Week 13 Summary: Integration, limits, and reflection

Cross-Book Summary

1. Explainability: Opening the Black Box

• Beyond Prediction: “Why” builds industrial trust over just “what”.
• Sensitivity Analysis: Perturb inputs to find primary process drivers.
• Levels of Explanation: Tailor explanations for managers, experts, and data scientists.

2. Causality and Semantics

• Causal Process Chains: Shift from anomaly detection to early prediction.
• Ontologies: Digitize semantic meaning (e.g., mapping variables to physical concepts) to enable algorithmic reasoning.

3. Limits of AI in Materials Science

• Data Bias: Models trained only on “successes” cannot predict failures.
• AI Hallucinations: Large models may generate physically impossible patterns.
• Expert’s Role: AI automates analysis, but human experts define questions and interpret final truths.

90-Minute Lecture Strategy

Part 1: Course Synthesis

• Recap: Signal formation to PINNs.
• AI-driven materials lifecycle.

Part 2: Explainable ML

• The Black Box problem.
• Sensitivity analysis (Perturbation theory).
• Feature importance (SHAP/LIME).

Part 3: Causality & Semantics

• Causal graphs (Cause → Mechanism → Effect).
• Detection vs. Prediction.
• Materials Ontologies.

Part 4: Ethics and Limits

• Representation and “Success” bias.
• Dangers of over-extrapolation.
• Environmental cost vs. PINN efficiency.

Part 5: Final Outlook

• Self-driving labs.
• Sustainable AI-driven discovery.

Quarto Website Update (Summary)

Summary for ML-PC Week 14:
- Reflects on Explainability and Sensitivity Analysis to open black-box models. - Discusses Causality in process chains and Ontologies for semantic reasoning. - Critically assesses Data Bias and physical AI hallucinations. - Examines the evolving partnership between autonomous algorithms and human experts.