Contrasting Approaches: Human vs AI Problem-Solving in Research | 4min snip from "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

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

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

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Contrasting Approaches: Human vs AI Problem-Solving in Research

This chapter examines the differences in research methodologies between humans and AI models when tackling problems. It contrasts the slower, more reflective process of human problem-solving with the rapid yet sometimes repetitive approaches of AI in code generation.

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In this episode of The Cognitive Revolution, Nathan explores METR's groundbreaking REBench evaluation framework with Neev Parikh. We dive deep into how this new benchmark assesses AI systems' ability to perform real machine learning research tasks, from optimizing GPU kernels to fine-tuning language models. Join us for a fascinating discussion about the current capabilities of AI models like Claude 3.5 and GPT-4, and what their performance tells us about the trajectory of artificial intelligence development.

Check out METR's work:

blog post: https://metr.org/blog/2024-11-22-evaluating-r-d-capabilities-of-llms/

paper: https://metr.org/AI_R_D_Evaluation_Report.pdf

jobs: https://hiring.metr.org/

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CHAPTERS:

(00:00:00) Teaser

(00:01:04) About the Episode

(00:05:14) Introducing METR

(00:07:36) Specialization of AI Risk

(00:09:52) AI R&D vs. Autonomy

(00:12:41) Benchmark Design Choices

(00:16:04) Benchmark Design Principles (Part 1)

(00:18:54) Sponsors: GiveWell | SelectQuote

(00:21:44) Benchmark Design Principles (Part 2)

(00:22:35) AI vs. Human Evaluation

(00:26:55) Optimizing Runtimes