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Seven Languages, Three Weeks, One Word
Chasing one word across seven languages for three weeks. Arabic, Urdu, German, Turkish, French, Greek, English. I read English, but high school French and Duolingo Spanish weren’t going to help.…
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Controlling AI-Generated Evidentiary Slop
The Controls Don’t Scale Down. Only the Effort Does. Four checks for anyone publishing with AI help Not all slop is the same “AI slop” has turned into a catch-all, and the catch-all is costing us something useful. Three different problems are hiding under one label. First, some posts are… Read ⇢
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What My Research Framework Caught: Seven Errors, None Published
Richard E. Rudd reflects on his AI-assisted research methodology applied to contested religious texts. He highlights how certain hypotheses can lead to errors, emphasizing the importance of verifying primary sources to avoid fabrication. The project revealed errors, but ultimately underscored the method’s effectiveness in producing findings through careful verification rather… Read ⇢
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Governance by Design: What Twenty Years in Regulated IT Taught Me About AI-Assisted Research
The post emphasizes that the choice of AI tools is secondary to ensuring the standard of work produced. Governance structures, essential in regulated industries, are vital for trust in AI-generated outputs. It advocates for multi-model verification to reduce biases and errors while maintaining human oversight, arguing for a robust framework… Read ⇢
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Four Platforms, One Standard: Why Serious AI-Assisted Research Needs More Than a Better Prompt
AI models frequently produce errors, leading to issues like “prompt brittleness” and “delusional spiraling.” A single model’s output can shape users’ beliefs without independent validation. To enhance accuracy, multi-platform AI verification is proposed, allowing different models to audit each other. This methodology aims to uphold rigorous research standards across fields. Read ⇢
