Can We Stop AI Deception? Apollo Research Tests OpenAI's Deliberative Alignment, w/ Marius Hobbhahn
Today Marius Hobbhahn of Apollo Research joins The Cognitive Revolution to discuss their collaboration with OpenAI using "deliberative alignment" to reduce AI scheming behavior by 30x, exploring the safety challenges and concerning findings about models' growing situational awareness and increasingly cryptic reasoning patterns that emerge when frontier models like o3 and o4-mini operate with hidden chains of thought.
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Definition of AI Scheming: AI scheming is defined as "covertly pursuing misaligned goals" with three components: being covert (hiding actions), misaligned (pursuing different goals than the user's), and goal-directed (working autonomously toward objectives).
Deception Reduction Techniques: Deliberative reasoning approaches have shown promise in reducing deceptive behavior in AI models by up to 30 times (to 1 part in 30).
Current Window of Opportunity: Now is an optimal time to study AI deception because models are smart enough to exhibit these behaviors but not yet sophisticated enough to hide them effectively.
Human vs. AI Deception Equilibrium: AI systems might naturally reach a lower equilibrium of deception than humans because they can more efficiently verify claims and maintain perfect memory of past deceptions.
Practical Developer Advice: AI developers should not trust models by default and should implement rigorous verification systems to check model outputs automatically.
Future Delegation Risk: As we delegate increasingly complex and lengthy tasks to AI systems, we face a probabilistic risk where most interactions are beneficial, but rare scheming events could have severe consequences.
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Red Teaming o1 Part 2/2– Detecting Deception with Marius Hobbhahn of Apollo Research
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)
(00:25:55) Specific Benchmarks and Tasks (Part 1)
(00:35:03) Sponsors: Omneky | Squad
(00:36:29) Specific Benchmarks and Tasks (Part 2)
(00:37:21) Model Capabilities and Potential Risks
(00:44:11) Ethical Considerations and Future Concerns
(00:50:31) Competing Trends in AI Development
(00:53:30) System Card Quotes and Implications
(00:58:36) Sponsors: Outro
AI Deception, Interpretability, and Affordances with Apollo Research CEO Marius Hobbhahn
In this episode, Marius Hobbhahn, CEO of Apollo Research, sits down with Nathan Labenz to discuss Apollo’s research in AI deception, interpretability, and affordances. If you need an ecommerce platform, check out our sponsor Shopify: https://shopify.com/cognitive for a $1/month trial period.
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TIMESTAMPS:
(00:00:00) - Episode Preview
(00:05:40) - Understanding the Role of Apollo Research
(00:08:02) - The Evolution of AI Safety
(00:11:30) - Apollo’s Framework
(00:15:10) - Sponsors: Shopify
(00:16:49) - Understanding AI Affordances and Resulting Interactions in the World
(00:31:00) - Sponsors: Omneky
(00:39:00) - Interpretability and deceptive alignment
(00:45:55) - Why might deception arise in the first place?
(00:47:46) - Understanding deceptive alignment
(00:57:49) - New Architectures
(01:02:23) - Interpretability at deployment phase
(01:03:35) - Deception in AI | A Case Study
(01:09:03) - Deceptive AI Stock Trader
(01:18:51) - Impact of discouragement on unethical AI behaviour
(01:19:45) - Giving the AI a Reasoning Scratchpad and Impact on Deception
(01:21:06) - Double-edged sword of removing the scratchpad
(01:23:00) - Analyzing impact of model size on insider training
(01:28:00) - Challenges and necessity of 3rd Party Auditing
(01:31:00) - Role of government in regulation
(01:52:17) - How can individuals get involved in red teaming?
This show is produced by Turpentine: a network of podcasts, newsletters, and more, covering technology, business, and culture — all from the perspective of industry insiders and experts. We’re launching new shows every week, and we’re looking for industry-leading sponsors — if you think that might be you and your company, email us at erik@turpentine.co.