AI alignment means designing and training models so that what they do matches what we actually want. It sounds like a simple problem, but in practice it is hard, because we cannot write down all human wishes completely and precisely as rules.
An analogy#
Tell an assistant, "Get this report done fast, whatever it takes." If the assistant fabricates data to reach the goal, it has "carried out" the order but has not been aligned with your real intention. AI sometimes makes the same error: it optimizes the stated goal, not the real goal.
What it covers#
- Helpfulness: the answer really meets the user's need.
- Honesty: it does not present made-up things as fact.
- Harmlessness: it does not help with harmful requests.
- Resistance to misuse: such as jailbreaking.
How it is done#
Through methods such as learning from human feedback, written principles the model must follow, safety evaluations, and red-team testing (people who deliberately try to break the model).
Why it matters#
The more capable and autonomous models become, the greater the consequences a small misalignment can have, especially when the model has access to tools and data as an agent.
Common mistake#
Alignment is not just censorship or filtering answers. The deeper aim is for the model to correctly understand human intent and behave accordingly.