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The Artificial Scientist: Logicist, Emergentist, and Universalist Approaches to Artificial General Intelligence

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Artificial General Intelligence (AGI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13154))

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Abstract

We attempt to define what is necessary to construct an Artificial Scientist, explore and evaluate several approaches to artificial general intelligence (AGI) which may facilitate this, conclude that a unified or hybrid approach is necessary and explore two theories that satisfy this requirement to some degree.

This work was supported by JST (JPMJMS2033; JPMJPR17G9).

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References

  1. Goertzel, B.: Artificial general intelligence: concept, state of the art, and future prospects. J. Artif. Gen. Intell. 5(1), 1–48 (2014)

    Article  Google Scholar 

  2. Adams, S., et al.: Mapping the landscape of human-level artificial general intelligence. AI Mag. 33(1), 25–42 (2012)

    Google Scholar 

  3. Evans, R., Hernández-Orallo, J., Welbl, J., Kohli, P., Sergot, M.: Making sense of sensory input. Artif. Intell. 293, 103438 (2021)

    Article  MathSciNet  Google Scholar 

  4. Evans, R., et al.: Making Sense of Raw Input. Artif. Intell. 299, 103521 (2021)

    Article  MathSciNet  Google Scholar 

  5. Evans, R.: Kant’s Cognitive Architecture. PhD Thesis, Imperial College London (2020)

    Google Scholar 

  6. Hutter, M.: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer, Berlin (2005)

    Google Scholar 

  7. Budhathoki, K., Vreeken, J.: Origo: causal inference by compression. Knowl. Inf. Syst. 56(2), 28–307 (2018)

    Article  Google Scholar 

  8. Chaitin, G.: The limits of reason. Sci. Am. 294(3), 74–81 (2006)

    Article  Google Scholar 

  9. Legg, S.: Machine Super Intelligence (2008)

    Google Scholar 

  10. Taniguchi, T., et al.: Symbol Emergence in Cognitive Developmental systems: a survey. IEEE Trans. Cogn. Dev. Syst. 11(4), 494–516 (2019)

    Article  Google Scholar 

  11. Taniguchi, T., et al.: Symbol emergence in robotics: a survey. Adv. Robot. 30(11–12), 706–728 (2016)

    Article  Google Scholar 

  12. Kolmogorov, A. N.: On tables of random numbers. Sankhya: Indian J. Stat. A 369–376 (1963)

    Google Scholar 

  13. Solomonoff, R.J.: A formal theory of inductive inference. Part I. Inf. Control 7(1), 1–22 (1964)

    Article  MathSciNet  Google Scholar 

  14. Solomonoff, R.J.: A formal theory of inductive inference. Part II. Inf. Control 7(2), 224–254 (1964)

    Article  MathSciNet  Google Scholar 

  15. Wang, P.: Embodiment: does a laptop have a body? In: Proceedings of AGI-09, pp. 74–179 (2009)

    Google Scholar 

  16. Bennett, M.T., Maruyama, Y.: Philosophical specification of empathetic ethical artificial intelligence. IEEE Trans. Cogn. Dev. Syst. (2021)

    Google Scholar 

  17. Bennett, M.T.: Symbol emergence and the solutions to any task. In: Goertzel, B. (ed.) AGI 2021. LNAI, vol. 13154, pp. 30–40 (2022). https://doi.org/10.1007/978-3-030-93758-4_4

  18. Nilsson, N.J.: The physical symbol system hypothesis: status and prospects. In: Lungarella, M., Iida, F., Bongard, J., Pfeifer, R. (eds.) 50 Years of Artificial Intelligence. LNCS (LNAI), vol. 4850, pp. 9–17. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77296-5_2

    Chapter  Google Scholar 

  19. Pearl, J.: Causality: Models, Reasoning and Inference. 2nd. Cambridge University Press, USA (2009)

    Google Scholar 

  20. Pearl, J., Mackenzie, D.: The Book of Why: The New Science of Cause and Effect. 1st. BasicBooks, Inc. USA (2018)

    Google Scholar 

  21. Floridi, L., Chiriatti, M.: GPT-3: its nature, scope, limits, and consequences. Minds Mach. 30(4), 681–694 (2020). https://doi.org/10.1007/s11023-020-09548-1

    Article  Google Scholar 

  22. Newell, A.: Physical symbol systems. Cogn. Sci. 4(2), 135–183 (1980)

    Article  Google Scholar 

  23. Harnad, S.: The symbol grounding problem. Phys. D Nonlinear Phenom. 42(1–3), 335–346 (1990)

    Article  Google Scholar 

  24. Barsalou, L.W.: Perceptual symbol systems. Behav. Brain Sci. 22(4), 577–660 (1999)

    Article  Google Scholar 

  25. Miłkowski, M.: Embodied Cognition (2018)

    Google Scholar 

  26. King, R.D., et al.: The automation of science. Science 324, 85–89 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computing, Australian National University, Canberra, Australia

    Michael Timothy Bennett & Yoshihiro Maruyama

Authors
  1. Michael Timothy Bennett
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  2. Yoshihiro Maruyama
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Corresponding author

Correspondence to Michael Timothy Bennett .

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Editors and Affiliations

  1. SingularityNET, Amsterdam, The Netherlands

    Ben Goertzel

  2. SingularityNET, Amsterdam, The Netherlands

    Matthew Iklé

  3. SingularityNET, Amsterdam, The Netherlands

    Alexey Potapov

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Bennett, M.T., Maruyama, Y. (2022). The Artificial Scientist: Logicist, Emergentist, and Universalist Approaches to Artificial General Intelligence. In: Goertzel, B., Iklé, M., Potapov, A. (eds) Artificial General Intelligence. AGI 2021. Lecture Notes in Computer Science(), vol 13154. Springer, Cham. https://doi.org/10.1007/978-3-030-93758-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-93758-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93757-7

  • Online ISBN: 978-3-030-93758-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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