👉 The core issue with current mathematical approaches, especially in areas like machine learning and complex systems, lies in their over-reliance on linear or high-dimensional models that struggle to capture the intricate, non-linear relationships inherent in many real-world phenomena. While powerful for structured data, these models often fail to generalize effectively when faced with messy, noisy, or highly variable inputs—common in fields like climate science, economics, or biological systems. Additionally, the "black box" nature of many advanced models (e.g., deep neural networks) obscures their decision-making processes, making it hard to validate their outputs or adapt them to novel scenarios. This disconnect between theoretical elegance and practical robustness limits their utility, as they often require extensive tuning, fail to explain underlying mechanisms, and struggle with edge cases, hindering their ability to drive meaningful, reliable innovation.
