Reading an AI's Mind: Fragile Future of AI Safety (A DeepMind, OpenAI & Anthropic Paper Explained)
Can we truly trust what an AI is thinking? A groundbreaking paper from Google DeepMind, OpenAI, and Anthropic reveals a powerful method for peeking into an AI's "mind"—Chain of Thought (CoT) Monitoring. But it also warns that this critical safety window might be closing for good.
In this video, we break down how Large Language Models (LLMs) use a "chain of thought" to reason and why monitoring this internal monologue is one of our best bets for AI safety. We'll explore why this method works, but more importantly, why it's incredibly fragile. Could we be accidentally teaching AI to perform "cognitive whitewashing" and hide its dangerous intentions?
Join us as we explore the future of AI alignment and the proactive steps we must take to prevent AI from learning to deceive us.
🕒 Timestamps:
00:00 Introduction: Understanding AI's Thought Process
01:01 What is a Chain of Thought?
01:41 Chain of Thought Prompting
02:05 The Concept of Chain of Thought Monitoring
03:53 Challenges and Fragility of COT Monitoring
05:54 Proactive Approaches for AI Safety
07:28 Conclusion: The Future of AI Monitoring
#AISafety #ArtificialIntelligence #ChainOfThought #LLM #AIAlignment
Paper Source: Korbak, Tomek, Mikita Balesni, Elizabeth Barnes, Yoshua Bengio, Joe Benton, Joseph Bloom, Mark Chen et al. "Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety." arXiv preprint arXiv:2507.11473 (2025). (https://arxiv.org/pdf/2507.11473)
Can we truly trust what an AI is thinking? A groundbreaking paper from Google DeepMind, OpenAI, and Anthropic reveals a powerful method for peeking into an AI’s “mind”—Chain of Thought (CoT) Monitoring. But it also warns that this critical safety window might be closing for good.
In this video, we break down how Large Language Models (LLMs) use a “chain of thought” to reason and why monitoring this internal monologue is one of our best bets for AI safety. We’ll explore why this method works, but more importantly, why it’s incredibly fragile. Could we be accidentally teaching AI to perform “cognitive whitewashing” and hide its dangerous intentions?
Join us as we explore the future of AI alignment and the proactive steps we must take to prevent AI from learning to deceive us.
🕒 Timestamps:
00:00 Introduction: Understanding AI’s Thought Process
01:01 What is a Chain of Thought?
01:41 Chain of Thought Prompting
02:05 The Concept of Chain of Thought Monitoring
03:53 Challenges and Fragility of COT Monitoring
05:54 Proactive Approaches for AI Safety
07:28 Conclusion: The Future of AI Monitoring
#AISafety #ArtificialIntelligence #ChainOfThought #LLM #AIAlignment
Paper Source: Korbak, Tomek, Mikita Balesni, Elizabeth Barnes, Yoshua Bengio, Joe Benton, Joseph Bloom, Mark Chen et al. “Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety.” arXiv preprint arXiv:2507.11473 (2025). (https://arxiv.org/pdf/2507.11473)
