Symbolic Behaviour in Artificial Intelligence

By Adam Santoro, Andrew Kyle Lampinen, Kory Wallace Mathewson, Timothy P. Lillicrap, David Raposo. 2021

40 citations recorded by the source corpus.

The ability to use symbols is the pinnacle of human intelligence, but has yet to be fully replicated in machines. Here we argue that the path towards symbolically fluent artificial intelligence (AI) begins with a reinterpretation of what symbols are, how they come to exist, and how a system behaves when it uses them. We begin by offering an interpretation of symbols as entities whose meaning is established by convention. But crucially, something is a symbol only for those who demonstrably and actively participate in this convention. We then outline how this interpretation thematically unifies the behavioural traits humans exhibit when they use symbols. This motivates our proposal that the field place a greater emphasis on symbolic behaviour rather than particular computational mechanisms inspired by more restrictive interpretations of symbols. Finally, we suggest that AI research explore social and cultural engagement as a tool to develop the cognitive machinery necessary for symbolic behaviour to emerge. This approach will allow for AI to interpret something as symbolic on its own rather than simply manipulate things that are only symbols to human onlookers, and thus will ultimately lead to AI with more human-like symbolic fluency.

Subfields: Artificial Intelligence, Cultural Studies, Developmental and Educational Psychology, Cognitive Science & Psychology, Philosophy

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Citations in the corpus

Can language models learn from explanations in context? (2022, background): “Thus, models trained in richer, more interactive settings might benefit more from explanations (Santoro et al., 2021).”
Language models show human-like content effects on reasoning (2022, background): “…that is supported by a range of different environmen- 3Causal evidence is scarce, because years of education are difficult to experimentally manipulate. tal and educational factors (Santoro et al., 2021; Wang, 2021), rather than a core ability that must be built in to an intelligent system.”
Tell me why! - Explanations support learning of relational and causal structure (2021, background): “That is, rather than seeking explanations only post-hoc, in an attempt to help humans understand the system (e.g. Chen et al., 2018; Topin and Veloso, 2019; Xie et al., 2020), these works use explanations to help the system understand the task (cf. Santoro et al., 2021).”
Tell me why! Explanations support learning relational and causal structure (2021, background): “That is, rather than seeking explanations post-hoc, to help humans understand the system (e.g., Chen et al., 2018; Topin & Veloso, 2019; Xie et al., 2020), these works use explanations to help the system understand the task (cf. Santoro et al., 2021).”