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Starts 4 July 2025 18:13

Ends 4 July 2025

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AI for Safety Critical Control

Explore the essential theoretical foundations of AI within safety-critical control systems in this insightful presentation by Claire Tomlin from UC Berkeley. This course emphasizes the importance of trust and reliability in environments where high-risk applications are managed using artificial intelligence. Hosted on YouTube, this session is p.
Simons Institute via YouTube

Simons Institute

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47 minutes

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Overview

Explore the essential theoretical foundations of AI within safety-critical control systems in this insightful presentation by Claire Tomlin from UC Berkeley. This course emphasizes the importance of trust and reliability in environments where high-risk applications are managed using artificial intelligence.

Hosted on YouTube, this session is perfect for those interested in understanding the intersection of AI and secure control mechanisms.

Learn from one of the leading experts in the field and enhance your knowledge in creating trustworthy AI-driven solutions.

Categories:

Artificial Intelligence Courses, Computer Science Courses

Syllabus

  • Introduction to AI for Safety Critical Control
  • Overview of safety-critical systems and their importance
    Introduction to trustworthiness in AI-driven applications
  • Theoretical Foundations
  • Basics of control systems and AI intersections
    Introduction to dynamical systems
    Stability and safety in control systems
  • AI Techniques in Control
  • Machine learning methods for control systems
    Reinforcement learning in safety-critical environments
    Model predictive control using AI
  • Trustworthiness and Reliability
  • Defining trustworthiness in AI
    Verifiable AI methods
    Assurance cases and argumentation frameworks
  • Risk Analysis and Management
  • Risk assessment techniques in AI control systems
    Mitigation strategies for AI-induced risks
  • Human-AI Interaction
  • Human factors in AI control loop
    Designing for human oversight and intervention
  • Applications in High-Risk Sectors
  • AI in aerospace and automotive systems
    AI-driven medical devices
    Robotics and automation in safety-critical environments
  • Case Studies and Real-World Examples
  • Success stories and lessons learned
    Failures and their implications for AI trustworthiness
  • Future Trends and Research Directions
  • Emerging techniques and technologies
    Policy and ethical considerations in AI safety
  • Course Conclusion
  • Summary of key learning points
    Final project presentations and discussions
  • Additional Resources
  • Recommended readings
    Online tools and platforms for further learning

Subjects

Computer Science