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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
2.2

Artificial Intelligence & Machine Learning

Artificial intelligence involves developing computer systems that can perform tasks that typically require human intelligence, from visual perception to decision-making. Machine learning is an approach to AI that involves developing models that can learn from data to perform tasks without being explicitly programmed.

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Review Questions

What is artificial intelligence (AI)?

Answer:

Artificial intelligence (AI) is an umbrella term that encapsulates the overarching idea of creating machines that perform tasks typically associated with human intelligence. It lacks a universal definition and is used to refer to many different—but related—ideas.

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Several conceptual categories have been developed and are often used to describe the capabilities of AI systems including, but not limited to, narrow AI, artificial general intelligence (AGI), human-level AI (HLAI), transformative AI (TAI), and superintelligence (ASI). For each of the following descriptions, name the type of AI to which it corresponds and provide an example.

  1. AI systems with impacts that are comparable to or more significant than the Industrial Revolution.
  2. AI systems that can learn and perform a wide range of tasks in many different areas.
  3. AI systems that significantly outclasses human performance in virtually all domains of interest.
  4. AI systems that are designed to perform specific tasks or solve particular problems within a specialized domain of expertise.
  5. AI systems that can perform approximately every task as well (and, in some definitions, at least as cheaply) as human workers.

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Answer:

Types of AI:

  1. Transformative AI (TAI)
  2. Artificial general intelligence (AGI)
  3. Superintelligence (ASI)
  4. Narrow AI
  5. Human-level artificial intelligence (HLAI)

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What is machine learning (ML)? What distinguishes ML from AI?

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Answer:

Machine learning (ML) is a subfield of AI that focuses on developing computer systems that can learn directly from data without following explicit pre-set instructions. ML is distinct from AI in that its systems use pattern recognition to extract information from and adapt to data.

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1.1
1.2
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