Machines must learn from human feedback from "summary" of Human Compatible by Stuart Russell
The key idea here is that machines must be designed to learn from human feedback. This is crucial because human values are complex and can't easily be programmed into machines in advance. Instead of trying to specify all possible situations and outcomes in advance, we need machines that can learn from feedback as they interact with the world. The reason is that our values and preferences are not fixed or universal; they can change over time, and they vary from person to person. So if we want machines to act in ways that are aligned with human values, they need to be able to learn from us. One approach to building machines that can learn from humans is reinforcement learning, where the machine receives feedback in the form of rewards or penalties based on its actions. This feedback allows the machine to learn what actions lead to positive outcomes and which ones lead to negative outcomes. By adjusting its actions in response to feedback, the machine can learn to achieve its goals in a way that is consistent with human values. Another approach is to have machines learn from demonstrations provided by humans. In this setting, a human demonstrates the desired behavior to the machine, which then tries to imitate it. By observing human behavior and learning from it, machines can acquire a rich understanding of human values and preferences. In both cases, the key idea is that machines must be designed to learn from human feedback in order to act in ways that are aligned with human values. This is a challenging task, as human values are complex and context-dependent, but it is essential if we want to ensure that machines act in ways that are beneficial to us. By incorporating human feedback into the learning process, we can create machines that are more capable of understanding and respecting human values.
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