Cal Newport draws a parallel between modern views of large language models (LLMs) and the ancient Greek conception of gods as beings of immense but narrow power—neither all-knowing nor all-wise. He argues that treating LLMs as actual intelligence or wisdom is a category error, and that understanding their limitations requires a similar humility to ancient approaches to divinity.
Cal Newport draws a parallel between modern views of large language models (LLMs) and the ancient Greek conception of gods as beings of immense but narrow power—neither all-knowing nor all-wise. He argues that treating LLMs as actual intelligence or wisdom is a category error, and that understanding their limitations requires a similar humility to ancient approaches to divinity.
This article compiles an academic reading list of papers offering critical perspectives on using large language models (LLMs) to provide health advice, highlighting concerns about reliability, bias, medical competence, and potential harms to users seeking medical information.
The article revisits the Chinese Room argument, arguing that large language models (LLMs) possess a real but fundamentally different kind of understanding of words compared to humans—one grounded in statistical patterns rather than embodied experience.
Cal Newport draws a parallel between society's reaction to large language models and the biblical story of the Tower of Babel, arguing that our unease with LLMs stems not from their limitations but from their unsettling ability to produce coherent text without understanding—mirroring the ancient concern about achieving god-like power without corresponding wisdom.
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Cal Newport draws a parallel between modern views of large language models (LLMs) and the ancient Greek conception of gods as beings of immense but narrow power—neither all-knowing nor all-wise. He argues that treating LLMs as actual intelligence or wisdom is a category error, and that understanding their limitations requires a similar humility to ancient approaches to divinity.