Abstract
To make accurate inferences in an interactive setting, an agent must not confuse passive observation of events with having intervened to cause them. The do operator formalises interventions so that we may reason about their effect. Yet there exist pareto optimal mathematical formalisms of general intelligence in an interactive setting which, presupposing no explicit representation of intervention, make maximally accurate inferences. We examine one such formalism. We show that in the absence of a do operator, an intervention can be represented by a variable. We then argue that variables are abstractions, and that need to explicitly represent interventions in advance arises only because we presuppose these sorts of abstractions. The aforementioned formalism avoids this and so, initial conditions permitting, representations of relevant causal interventions will emerge through induction. These emergent abstractions function as representations of one’s self and of any other object, inasmuch as the interventions of those objects impact the satisfaction of goals. We argue that this explains how one might reason about one’s own identity and intent, those of others, of one’s own as perceived by others and so on. In a narrow sense this describes what it is to be aware, and is a mechanistic explanation of aspects of consciousness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The vocabulary \(\mathfrak {v}\) we single out represents the sensorimotor circuitry with which an organism enacts cognition - their brain, body, local environment and so forth.
- 2.
e.g. \(Z_s\) is the extension of s.
- 3.
For example, were we trying to generalise from \(\alpha \) to \(\omega \) (where \(\alpha \sqsubset \omega \)) and knew the definition of \(\alpha \) contained misleading errors, we might selectively forget outlying decisions in \(\alpha \) to create a child \(\gamma = \langle S_\gamma , D_\gamma , M_\gamma \rangle \) (where \(\gamma \sqsubset \alpha \)) such that \(M_\gamma \) contained far weaker hypotheses than \(M_\alpha \).
- 4.
Assuming interventions are distinguishable.
References
Bennett, M.T.: Technical Appendices. Version 1.2.1 (2023). https://doi.org/10.5281/zenodo.7641742. https://github.com/ViscousLemming/Technical-Appendices
Pearl, J., Mackenzie, D.: The Book of Why: The New Science of Cause and Effect, 1st edn. Basic Books Inc, New York (2018)
Ortega, P.A., et al.: Shaking the foundations: delusions in sequence models for interaction and control. Deepmind (2021)
Pearl, J.: Causal diagrams for empirical research. Biometrika 82(4), 669–688 (1995)
Pearl, J.: Causality, 2nd edn. Cambridge University Press, Cambridge (2009)
Hutter, M.: Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability. Springer, Heidelberg (2010). https://doi.org/10.1007/b138233
Bennett, M.T.: Symbol emergence and the solutions to any task. In: Goertzel, B., Iklé, M., Potapov, A. (eds.) AGI 2021. LNCS (LNAI), vol. 13154, pp. 30–40. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-93758-4_4
Harnad, S.: The symbol grounding problem. Phys. D: Nonlinear Phenom. 42(1), 335–346 (1990)
Franklin, S., Baars, B.J., Ramamurthy, U.: A phenomenally conscious robot? In: APA Newsletter on Philosophy and Computers, vol. 1 (2008)
Boltuc, P.: The engineering thesis in machine consciousness. Techné Res. Philos. Technol. 16(2), 187–207 (2012)
Block, N.: The harder problem of consciousness. J. Philos. 99(8), 391 (2002)
Chalmers, D.: Facing up to the problem of consciousness. J. Conscious. Stud. 2(3), 200–219 (1995)
Ward, D., Silverman, D., Villalobos, M.: Introduction: the varieties of enactivism. Topoi 36(3), 365–375 (2017). https://doi.org/10.1007/s11245-017-9484-6
Wheeler, M.: Martin heidegger. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy. Fall 2020. Stanford University (2020)
Bennett, M.T., Maruyama, Y.: Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI. Manuscript (2021)
Bekinschtein, P., et al.: A retrieval-specific mechanism of adaptive forgetting in the mammalian brain. Nat. Commun. 9(1), 4660 (2018)
Berlin, S.B.: Dichotomous and complex thinking. Soc. Serv. Rev. 64(1), 46–59 (1990)
Nickerson, R.S.: Confirmation bias: a ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 2(2), 175–220 (1998)
Bennett, M.T., Maruyama, Y.: Philosophical specification of empathetic ethical artificial intelligence. IEEE Trans. Cogn. Dev. Syst. 14(2), 292–300 (2022)
Grice, H.P.: Studies in the Way of Words. Harvard University Press, Cambridge (2007)
Kautz, H., Selman, B.: Planning as satisfiability. In: IN ECAI 1992, pp. 359–363. Wiley, New York (1992)
Urquiza-Haas, E.G., Kotrschal, K.: The mind behind anthropomorphic thinking: attribution of mental states to other species. Anim. Behav. 109, 167–176 (2015)
Acknowledgement
Appendices available on GitHub [1], supported by JST (JPMJMS2033).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bennett, M.T. (2023). Emergent Causality and the Foundation of Consciousness. In: Hammer, P., Alirezaie, M., Strannegård, C. (eds) Artificial General Intelligence. AGI 2023. Lecture Notes in Computer Science(), vol 13921. Springer, Cham. https://doi.org/10.1007/978-3-031-33469-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-33469-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-33468-9
Online ISBN: 978-3-031-33469-6
eBook Packages: Computer ScienceComputer Science (R0)