Probabilistic Safety Guarantees Using Model Internals

Overview

Explore probabilistic safety guarantees for language models through analysis of model internals with Jacob Hilton from Alignment Research Center.

Syllabus

• Introduction to Probabilistic Safety
Overview of Safety in AI Systems
Understanding Probabilistic Guarantees
• Fundamentals of Model Internals
Architecture of Language Models
Key Components and Their Functions
• Analyzing Model Internals
Techniques for Internal Inspection
Tools and Software for Analysis
• Probabilistic Methods in AI Safety
Basics of Probability Theory
Application of Probabilistic Methods in AI
• Developing Safety Guarantees
Criteria for Safety in Language Models
Constructing Safety Guarantees using Probabilistic Approaches
• Case Studies and Practical Examples
Review of Past Research and Findings
Analysis of Real-world Language Model Scenarios
• Implementing Safety Frameworks
Designing Safety Mechanisms Based on Internals
Testing and Validating Safety Measures
• Evaluating Safety in Language Models
Metrics for Safety Assurance
Continuous Assessment and Improvement Strategies
• Tools and Resources
Software Libraries for Model Analysis
Datasets for Testing Safety Protocols
• Guest Lecture by Jacob Hilton
Insights from the Alignment Research Center
Q&A on Advanced Safety Topics
• Conclusion and Future Directions
Summary of Key Learnings
Future Challenges and Opportunities in AI Safety
• Final Project
Application of Course Concepts
Development of a Probabilistic Safety Framework for a Language Model

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Subjects

Computer Science