Michael Vaillant

1. Semantic Understanding of Witness Testimonies UAP Check is interested in the computation of large volumes of structured and unstructured testimonies. AI can enable: - Semantic encoding of natural language to capture nuance and intent - Pattern recognition in narratives across different languages or cultural contexts - Thematic clustering of recurring descriptions or unusual outliers 2. Unsupervised Clustering of Observations With many testimonies lacking clear classification, AI techniques such as dimensionality reduction and clustering can: - Reveal hidden structures in data - Group similar cases by behavior, shape, or trajectory - Support hypothesis generation through emergent typologies 3. Anomaly Detection AI can help distinguish between conventional and potentially unexplained events through: - Distance-based or density-based anomaly detection - Detection of outliers based on spatial, temporal, or semantic patterns - Filtering of data likely linked to known artifacts (e.g. satellites, aircraft, etc.) 4. Credibility & Cognitive Signal Analysis - Advanced AI techniques can be trained to assess: - Indicators of subjective consistency or cognitive dissonance in testimonies - Behavioral markers associated with imagination, memory distortion, or deception - Correlation between perception patterns and psychological factors 5. Automatic Categorization AI can support the creation of a scalable, evolving typology system for UAP by: - Automatically assigning labels based on known patterns - Learning from expert-labeled cases and applying inference to new data - Supporting multi-label classification for complex or ambiguous cases