AI Safety Resources
Access our curated collection of resources to deepen your understanding of AI safety.
AI Safety Glossary
A comprehensive glossary of key terms and concepts in AI safety:
AI Alignment
The problem of ensuring that artificial intelligence systems act in accordance with human intentions and values.
Interpretability
The ability to understand and explain the decisions and internal workings of AI systems.
Robustness
The ability of AI systems to maintain reliable and safe behavior even when faced with unexpected inputs or situations.
Paperclip Maximizer
A thought experiment illustrating how an AI with seemingly harmless goals could cause catastrophic outcomes if not properly aligned with human values.
RLHF (Reinforcement Learning from Human Feedback)
A technique where AI systems learn from human preferences and feedback rather than from a predefined reward function.
Reading Lists
Curated reading lists for different levels of understanding:
Beginner Reading List
Essential readings for those new to AI safety:
- Superintelligence by Nick Bostrom
- Human Compatible by Stuart Russell
- The Alignment Problem by Brian Christian
- AI Safety Fundamentals Course Materials
Intermediate Reading List
Deeper dives into AI safety concepts:
- Concrete Problems in AI Safety (Amodei et al.)
- Risks from Learned Optimization (Hubinger et al.)
- AI Alignment: Why It's Hard & Where to Start (Yudkowsky)
- The Case for Taking AI Seriously as a Threat (Vox)
Advanced Reading List
Technical papers and research directions:
- Scalable Agent Alignment via Reward Modeling
- Mechanistic Interpretability Approaches
- Cooperative Inverse Reinforcement Learning
- Current Research in AI Alignment
Educational Materials
Additional resources to support your learning:
Study Guides
Structured guides to help you navigate AI safety concepts systematically.
Access GuidesExternal Resources
Links to other organizations, courses, and materials on AI safety.
Explore Resources