Definition
AI alignment addresses the challenge of making artificial intelligence systems robustly do what their users intend, rather than what they literally specify. It involves technical methods to ensure that powerful AI models remain beneficial, safe, and controllable as they become more capable. Key aspects include value learning, interpretability, and robustness against adversarial attacks, aiming to prevent unintended harmful consequences from misaligned objectives.
Summary
The field of study focused on ensuring AI systems behave in accordance with human values and intentions.
Key Concepts
- value learning
- beneficial behavior
- control problem
- interpretability
Use Cases
- Safety research in large language models
- Developing reward functions for RLHF
- Ethical guideline implementation