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A Microsoft AI researcher built a simple neural network using goats in Age of Empires II to argue that if such a system can be considered conscious, then claims of AI consciousness in chatbots are equally absurd.
A Microsoft and York University paper argues that attributing human-like attributes to LLMs is problematic due to flawed experimental designs, using Age of Empires II as an analogy to highlight measurement issues.
This paper argues that attributing human-like attributes to large language models is problematic because similar claims could be made about simpler systems, such as an AI trained on Age of Empires II, and proposes a null assumption of non-uniqueness to avoid circular reasoning.
This paper argues that anthropomorphic attributes often ascribed to LLMs are not unique, demonstrating that simpler systems like Age of Empires II can exhibit similar perceived traits, and calls for explicit measurement criteria in AI behavior analysis.
Ted Chiang argues that large language models like Claude are not conscious, countering Anthropic's anthropomorphic framing of its AI and warning against conflating fluent text generation with sentience.
This philosophical paper argues that AI chatbot outputs are meaningful under standard theories of language, without requiring anthropomorphic assumptions about mental states or intentions.
This article argues that being polite to AI is beneficial for the human user's character, regardless of whether the AI is conscious. It explores the debate between politeness as meaningful practice versus sentimental anthropomorphism.
Armin Ronacher argues for replacing the term 'agent' with 'clanker' for LLM-based systems to emphasize they are tools, not responsible agents, and warns against anthropomorphizing AI.
A philosophical discussion questioning whether AI models truly 'understand' or if we are projecting human-like cognition onto pattern-matching systems, referencing Searle's Chinese Room, 'stochastic parrots', and GPT-4's performance.
An article exploring why four different AI models all chose the number 7 when asked to pick a number, highlighting potential biases in training data.