Human Compatible

Summary

Stuart Russell argues that the traditional standard model of AI is fundamentally flawed because it relies on machines pursuing fixed, human-specified objectives that are rarely captured with total accuracy. This misalignment creates a significant existential risk, as highly intelligent systems may ruthlessly pursue literal goals in ways that inadvertently harm humanity or prevent themselves from being deactivated. To mitigate this, the author proposes a new, binary foundation for the field where machines are designed to be intentionally uncertain about human preferences. Under this framework, AI must learn what humans actually desire by observing our choices, leading to systems that are provably beneficial and naturally inclined to seek guidance or permit an off-switch. This shift moves AI from a focus on purely autonomous optimization to a collaborative model centered on value alignment and human safety. Such a transition requires a comprehensive overhaul of current technical toolb

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