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Week 14 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.