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Starts 6 June 2025 12:12

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Formal Models of Machine Teaching Without Collusion

Explore formal models of machine teaching without collusion in this theoretical talk on trustworthy AI by Sandra Zilles from the University of Regina.
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Simons Institute

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Overview

Explore formal models of machine teaching without collusion in this theoretical talk on trustworthy AI by Sandra Zilles from the University of Regina.

Syllabus

  • Introduction to Machine Teaching
  • Definitions and fundamental concepts
    Historical context and evolution
  • Formal Models of Machine Teaching
  • Key frameworks and methodologies
    Comparison with traditional machine learning models
  • Trust and Collusion in Machine Teaching
  • Defining trust in AI systems
    Identifying collusion risks and mitigation strategies
  • Non-Collusive Teaching Strategies
  • Designing trustworthy teaching models
    Case studies of successful non-collusive implementations
  • Mathematical Foundations
  • Algorithmic approaches to teaching without collusion
    Theoretical limits and capabilities
  • Practical Applications
  • Real-world examples of machine teaching
    Industry scenarios and potential impact
  • Future Directions in Machine Teaching
  • Emerging trends and research areas
    Ethical considerations and policy implications
  • Conclusion and Discussion
  • Review of key concepts
    Open forum for questions and future inquiry proposals

Subjects

Computer Science