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Woody de Kafou | March 1, 2026Large World Models Is the Inflection PointLarge World Models (LWMs) represent the next major AI paradigm shift — moving beyond text and image generation to simulate entire environments with spatial, physical, and multimodal understanding. CTOs should begin preparing now by identifying high-impact use cases, building richer data pipelines, and running small pilots, while staying clear-eyed about the significant compute costs and reliability challenges that come with the territory.Woody de Kafou | January 11, 2026AI for Orebody Knowledge & Uncertainty Reduction [Part 3 of 3] - Uncertainty Quantification in AI-Generated Orebody Models: From Monte Carlo to Bayesian Neural NetworksIn this article, we explore the uncertainty quantification challenge, compare modern ML methods with traditional geostatistics, and present a practical framework for integrating AI uncertainty into resource estimation and mine planning.Woody de Kafou | November 25, 2025AI for Orebody Knowledge & Uncertainty Reduction [Part 2 of 3] -Large World Models for Subsurface Intelligence: Teaching AI the Physics of Mineral SystemsIn this article, we explore what subsurface LWMs are, why traditional AI falls short for geological problems, and how mining companies can prepare for this paradigm shift.Woody de Kafou | October 17, 2025AI for Orebody Knowledge & Uncertainty Reduction [Part 1 of 3] -From Drill Holes to Digital Orebodies: How Generative AI Fills the Gaps Between Sparse SamplingIn this article, we explore how generative models are transforming the sparse sampling problem from a statistical challenge into a physics-informed simulation task.
Woody de Kafou
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