networks 5: 55-82.Google Scholar
  17. Simon, H.A. (1962). The architecture of complexity. Procedings of the American Society for the Philosophy of Science 106: 467-482.Google Scholar
  18. Teilhard de Jardin, P. (1969). The future of man. Editions de Seuil V, Paris, (1946).Google Scholar
  19. Warwick, K. (1997). The march of the machines. Why the new race of robots will rule the world. Century Books Ltd., London.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Gerard Jagers op Akkerhuis
    • 1
  1. 1.AlterraWageningenThe Netherlands

Personalised recommendations