ALAN SCOTT ENCINAS CASE STUDY · LUNARSITE (FULL PIPELINE) LunarSite: An end-to-end ML pipeline for lunar south pole landing site selection Sim-to-real terrain segmentation, fine-tuned crater detection, PSR-aware XGBoost site scoring, and calibrated uncertainty — validated against NASA’s Artemis III…
Building real-time fraud detection for financial systems: 92.6% accuracy with just 13 features and sub-millisecond inference. Through aggressive feature selection (435 features → 13), logistic regression proved that production-ready ML doesn't require complexity—it requires smart fundamentals and clear constraints.
How a flower classification model became a medical diagnostic tool for underserved communities. Using transfer learning with InceptionV3, we built a pneumonia detection system that achieved 73% validation accuracy and increased screening throughput by 340% at UCI Medical's community health center. Real deployment, real impact, real constraints.
AI today can write code, compose music, and even fly drones—but it still can't truly think. It reacts, predicts, and imitates. But it doesn't perceive. That's the gap between the AI we know and the intelligence that's coming next: Cognitive AI.
The internet is becoming toxic and AI is drinking from the stream. When Meta removed professional fact-checking, it didn't just change how people consume information—it altered how machines learn. Data poisoning, model collapse, and what it means for AI reliability.
