On Three Categories of Conscious Machines

  • Xerxes D. Arsiwalla
  • Ivan Herreros
  • Paul Verschure
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9793)

Abstract

Reviewing recent closely related developments at the crossroads of biomedical engineering, artificial intelligence and biomimetic technology, in this paper, we attempt to distinguish phenomenological consciousness into three categories based on embodiment: one that is embodied by biological agents, another by artificial agents and a third that results from collective phenomena in complex dynamical systems. Though this distinction by itself is not new, such a classification is useful for understanding differences in design principles and technology necessary to engineer conscious machines. It also allows one to zero-in on minimal features of phenomenological consciousness in one domain and map on to their counterparts in another. For instance, awareness and metabolic arousal are used as clinical measures to assess levels of consciousness in patients in coma or in a vegetative state. We discuss analogous abstractions of these measures relevant to artificial systems and their manifestations. This is particularly relevant in the light of recent developments in deep learning and artificial life.

Keywords

Conscious agents Artificial intelligence Complex systems Bioethics 

References

  1. 1.
    Arsiwalla, X.D., Verschure, P.: Computing information integration in brain networks. In: Wierzbicki, A., Brandes, U., Schweitzer, F., Pedreschi, D., et al. (eds.) NetSci-X 2016. LNCS, vol. 9564, pp. 136–146. Springer, Heidelberg (2016). doi:10.1007/978-3-319-28361-6_11 CrossRefGoogle Scholar
  2. 2.
    Arsiwalla, X.D., Verschure, P.F.: Integrated information for large complex networks. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE (2013)Google Scholar
  3. 3.
    Hutchison, C.A., Chuang, R.Y., Noskov, V.N., Assad-Garcia, N., Deerinck, T.J., Ellisman, M.H., Gill, J., Kannan, K., Karas, B.J., Ma, L., et al.: Design and synthesis of a minimal bacterial genome. Science 351(6280), aad6253 (2016)CrossRefGoogle Scholar
  4. 4.
    Kurihara, K., Okura, Y., Matsuo, M., Toyota, T., Suzuki, K., Sugawara, T.: A recursive vesicle-based model protocell with a primitive model cell cycle. Nat. Commun. 6, 8352 (2015)CrossRefGoogle Scholar
  5. 5.
    Laureys, S.: The neural correlate of (un) awareness: lessons from the vegetative state. Trends Cogn. Sci. 9(12), 556–559 (2005)CrossRefGoogle Scholar
  6. 6.
    Laureys, S., Owen, A.M., Schiff, N.D.: Brain function in coma, vegetative state, and related disorders. Lancet Neurol. 3(9), 537–546 (2004)CrossRefGoogle Scholar
  7. 7.
    Malyshev, D.A., Dhami, K., Lavergne, T., Chen, T., Dai, N., Foster, J.M., Corrêa, I.R., Romesberg, F.E.: A semi-synthetic organism with an expanded genetic alphabet. Nature 509(7500), 385–388 (2014)CrossRefGoogle Scholar
  8. 8.
    Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xerxes D. Arsiwalla
    • 1
  • Ivan Herreros
    • 1
  • Paul Verschure
    • 1
    • 2
  1. 1.Synthetic Perceptive Emotive and Cognitive Systems (SPECS) Lab, Center of Autonomous Systems and NeuroroboticsUniversitat Pompeu FabraBarcelonaSpain
  2. 2.Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain

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