What question did this study set out to answer?

This paper aims to examine how meaning relates to artificial intelligence and large language models in terms of grounding and normativity.

August 13, 2026Open Access

Can Artificial Intelligence Possess Meaning Without the Pressure to Survive? A Relational Account of Meaning, Grounding, Normativity, and the Limits of Large Language Models

View Full Paper

Ask AI

Bookmark

Authors

Perspective explores how artificial agents may acquire meaning and significance, highlighting implications for normativity.

This paper aims to examine how meaning relates to artificial intelligence and large language models in terms of grounding and normativity.
Developed a relational account of meaning concerning systems' organization and goals.
Analyzed large language models and their learning processes in relation to human perception and embodiment.
Introduced the concept of meaning ownership to articulate responsibility in meaning attribution.
Demonstrated that while LLMs can reconstruct complex structures, they lack intrinsic normativity tied to biological survival.
Highlighted that artificial agents may gain operational significance through various processes, including embodiment and memory.
Raised questions about the potential for first-person meaning and consciousness in artificial intelligence.

Dongsheng Xiao (2026) studied this question.

View Full Paper

Ask AI

Bookmark

August 13, 2026Open Access

View Full Paper

Ask AI

Bookmark

Authors

Perspective explores how artificial agents may acquire meaning and significance, highlighting implications for normativity.

This paper aims to examine how meaning relates to artificial intelligence and large language models in terms of grounding and normativity.
Developed a relational account of meaning concerning systems' organization and goals.
Analyzed large language models and their learning processes in relation to human perception and embodiment.
Introduced the concept of meaning ownership to articulate responsibility in meaning attribution.
Demonstrated that while LLMs can reconstruct complex structures, they lack intrinsic normativity tied to biological survival.
Highlighted that artificial agents may gain operational significance through various processes, including embodiment and memory.
Raised questions about the potential for first-person meaning and consciousness in artificial intelligence.

Dongsheng Xiao (2026) studied this question.

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark