What Can Theory of Cryptography Tell Us About AI Safety

Overview

Unlock the potential of cryptographic theory in enhancing AI safety with this insightful content featuring Shafi Goldwasser. Discover how vital principles of cryptography can be harnessed to develop language models with innate safety guarantees.

Ideal for enthusiasts and professionals alike, this session bridges the gap between cutting-edge technology and security, provided by YouTube in collaboration with leading academic institutions.

Syllabus

• Introduction to Cryptographic Theory
Brief history and key concepts
Overview of modern cryptographic techniques
• Fundamentals of AI Safety
Definition and scope of AI safety
Current challenges in AI safety
• Intersection of Cryptography and AI Safety
Cryptographic principles applicable to AI models
Case studies where cryptography enhances AI safety
• Insights from Shafi Goldwasser
Key contributions to cryptographic theory
Application of Goldwasser's principles to AI safety
• Developing Safety-Guaranteed Language Models
Mechanisms for ensuring data privacy and integrity
Techniques for safeguarding model behavior
• Secure Multi-Party Computation (SMPC)
Principles of SMPC
Applications in AI to prevent data leakage
• Zero-Knowledge Proofs in AI
Introduction to zero-knowledge proofs
Practical applications to verify AI model’s claims without revealing internal data
• Differential Privacy in AI Systems
Concepts of differential privacy
Implementing differential privacy in training AI models
• Challenges and Limitations
Limitations of current cryptographic techniques in AI
Potential risks and how to mitigate them
• Future Directions
Emerging trends in cryptography for AI safety
Research opportunities and areas of development
• Course Conclusion and Takeaways
Recap of key lessons
Discussion on interdisciplinary approaches to AI safety

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Subjects

Computer Science