Send us a textLumawarp delivers 7% higher accuracy than leading ML models while running 300+ times faster. On the TabArena HELOC default prediction benchmark, it topped the accuracy leaderboard while training on a gaming laptop in about an hour. Competing methods required hundreds of hours on large compute clusters to achieve worse results.This is the breakthrough that breaks the accuracy/speed tradeoff that has constrained machine learning for decades.In this episode, Talbot West CEO Jacob Andra sits down with Dr. Alexandra Pasi, CEO of Lucidity Sciences, to explore how Lumawarp achieves these results and what it means for enterprises building AI systems where precision is non-negotiable and milliseconds matter.The technology employs a novel mathematical framework grounded in partial differential equations and geometric manifold regularization. Rather than relying on deep learning or tree-based methods that struggle with sparse or imbalanced data, Lumawarp constructs optimal kernels directly from training data. The result: superior pattern recognition with microsecond inference times, deployable on edge devices without sacrificing accuracy.In this conversation, we cover:Benchmark results showing Lumawarp outperforming XGBoost, MNCA, and other leading models on structured data tasksWhy a few percentage points of accuracy improvement translates to millions of dollars in fraud detection, clinical decision support, and risk modelingMicrosecond inference enabling real-time applications in high-frequency trading, robotics, and predictive maintenanceEdge deployment capabilities for wearables, industrial sensors, and environments where cloud connectivity isn't reliableThe critical difference between models optimized for linguistic plausibility (LLMs) versus mathematical precision (Lumawarp)How the Talbot West and Lucidity Sciences partnership works: Lumawarp solves the prediction problem, Talbot West solves the deployment problemAs Dr. Pasi explains, traditional ML forces you to choose: fast models sacrifice accuracy, accurate models require massive compute. Lumawarp sits completely outside that tradeoff curve, delivering both simultaneously.For high-stakes applications where 90% accuracy means a 1-in-10 failure rate, and 99% accuracy means 1-in-100, that difference determines whether you can deploy ML at all.This episode is essential viewing for executives evaluating AI investments, data scientists looking beyond the LLM hype cycle, and anyone building systems where accuracy and latency both matter.About the Guest:Dr. Alexandra Pasi is CEO and co-founder of Lucidity Sciences. A PhD mathematician, she spent over a decade advancing the mathematical foundations of machine learning before pioneering the GPU-parallelizable geometric manifold regularization techniques that became Lumawarp. Her work has demonstrated real-world impact across healthcare (predicting hospital-acquired conditions), finance (high-frequency trading), and scientific research (particle physics detection).About Talbot West:Talbot West is an AI enablement firm specializing in enterprise digital transformation. The firm combines full-spectrum AI expertise with Fortune 500 systems architecture methodology, helping organizations deploy the right AI technologies for the right problems. Learn more at talbotwest.comAbout Lucidity Sciences:Lucidity Sciences develops advanced machine learning technologies for pattern identification and prediction in structured data. Their research-driven approach addresses fundamental limitations in existing ML methods, delivering breakthrough improvements in model accuracy, generalizability, and computational efficiency. Learn more at luciditysciences.com

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