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

(00:36:02) Sponsors: Oracle Cloud Infrastructure (OCI) | Weights & Biases RAG++

(00:38:20) AI Myopia

(00:43:37) Optimizing Loss

(00:47:59) Optimizing Win Rate

(00:50:24) Best of K Analysis

(01:02:26) Best of K Limitations

(01:09:04) Agent Interaction Modalities

(01:12:34) Analyzing Benchmark Results

(01:17:16) Model Performance Differences

(01:22:49) Elicitation and Scaffolding

(01:27:08) Context Window & Best of K

(01:35:17) Reward Hacking & Bad Behavior

(01:43:47) Future Directions & Hiring

(01:46:20) Outro

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From Teacher To Writer: Ashley Hickson-Lovence