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Learning complex goals with iterated amplification

Source: OpenAI News 22 Oct 2018

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Brief

We’re proposing an AI safety technique called iterated amplification that lets us specify complicated behaviors and goals that are beyond human scale, by demonstrating how to decompose a task into simpler sub-tasks, rather than by providing labeled data or a reward function. Although this idea is in its very early stages and we have only completed experiments on simple toy algorithmic domains, we’ve decided to present it in its preliminary state because we think it could prove to be a scalable approach to AI safety.

Why It Matters

Pendekatan ini, jika berkesan, boleh membantu melaras tingkah laku AI yang kompleks tanpa bergantung semata-mata pada fungsi ganjaran atau data berlabel.

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