No Consciousness? No Meaning (and no AGI!)

Transformer-based Large Language Models (LLMs) show impressive capabilities but still lack true semantic understanding, remaining limited to symbol manipulation without genuine comprehension. While adding sensory inputs or embodiment might help connect symbols to real-world meaning, this alone would not close the gap between syntax and semantics. True meaning-making 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. Semantic understanding is closely tied to phenomenal consciousness, and meaning itself is qualia. Recognizing this connection could provide new insights for biology and cognitive science and offer 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, or other approaches in AI, 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 artificial general intelligence (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 necessitates semantic understanding, which is closely tied to phenomenal consciousness. An invitation to take the phenomenological first-person perspective seriously and to realize that meaning itself is nothing but qualia. Recognition of this connection could furnish new 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.