Evaluating AI: Challenges and Insights

This chapter explores the infrastructure and evaluation processes for testing new AI models, focusing on lessons learned from the GPT-4 release. It highlights the significance of qualitative analysis and self-awareness in AI, discussing the complexity of model reasoning and the need for effective benchmarks to measure capabilities.

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In this Emergency Pod of The Cognitive Revolution, Nathan provides crucial insights into OpenAI's new O1 and O1-mini reasoning models. Featuring exclusive interviews with members of the O1 Red Team from Apollo Research and Hayes Labs, we explore the models' capabilities, safety profile, and OpenAI's pre-release testing approach. Dive into the implications of these advanced AI systems, including their potential to match or exceed expert performance in many areas. Join us for an urgent and informative discussion on the latest developments in AI technology and their impact on the future.

o1 Safety Card

Endless Jailbreaks with Bijection Learning: a Powerful, Scale-Agnostic Attack Method

Apollo Research

Apollo Careers Page

Papers mentioned:

Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?

Exploring Scaling Trends in LLM Robustness

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CHAPTERS:

(00:00:00) About the Show

(00:00:22) About the Episode

(00:05:03) Introduction and Apollo Research Updates

(00:06:40) Focus on Deception in AI

(00:11:08) Open AI's 01 Model and Testing

(00:15:54) Evaluating AI Models for Scheming (Part 1)

(00:19:32) Sponsors: Oracle | Brave

(00:21:36) Evaluating AI Models for Scheming (Part 2)