LLMs require boundaries, mirroring family table dependencies

The author argues that the current focus on large language models (LLMs) overlooks crucial dependencies, comparing the complex infrastructure of machine rooms to the equally vital dependencies of a family table. They contend that LLMs, while powerful tools, can create a false sense of security and accountability due to their lack of evidence custody and potential for overclaiming. The piece emphasizes the need for boundaries and trust earned through rigorous testing rather than sheer volume or speed, drawing parallels to cybernetics and the foundational lessons of human connection and care. AI

IMPACT Highlights the need for ethical development and deployment of AI, emphasizing accountability and trust over raw capability.

RANK_REASON The item is an opinion piece discussing the nature and limitations of LLMs, drawing parallels to human systems and emphasizing ethical considerations.

Read on dev.to — LLM tag →

• Alianna J. Maren
• Fred Rogers
• generative pre-trained transformer
• Jax
• Norbert Wiener
• OpenAI
• SolutionWright
• Universal Natural Intelligence

• opinion
• other

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