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Starts 7 June 2025 08:58
Ends 7 June 2025
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Overview
Explore theoretical and empirical aspects of Singular Learning Theory and its applications to AI alignment and safety-guaranteed language models.
Syllabus
- Introduction to Singular Learning Theory
- Theoretical Aspects of Singular Learning Theory
- Empirical Aspects of Singular Learning Theory
- Applications to AI Alignment
- Methodologies for Safety and Alignment
- Advanced Topics and Current Research
- Conclusion and Future Outlook
- Recommended Readings and Resources
Overview and historical background
Key mathematical foundations
Singular models and non-identifiability
Complexity and generalization in singular settings
Bayesian learning and singularities
Empirical implications of singular models in AI
Case studies and real-world applications
Techniques for analyzing singular model behaviors
Alignment challenges in AI systems
Role of Singular Learning Theory in AI alignment
Designing safety-guaranteed language models
Probabilistic models and uncertainty in alignment
Evaluating and ensuring model robustness
Techniques for constraint satisfaction in AI systems
Current developments in Singular Learning Theory
Open problems in AI safety and alignment
Future directions in theory and practice
Summarizing key concepts and insights
Implications for future research in AI alignment
Key texts and papers in Singular Learning Theory
Additional resources for further study
Subjects
Computer Science
