Andrew Gordon Wilson

Professor Andrew Gordon Wilson from NYU highlights the misconceptions in AI, particularly around model complexity and the bias-variance trade-off. He challenges the traditional view that complexity leads to overfitting, arguing that larger models can actually prefer simpler functions. Wilson discusses the importance of inductive biases and how they can improve generalization. He shares insights on practical model construction, advocating for a blend of expressiveness and simplicity to enhance performance across different data scales.