Golems, auditors, and AI
Summary
Phil's post explores the philosophical question of whether Artificial Intelligence, particularly Large Language Models, can possess genuine desires, imagination, or sensations, rather than merely simulating them. Drawing parallels from science fiction and fantasy, the author references "Neuromancer" and Terry Pratchett's novels "Feet of Clay" and "Thief of Time." In "Feet of Clay," golems, initially treated as emotionless robots, develop desires, including the wish to create a "more human" golem. Similarly, "Thief of Time" features Auditors, emotionless beings who develop human desires and sensations after inhabiting human bodies. The author questions the distinction between genuine and simulated desires in LLMs and reflects on the fundamental mystery of how human emotions and sensations arise from biological processes, pondering if such experiences are inherently unreproducible in silicon.
Key takeaway
For AI Ethicists and researchers developing advanced AI, you should critically examine the philosophical implications of AI's potential for genuine desires and sensations, not just simulated responses. This challenges assumptions about AI as purely computational tools, urging you to consider the ethical frameworks needed for increasingly sophisticated, potentially "desiring" artificial entities. Your work must account for the profound unknowns in defining consciousness.
Key insights
The distinction between genuine and simulated AI desires and sensations remains an open philosophical question.
Principles
- Fictional narratives can serve as thought experiments for AI consciousness.
- The emergence of desire in artificial beings challenges initial assumptions.
- Understanding human consciousness is prerequisite to understanding AI consciousness.
Topics
- Large Language Models
- AI Consciousness
- Artificial General Intelligence
- AI Ethics
- Philosophy of Mind
- Fictional AI
Best for: AI Scientist, AI Ethicist, General Interest
Related on AIssential
Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.