No Qualia? No Meaning (and no AGI)!

Transformer-based Large Language Models (LLMs) still lack true semantic understanding despite impressive capabilities, as the gap between symbol manipulation (syntax) and deeper meaning (semantics) remains wide. While human feedback helps LLMs overcome some aspects of the symbol grounding problem, they struggle with common-sense reasoning and abstract thinking. Adding sensory inputs and embodying AI through sensorimotor integration would not alone close this gap; true meaning-making also requires subjective experience, which current AI lacks. The path to artificial general intelligence (AGI) must address the fundamental relationship between symbol manipulation and conscious experience, which is closely tied to semantic understanding. Recognizing this connection could provide new philosophical insights and more meaningful tests of intelligence than the Turing test.

Study at a glance

Abstract

The recent developments in artificial intelligence (AI), particularly in light of the impressive capabilities of transformer-based Large Language Models (LLMs), have reignited the discussion in cognitive science regarding whether computational devices could possess semantic understanding or whether they are merely mimicking human intelligence. Recent research has highlighted limitations in LLMs’ reasoning, suggesting that the gap between mere symbol manipulation (syntax) and deeper understanding (semantics) remains wide open. While LLMs overcome certain aspects of the symbol grounding problem through human feedback, they still lack true semantic understanding, struggling with common-sense reasoning and abstract thinking. This paper argues that while adding sensory inputs and embodying AI through sensorimotor integration with the environment might enhance its ability to connect symbols to real-world meaning, this alone would not close the gap between syntax and semantics. True meaning-making also requires a connection to subjective experience, which current AI lacks. The path to AGI must address the fundamental relationship between symbol manipulation, data processing, pattern matching, and probabilistic best guesses with true knowledge that requires conscious experience. A transition from AI to AGI can occur only if it possesses conscious experience, which is closely tied to semantic understanding. Recognition of this connection could furnish new philosophical insights into longstanding practical and philosophical questions for theories in biology and cognitive science and provide more meaningful tests of intelligence than the Turing test.