--target-resolution / --target-fps — adjust preprocessing quality
MIT investigators employed 41 distinct large language models—such as iterations of Claude, Gemini, and ChatGPT—to assess performance on over 11,000 text-centric activities tied to occupations defined by the Department of Labor. Professionals with relevant field experience then rated the AI-generated outputs. The objective was to determine how frequently an AI substitute could deliver work deemed satisfactory by a supervisor without requiring adjustments, and to measure its overall caliber.
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Established by the pivotal 1980 Bayh-Dole Act, this "technology transfer" framework was instituted to promote such licensing. Before its enactment, universities had minimal motivation to patent or license findings from federally supported research, as the government held the intellectual property rights. Essentially, public funds were channeled into scientific inquiry, leading to significant breakthroughs in university laboratories, yet these discoveries seldom translated into practical applications for citizens.