explainable AI

ai drilling optimization drilling regimes 68 percent speed a oil derrick lattice tower on a drill floor

AI Helps Identify Drilling Regimes With a Projected Potential Increase in Drilling Speed of up to 68%

A Skoltech study combining an LSTM model, SHAP explanations and the NSGA-III algorithm projects that better weight on bit, rotary speed and flow rate settings could raise the rate of penetration by up to 68.4% in one of three carbonate wells. We explain how the AI drilling optimization works, the results well by well, why a 68% faster speed means about 40% less drilling time, the study’s limits, and what its design offers industry beyond oil.

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nuclear reactor monitoring ai molten salt heat exchanger a manifold bar with five spigots

Reactor Monitoring: Powerful AI Catches Hidden Blockages

Argonne National Laboratory researchers have published a design that pairs distributed fibre-optic temperature sensing with explainable machine learning to detect coolant channels freezing shut inside a molten-salt reactor heat exchanger, while inlet and outlet readings still look normal. Eight classifiers were benchmarked on 25,704 simulated instances; XGBoost won with 36 missed plugs and an F1 of 0.96 at 60% flow reduction, falling to 0.51 at 20%. This article covers the sensor design, the class imbalance, the SHAP-plus-POSET explainability layer and the four caveats that stand between it and a licensed product.

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Ethical AI: Implementation Guide – A Business Leader's Playbook

Ethical AI: Implementation Guide – A Business Leader’s Playbook

Ethical AI has become one of the most important strategic priorities for organizations adopting artificial intelligence at scale. As AI systems increasingly influence business decisions, customer experiences, financial operations, healthcare services, software development, recruitment, cybersecurity, and public services, organizations must ensure these technologies operate responsibly, transparently, securely, and fairly. Successful AI adoption is no longer […]

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