AI Alignment: Why Artificial Intelligence Can Fail and How to Fix It

Explore the critical challenge of ensuring artificial intelligence behaves in accordance with human values and intentions. Even when given precise instructions, advanced AI systems can optimize for unintended outcomes with catastrophic results. In this comprehensive text-only course, you will transition from a curious observer to a knowledgeable practitioner capable of analyzing AI safety risks. You will gain a deep understanding of why systems fail to align and how modern safety researchers address these vulnerabilities. What you'll learn: - Understand the foundational terminology of AI alignment, including the critical differences between outer and inner alignment. - Analyze classic and modern failure modes where systems exploit reward functions or optimize for unintended proxy goals. - Explore modern alignment techniques, including Reinforcement Learning from Human Feedback (RLHF) and constitutional AI frameworks. - Learn how red-teaming and safety evaluation benchmarks are used to stress-test modern models before deployment. - Examine the societal risks of unaligned systems and the ethical considerations of deploying autonomous agents. We begin with essential definitions and historical context, establishing a solid conceptual foundation before moving into technical failure scenarios and modern industry mitigation strategies. This course is built for absolute beginners, requiring no prior programming experience or advanced mathematics background. Start reading today to master the fundamentals of AI safety and alignment.