Challenges of AI in Scientific Modeling

The chapter explores the limitations of AI in modeling scientific data, using examples such as predicting future based on weight-spring motion curves and the influence of activation functions in neural networks. It discusses the challenges in approximating complex functions like sine waves and the importance of broad training data for more accurate predictions. The chapter also delves into using AI to predict cellular automaton behavior, highlighting both successes and limitations in capturing complex patterns.