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1. Overview of Catastrophic AI Risks
0.1
Preface
1.1
Overview of Catastrophic AI Risks
1.2
Malicious Use
1.3
AI Race
1.4
Organizational Risks
1.5
Rogue AIs
1.6
Discussion of Connections Between Risks
2. AI Fundamentals
2.1
AI Fundamentals
2.2
Artificial Intelligence & Machine Learning
2.3
Deep Learning
2.4
Scaling Laws
2.5
Conclusion
3. Single Agent Safety
3.1
Single Agent Safety
3.2
Monitoring
3.3
Robustness
3.4
Alignment
3.5
Systemic Safety
3.6
Safety and General Capabilities
3.7
Conclusion
4. Safety Engineering
4.1
Safety Engineering
4.2
Risk Decomposition
4.3
Nines of Reliability
4.4
Safe Design Principles
4.5
Component Failure Accident Models and Methods
4.6
Systemic Factors
4.7
Tail Events and Black Swans
4.8
Conclusion
5. Complex Systems
5.1
Complex Systems
5.2
Introduction to Complex Systems
5.3
Complex Systems for AI Safety
5.4
Conclusion
6. Beneficial AI and Machine Ethics
6.1
Beneficial AI and Machine Ethics
6.2
Law
6.3
Fairness
6.4
The Economic Engine
6.5
Wellbeing
6.6
Preferences
6.7
Happiness
6.8
Social Welfare Functions
6.9
Moral Uncertainty
7. Collective Action Problems
7.1
Collective Action Problems
7.2
Game Theory
7.3
Cooperation
7.4
Conflict
7.5
Evolutionary Pressures
7.6
Conclusion
8. Governance
8.1
Governance
8.2
Timelines
8.3
Distribution
8.4
Compute Governance
8.5
Corporate Governance
8.6
National Governance
8.7
International Governance
8.8
Conclusion
9. Appendices
9.1
Acknowledgements
10.1
App. A: Normative Ethics
11.1
App. B: Utility Functions
12.1
App. C: Reinforcement Learning
13.1
App. D: Long-Tailed and Thin-Tailed Distributions
14.1
App. E: Evolutionary Game Theory
15.1
App. F: Other Cooperation Mechanisms
16.1
App. G: Intrasystem Conflict Causes
All
Section
Appendix
7.5

Evolutionary Pressures

It seems likely that evolutionary pressures will apply to AI systems over coming years. By default, we should expect these to lead to the emergence of "selfish" AI systems optimised for ensuring their own reproduction rather than human wellbeing.

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Review Questions

Explain the idea of “Generalized Darwinism".

Answer:

Generalized Darwinism refers to adapting concepts from evolutionary theories of natural selection in the life sciences to describe competitive dynamics in other fields, such as the propagation of information.

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What are Lewontin's conditions and why do they suggest that AI development could be subject to evolutionary pressures?

Answer:

The process of AI development is likely to fulfill the three Lewontin’s conditions

  • 1) If there are multiple AIs, they will almost certainly vary from one another in many respects: e.g. due to diverse market demands or ML laws (that AI ensembles are more accurate than single AIs)
  • 2) Some the traits which vary between AIs will be retained by subsequent generations: e.g. due to learning or direct copying of desirable model behavior
  • 3) Some of the ways in which these AIs vary will cause them or their retained traits to be propagated differentially: e.g. more useful-seeming models or behaviors will be copied more than less useful-seeming ones
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Why might natural selection promote selfish AI behavior? Selfishness here refers to furthering one’s own propagation at the expense of others.

Answer:

On the level of an individual organism, individuals may behave altruistically under specific conditions such as genetic relatedness, where it can be evolutionarily advantageous to help organisms with similar genes. On the level of information, which is the level of selection relevant to AI systems, evolution by natural selection does not have a similar mechanism and will tend to produce selfish behaviour.

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0.1
1.1
1.2
1.3
1.4
1.5
1.6
2.1
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2.3
2.4
2.5
3.1
3.2
3.3
3.4
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3.6
3.7
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4.6
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4.8
5.1
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5.3
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9.1
10.1
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16.1
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