Ted Chiang: LLM output can inadvertently fool users

By PulseAugur Editorial · [1 sources] · 2026-06-04 02:33

Author Ted Chiang distinguishes deepfake photos from AI-generated conversations by highlighting intent. He notes that deepfake creators aim to deceive, while users of large language models may unintentionally fool themselves with the output. Chiang suggests that the perceived intelligence or consciousness in LLM interactions often stems from user projection rather than genuine AI sentience. AI

IMPACT Highlights the potential for users to misinterpret AI output, emphasizing the need for critical engagement with generative AI.

RANK_REASON Opinion piece by a named, credible author on AI capabilities.

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COVERAGE [1]

Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] · 2026-06-04 02:33

# TedChiang : "The primary difference between deepfake photos and # LLM conversations is that the people who generate the former are deliberately trying to fool

# TedChiang : "The primary difference between deepfake photos and # LLM conversations is that the people who generate the former are deliberately trying to fool others, and many of the people who elicit the latter from # LLMs have inadvertently fooled themselves." # AI # GenAI # …

LINKS theatlantic.com/…/687378

COVERAGE [1]

# TedChiang : "The primary difference between deepfake photos and # LLM conversations is that the people who generate the former are deliberately trying to fool

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