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Starts 4 July 2025 21:41

Ends 4 July 2025

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Chaos Engineering Meets AI

Join us for an enlightening exploration where chaos engineering converges with artificial intelligence. This event promises to deliver valuable insights into cutting-edge topics such as reinforcement learning, the intricacies of transformer models and neural networks, as well as advances in AI-powered threat detection systems. Hosted by lead.
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Overview

Join us for an enlightening exploration where chaos engineering converges with artificial intelligence. This event promises to deliver valuable insights into cutting-edge topics such as reinforcement learning, the intricacies of transformer models and neural networks, as well as advances in AI-powered threat detection systems.

Hosted by leading experts in the field, this YouTube event is perfect for enthusiasts and professionals eager to expand their understanding of the dynamic and rapidly evolving landscape of AI and chaos engineering.

Syllabus

  • Introduction to Chaos Engineering
  • Definition and Principles
    Historical Context and Evolution
    Importance in Modern Systems
  • Fundamentals of Artificial Intelligence
  • Overview of AI Technologies
    Machine Learning Basics
    Introduction to Neural Networks
  • Chaos Engineering Applied to AI Systems
  • Benefits and Challenges
    Tools and Techniques
    Case Studies and Industry Examples
  • Reinforcement Learning in Chaotic Environments
  • Basics of Reinforcement Learning
    Applications in Dynamic Systems
    Experimentation and Testing with Chaos Engineering
  • Transformer Models and Their Robustness
  • Introduction to Transformer Architectures
    Vulnerabilities and Testing in Real-World Scenarios
    Improving Stability and Performance with Chaos Testing
  • Ensuring Resilience in Neural Networks
  • Common Failure Modes
    Strategies for Increasing Robustness
    Testing Neural Networks with Chaos Engineering
  • AI-Powered Threat Detection and Chaos Engineering
  • Overview of AI in Cybersecurity
    Stress Testing Threat Detection Algorithms
    Case Studies of AI in Dynamic, Adversarial Environments
  • Designing Chaos Experiments for AI Systems
  • Experiment Planning and Execution
    Measuring Outcomes and Improvements
    Iterative Enhancement Based on Findings
  • Ethical Considerations in Chaos Engineering and AI
  • Balancing Innovation with Safety
    Addressing Bias and Fairness
    Regulatory and Compliance Challenges
  • Course Review and Future Trends
  • Summary of Key Learnings
    Emerging Trends at the Intersection of Chaos and AI
    Discussion on Future Research Directions
  • Capstone Project
  • Designing and Implementing a Chaos Experiment on an AI System
    Presentations and Peer Feedback

Subjects

Computer Science