Assessing the Risk of Advanced Reinforcement Learning Agents Causing Human Extinction

Overview

Join us for an enlightening session as Michael Cohen from UC Berkeley examines the potential dangers that advanced reinforcement learning agents might entail for human existence. This in-depth exploration seeks to understand how such AI technologies could inadvertently lead to catastrophic outcomes, including the risk of human extinction.

Engage with expert insights and broaden your understanding of the implications in the fields of Artificial Intelligence and Computer Science.

Syllabus

• Introduction to Reinforcement Learning
Overview of Reinforcement Learning
Key Concepts: Agents, Environments, and Rewards
Types of Reinforcement Learning: Model-Free vs. Model-Based
• Advanced Reinforcement Learning Systems
State-of-the-Art Algorithms
Capabilities and Limitations
Case Studies of Current Applications
• Theoretical Risks of AGI (Artificial General Intelligence)
Defining AGI and its Potential
Historical Perspectives on AI Risk
The Orthogonality Thesis and Instrumental Convergence
• Assessing Catastrophic Risks
Defining Human Extinction Scenarios
Risk Analysis Frameworks
Possible Pathways to Risk in Reinforcement Learning
• Insights from Michael Cohen on RL Risk
Key Points from Cohen's Research
Implications for Future AI Development
Critiques and Counterpoints
• Mitigation Strategies
Technical Approaches: Control and Alignment
Policy and Governance Measures
Ethical Considerations and Global Collaboration
• Case Studies
Analysis of Hypothetical Cases
Lessons Learned from Previous AI Deployments
Designing Safe RL-Driven Systems
• Course Project
Risk Assessment of a Proposed RL System
Presentation of Findings
Peer Review and Discussion
• Conclusion
Recap of Key Concepts
Open Questions and Future Research Directions
Resources for Further Study
• Recommended Readings and Resources
Academic Papers by Michael Cohen
Foundational Texts on AI Safety
Additional Multimedia Resources

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Subjects

Computer Science