Neurosymbolic Synthesis for Trustworthy Machine Learning

Overview

Explore neurosymbolic synthesis techniques for building trustworthy machine learning systems with Osbert Bastani from the University of Pennsylvania.

Syllabus

• Introduction to Neurosymbolic Synthesis
Definition and Overview of Neurosymbolic Systems
Importance and Applications in Machine Learning
Key Challenges in Building Trustworthy AI
• Fundamentals of Symbolic AI
Logic and Reasoning Approaches
Knowledge Representation
Inference and Deduction Techniques
• Neural Networks and Deep Learning
Overview of Neural Network Architectures
Training Methods and Optimization
Interpretability and Explainability in Deep Learning
• Integration of Symbolic AI and Neural Networks
Approaches to Combining Symbolic and Sub-symbolic Methods
Hybrid Models Overview
Case Studies in Neurosymbolic AI
• Trust in Machine Learning Systems
Defining Trustworthiness in AI
Common Pitfalls and Risks
Assessing Trust in Neurosymbolic Systems
• Techniques for Enhancing Trust
Validation and Verification Methods
Bias Detection and Mitigation
Robustness and Safety Guarantees
• Tools and Frameworks for Neurosymbolic Synthesis
Overview of Current Tools and Platforms
Practical Implementations
Evaluation Metrics for Trustworthiness
• Case Studies and Applications
Real-World Applications in Various Domains
Success Stories and Lessons Learned
Challenges and Opportunities in Practice
• Ethical and Societal Considerations
Ethical Implications of Neurosymbolic AI
Regulatory and Compliance Issues
Future Directions in Ethical AI Development
• Future of Neurosymbolic Synthesis
Emerging Trends and Technologies
Research Frontiers
Long-term Vision for Trustworthy AI
• Course Review and Project
Summary of Key Concepts
Collaborative Project on a Neurosymbolic System
Presentation and Peer Review Sessions

Subjects

Computer Science