Scaling Test Time Compute to Multi-Agent Civilizations — Noam Brown, OpenAI Introduction and Background 00:05
The podcast hosts introduce Noam Brown from OpenAI, highlighting his recent achievement of winning the World Diplomacy Championship with the bot Cicero.
Brown discusses how working on Cicero enhanced his understanding of the game Diplomacy and contributed to his personal success in tournaments.
Insights on Cicero and Diplomacy 01:36
Brown explains the challenges of debugging AI behavior in games and how the bot's unexpected actions sometimes provided learning opportunities.
He notes that the current language models have significantly improved since Cicero's launch, complicating the detection of AI in games.
AI Safety and Control 03:57
Brown addresses concerns from the AI safety community regarding Cicero, emphasizing its controllability and clear action conditioning.
He expresses interest in seeing how newer models perform in Diplomacy as benchmarks to evaluate their capabilities.
Progress in AI Models 05:27
He discusses the trajectory of OpenAI's models and their consistent progress, highlighting the emergence of agentic behavior in newer versions.
Brown shares his positive experiences using the latest models in daily tasks, especially for deep research.
Deep Research and Model Performance 07:20
Brown argues against the perception that AI struggles in less quantifiable domains, citing successful applications in deep research.
He asserts that users can differentiate between varying quality levels of research outputs generated by AI.
Reasoning Paradigms in AI 09:00
He discusses the importance of pre-trained model capabilities for benefiting from advanced reasoning techniques.
Brown compares AI's reasoning capabilities with human cognitive functions, suggesting that a certain level of intelligence is necessary for advanced reasoning.
AI and Visual Reasoning 11:03
The conversation shifts to the effectiveness of AI in visual reasoning tasks, indicating some tasks benefit more from system-level thinking than others.
Harnesses and Model Efficiency 14:00
Brown discusses the concept of "harnesses" in AI, advocating for minimizing their necessity as models improve.
He emphasizes the need for AI to check the legality of moves in games without relying on additional tools or crutches.
Multi-Agent Systems and Future Directions 41:44
Brown describes ongoing projects related to multi-agent systems and the ambition of scaling test time compute to handle more complex problems.
He reflects on the potential of AI civilizations arising from cooperative and competitive interactions among numerous AIs.
Closing Thoughts 73:04
Brown concludes by highlighting the rapid evolution of AI capabilities and the need for researchers to remain adaptable amid fast progress.
He suggests that future AI developments may lead to significant advancements in a variety of remote working tasks beyond software engineering.