The Problem of Meaning in AI and Robotics: Still with Us after All These Years

The essay critically evaluates progress on the problem of meaning in artificial intelligence and robotics, remaining skeptical that deep neural networks or cognitive robotics fundamentally address it. It agrees with the enactive approach that things appear meaningful for living beings due to their precarious existence as adaptive autopoietic individuals, but notes this approach fails to explain how meaning could influence an agent's behavior. If life and mind are identified with physically deterministic phenomena, meaning cannot play a causal role. The authors argue this impotence of meaning can be resolved by revising the concept of nature so that the macroscopic scale of the living involves physical indeterminacy, and they consider implications for synthetic approaches.

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Abstract

In this essay we critically evaluate the progress that has been made in solving the problem of meaning in artificial intelligence (AI) and robotics. We remain skeptical about solutions based on deep neural networks and cognitive robotics, which in our opinion do not fundamentally address the problem. We agree with the enactive approach to cognitive science that things appear as intrinsically meaningful for living beings because of their precarious existence as adaptive autopoietic individuals. But this approach inherits the problem of failing to account for how meaning as such could make a difference for an agent’s behavior. In a nutshell, if life and mind are identified with physically deterministic phenomena, then there is no conceptual room for meaning to play a role in its own right. We argue that this impotence of meaning can be addressed by revising the concept of nature such that the macroscopic scale of the living can be characterized by physical indeterminacy. We consider the implications of this revision of the mind-body relationship for synthetic approaches.