"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

Can AIs do AI R&D? Reviewing REBench Results with Neev Parikh of METR

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Dec 21, 2024
Guest
Niamh Parikh
Niamh Parikh, a member of the technical staff at METR, discusses the innovative REBench evaluation framework designed for assessing AI systems' real-world research capabilities. The chat dives into how AI models like Claude 3.5 and GPT-4 perform in tasks ranging from optimizing GPU kernels to tuning language models. They explore the nuances of AI versus human problem-solving approaches, the challenges of benchmarking, and the impacts of AI performance on future research. Insights on the AI R&D capabilities and the need for effective evaluation metrics are also covered.
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INSIGHT

REBench's Novel Evaluation

  • REBench pushes AI models beyond simple pattern matching by requiring reasoning over unfamiliar problems.
  • It necessitates effective tool use and coherent long-term planning, unlike traditional benchmarks.
INSIGHT

Human vs. AI Learning Curves

  • Humans and AIs exhibit different learning curves in REBench: humans start slow but improve steadily, while AIs progress quickly initially but plateau.
  • This highlights the fundamental differences in how these systems approach problem-solving.
INSIGHT

AI Automating AI R&D

  • AI is approaching capability thresholds where it can significantly automate AI R&D, a potential intelligence explosion indicator.
  • Improved prompting and scaffolding, along with core model advancements, suggest further progress.
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