Human vs AI in Coding Tasks | 8min 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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Human vs AI in Coding Tasks

This chapter explores the complexities of assessing human performance against AI in coding, referencing a study that incorporated internet and language model usage. It highlights challenges like capturing human thought processes compared to machine outputs, and suggests long-term data collection could benefit AI training. The discussion also addresses the interaction modalities between humans and AI, examining the limitations and future potential of mouse and keyboard-driven engagements with technology.

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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