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Starts 5 June 2025 22:32
Ends 5 June 2025
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Overview
Explore the development of trustworthy multimodal AI systems that can adapt continuously, presented by Jaehong Yoon from UNC Chapel Hill.
Syllabus
- Introduction to Multimodal AI Systems
- Trust in AI Systems
- Continual Learning in AI
- Adaptability in Multimodal AI
- Designing Trustworthy Multimodal AI Systems
- Multimodal Data Integration
- Advanced Topics in Multimodal AI
- Case Study: Trustworthy Multimodal AI in Action
- Workshops and Practical Sessions
- Conclusion and Future Challenges
Overview of Multimodal AI
Importance of Multimodal Integration
Defining Trustworthiness in AI
Ethical Considerations and AI Bias
Techniques for Ensuring AI System Reliability
Fundamentals of Continual Learning
Strategies for Lifelong Learning in AI Systems
Handling Catastrophic Forgetting
The Concept of Adaptability in AI
Case Studies of Adaptive Multimodal Systems
Principles of Trustworthy System Design
Verification and Validation Techniques
Data Fusion Techniques
Challenges in Multimodal Data Integration
Emerging Trends and Future Directions
Potential Real-World Applications
Analysis of a Real-World Multimodal AI System
Hands-on Projects with Multimodal Data
Building a Simple Trustworthy Multimodal AI System
Summarizing Key Insights
Open Questions and Areas for Further Research
Subjects
Computer Science
