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Emergent Causality and the Foundation of Consciousness

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Artificial General Intelligence (AGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13921))

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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.

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Notes

  1. 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. 2.

    e.g. \(Z_s\) is the extension of s.

  3. 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. 4.

    Assuming interventions are distinguishable.

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Acknowledgement

Appendices available on GitHub [1], supported by JST (JPMJMS2033).

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Correspondence to Michael Timothy Bennett .

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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

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  • DOI: https://doi.org/10.1007/978-3-031-33469-6_6

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