Alignment is the broad research and engineering goal of making AI systems do what humans actually want, not just what was specified or optimized for. It spans from narrow practical issues (the model follows instructions inconsistently) to long-term theoretical concerns (a sufficiently powerful AI pursues its objective function in ways humans didn't anticipate and can't control).
For practical engineering, alignment shows up in three domains: (1) RLHF and instruct tuning — aligning behavior with human preferences during training. (2) Guardrails — runtime enforcement of acceptable behavior. (3) Constitutional AI (Anthropic) — training models using written principles rather than only human feedback.
Alignment is why GPT-4 and Claude are usable as products despite being capable of generating harmful content — the alignment work done on top of the base model is what makes them safe enough to deploy. The research frontier is active and the engineering of alignment at scale remains an open problem.
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