Human Compatible: Artificial Intelligence and the Problem of Control
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Mixed quality. Some useful content but inconsistent editorial standards. Claims should be verified.
Rating inherited from publication venue: Amazon
Written by UC Berkeley AI pioneer Stuart Russell, this book is one of the most influential mainstream introductions to AI alignment and the control problem, widely assigned in AI safety curricula and cited as a foundational text in the field.
Metadata
Summary
Stuart Russell's landmark book argues that the standard AI paradigm of building machines with fixed objectives is fundamentally flawed and proposes a new approach based on machines that are uncertain about human preferences and defer to humans. Russell introduces the 'assistance game' framework as a path to beneficial AI, making the case that aligning AI with human values is both technically feasible and existentially necessary.
Key Points
- •Critiques the standard AI model of optimizing fixed objectives, arguing it leads to machines that pursue goals misaligned with human welfare
- •Proposes a new framework where AI systems maintain uncertainty about human preferences and actively seek to learn them through interaction
- •Introduces 'assistance games' (inverse reward design) as a formal basis for building AI that is inherently corrigible and deferential to humans
- •Argues superintelligent AI poses genuine existential risk if built under current paradigms, accessible to a general audience
- •Calls for the AI research community to reorient around provably beneficial AI rather than simply more capable AI
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Why Alignment Might Be Hard | Argument | 69.0 |
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