Building Adaptive and Resilient AI

Overview

Embark on a journey into the world of artificial intelligence with Zhenyi Wang from the University of Maryland. Discover innovative strategies and methodologies for creating more adaptive and resilient AI systems.

This insightful exploration will delve into the latest advancements and techniques aimed at enhancing the robustness and adaptability of AI technologies.

Whether you're a seasoned professional in computer science or a curious learner eager to understand the mechanisms of artificial intelligence, this event provides valuable insights into the future of AI innovation. Join us in exploring the forefront of AI development and gain a comprehensive understanding of how adaptive and resilient systems are shaping the world.

Don’t miss out on this opportunity to enhance your knowledge and skills in AI.

Tune in to this informative session available through YouTube, guided by an expert in the field.

Syllabus

• Introduction to Adaptive and Resilient AI
Overview of AI system challenges
Importance of adaptability and resilience in AI
• Fundamentals of Adaptive Systems
Definition and characteristics of adaptive AI
Techniques for designing adaptive systems
Case studies: Successful adaptive AI systems
• Building Resilient AI Systems
Understanding resilience in AI
Designing for fault tolerance and recovery
Tools and frameworks for building resilient AI
• Machine Learning for Adaptation and Resilience
Adaptive learning algorithms
Transfer learning and meta-learning techniques
Robust model training methods
• Handling Uncertainty and Variability
Managing noise and data variability
Strategies for dealing with incomplete or changing data
Predictive modeling under uncertain conditions
• Agile Development of AI Systems
Rapid prototyping for adaptive AI
Iterative development processes
Testing and validation practices for robustness
• Infrastructure and Deployment
Architectures for adaptive and resilient AI
Cloud and edge computing platforms
Scalable and distributed AI system design
• Evaluating and Monitoring AI Systems
Metrics for evaluating adaptability and resilience
Monitoring system performance and adaptability in real-time
Continuous improvement and learning strategies
• Ethical and Societal Implications
Addressing biases and fairness in adaptive AI
Impact of resilient AI on industry and society
Policies and frameworks for responsible AI deployment
• Case Studies and Real-World Applications
Analysis of adaptive and resilient AI in various sectors
Lessons learned from real-world implementations
• Future Directions and Research in Adaptive AI
Emerging trends and technologies
Open questions and areas for further exploration
• Course Summary and Conclusion
Review of key concepts
Final thoughts on building adaptive and resilient AI systems

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Subjects

Computer Science