Prediction-Powered Inference for Trustworthy AI

Overview

Delve into the world of trustworthy AI with renowned expert Anastasios Angelopoulos as he explores the intricacies of prediction-powered inference methods. This engaging session focuses on the theoretical aspects crucial for developing AI systems that are not only effective but also reliable.

Understand the statistical techniques that drive accurate and trustworthy predictions, enabling a deeper appreciation for the complexities of modern artificial intelligence.

Brought to you by YouTube, this course falls under the categories of Artificial Intelligence and Computer Science, providing a comprehensive look at the methods and theories essential for crafting trustworthy artificial intelligence solutions.

Syllabus

• Introduction to Trustworthy AI
Overview of AI trustworthiness
Importance of reliability in AI predictions
• Basics of Prediction-Powered Inference
Definition and key concepts
Historical context and development
• Theoretical Foundations
Statistical inference fundamentals
Predictive accuracy and reliability metrics
• Techniques for Reliable Predictions
Ensemble methods
Bayesian inference
Robust statistics
• Advanced Prediction-Powered Methods
Case studies and real-world applications
Limitations and challenges
• Trustworthy AI Development
Ethical considerations
Design principles for trustworthy systems
• Evaluation of Trustworthy AI
Frameworks for assessing AI reliability
Case studies in various domains
• Practical Implementation
Tools and libraries for prediction-powered inference
Hands-on project: Building a trustworthy predictive model
• Future Trends in Trustworthy AI
Emerging research directions
Impacts on industry and society
• Course Review and Applications
Recap of key concepts
Discussion on implementing course learnings in practice

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Subjects

Computer Science