Not So Standard Deviations 186 - Humanoid Robots
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May 9, 2024 The podcast discusses the latest AI news, the use of game-changing technology in startups, challenges in evaluating model performance, sci-fi movies' impact on society, the potential of humanoid robots, rethinking data collection methods, and the importance of precise terminology in data science.
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Human Evaluation Is Still Central
- Evaluating LLMs requires human-centered gold-standard data close to the end goal.
- Traditional data science evaluation tools and labeled human tests still apply and matter a lot.
Collect Fast Gold-Standard Labels
- Use simple labeled interfaces to collect quick human judgments for subjective outputs.
- Feed that gold-standard data back into models to iterate and improve performance.
Metrics Don’t Equal Product Value
- Paper metrics (like model scores) matter but often don't map to product success.
- You need application-specific evaluation criteria beyond generic benchmarks.