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This webinar perfectly captures the 'AI paradox'-the irony of using energy-hungry algorithms to solve climate issues. This is precisely why we've focused on a model-based Machine Learning approach at Dyseco.io. Unlike traditional AI that requires massive, fossil-fuel-intensive processing power, our targeted algorithm is designed for efficiency. By precisely predicting the climate needs of collections and adjusting Temperature and Humidity Setpoints, we've drivne museums to reduce HVAC energy consumption, with results between 30% to 70%. Because the ML itself has such a negligible computational footprint compared to the massive energy it saves, the impact isn't just net-neutral; it's net-negative. It's a practical, low-carbon path for preventive conservation that respects both the art and the planet's resources. If interested check out www.dyseco.io