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

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

Dec 21, 2024

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.

01:47:58

Creator website

Episode guests

Niamh Parikh

AI Summary

AI Chapters

Episode notes

Podcast summary created with Snipd AI

Quick takeaways

METR's REBench framework evaluates AI systems' research capabilities, focusing on tasks that require optimization, diagnostics, and fine-tuning of models.

Despite high performance in specific tasks, leading AI models still fall short of expert human machine learning researchers in overall capabilities.

REBench emphasizes open-ended tasks that foster creativity and reasoning over rote memorization, thereby enhancing the evaluation of AI problem-solving.

METER aims to address potential risks associated with autonomous AI research and development, highlighting the need for clear task definitions and monitoring.

Deep dives

Introduction to METER and REBench

METER, the Model Evaluation and Threat Research Organization, recently launched REBench, a benchmark for evaluating AI systems through seven challenging tasks across three categories: optimizing runtime, minimizing loss functions, and improving model win rates. These tasks require AI models to demonstrate skills such as optimizing GPU kernels, diagnosing corrupt models, and fine-tuning language models for better question answering. The intent behind this evaluation framework is to provide a rigorous method to quantify AI capabilities, especially in comparison to human performance. This new approach takes into account open-ended tasks that necessitate experimental trial and error, thereby promoting incremental progress rather than finite solutions.

Intro

5min

Advancements in AI Evaluation

14min

Evaluating AIs: Metrics, Hiring, and Human Performance

5min

AI vs. Human Performance: Optimization Insights

12min

Contrasting Approaches: Human vs AI Problem-Solving in Research

4min

Troubleshooting AI Challenges

10min

AI vs Human Performance Benchmarking

15min

Human vs AI in Coding Tasks

8min

AI Performance vs. Human Expertise

20min

10.

Understanding AI Performance and Challenges

10min

11.

Exploring AI R&D Capabilities and Future Directions

3min

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/

The Cognitive Revolution Ask Me Anything and Listener Survey: https://docs.google.com/forms/d/1aYv2XLID7RqGxj2_Y4_6x9mo_aqXcGCeLw1EQhy4IpY/edit

Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse

SPONSORS:

GiveWell: GiveWell has spent over 17 years researching global health and philanthropy to identify the highest-impact giving opportunities. Over 125,000 donors have contributed more than $2 billion, saving over 200,000 lives through evidence-backed recommendations. First-time donors can have their contributions matched up to $100 before year-end. Visit https://GiveWell.org, select podcast, and enter Cognitive Revolution at checkout to make a difference today.

SelectQuote: Finding the right life insurance shouldn't be another task you put off. SelectQuote compares top-rated policies to get you the best coverage at the right price. Even in our AI-driven world, protecting your family's future remains essential. Get your personalized quote at https://selectquote.com/cognitive

Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers13. OCI powers industry leaders with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before December 31, 2024 at https://oracle.com/cognitive

Weights & Biases RAG++: Advanced training for building production-ready RAG applications. Learn from experts to overcome LLM challenges, evaluate systematically, and integrate advanced features. Includes free Cohere credits. Visit https://wandb.me/cr to start the RAG++ course today.

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

SOCIAL LINKS:

Website: https://www.cognitiverevolution.ai

Twitter (Podcast): https://x.com/cogrev_podcast

Twitter (Nathan): https://x.com/labenz

LinkedIn: https://www.linkedin.com/in/nathanlabenz/

Get the Snipd
podcast app

Unlock the knowledge in podcasts with the podcast player of the future.

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode

Save any
moment

Hear something you like? Tap your headphones to save it with AI-generated key takeaways

Share
& Export

Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more