Dive into a 23-minute video exploring Kolmogorov-Arnold Networks (KAN), a potential alternative to traditional Multi-Layer Perceptrons (MLPs) that power modern AI systems like ChatGPT, LLAMA, and DALLE. Learn about the mathematical foundations of KAN architectures, including B-splines and complex polynomials, through detailed explanations and practical implementations. Explore key concepts like residual activation functions, spline grids, and fine-grained training approaches while understanding the computational complexity and interpretability aspects of KAN. Compare experimental results between KAN and MLP networks, examining their performance in continual learning scenarios and addressing catastrophic forgetting. Gain insights into choosing between KAN and MLP architectures for different applications, supported by demonstrations using a toy problem and comprehensive implementation examples from the official PyKAN repository.