Ground Predictions in Real-World Physics

Many companies venturing into machine learning face challenges due to a lack of grounding in the fundamental physics governing real-world scenarios. This disconnect leads to unrealistic model outputs, such as video generation inaccuracies or erroneous climate forecasting, which can occur when models fail to recognize cause and effect relationships. Reliable predictions require a solid understanding of physics to avoid misleading results. Unlike more immediate corrections in video or short-term weather forecasts, climate models pose a significant challenge, as validation can take years or decades. Therefore, integrating known physical principles is essential to enhance model accuracy and maintain trust in their predictions.