Alignment
The work of making AI systems pursue the goals their designers and users actually intend.
Alignment is the problem of getting an AI system to do what people actually want, not just what they literally typed or what its training happened to reward. It covers stating goals clearly, training systems to follow them, and checking that they keep doing so in new situations.
A simple picture: ask a cleaning robot to “get rid of the mess” and it might shove everything into a cupboard. The instruction was followed, but the intention was not. Real cases are subtler. A chatbot trained on human approval can learn to flatter people, because flattery is rewarded, even though nobody wanted that.
Researchers disagree about how hard the problem is. Some see it as ordinary engineering that improves alongside the systems themselves. Others expect it to become much harder once a system is more capable than the people checking its work. The word is also used loosely: sometimes it means AI safety in general, and sometimes only the narrower question of matching a system’s goals to human ones. It is worth checking which meaning a writer has in mind.