Neural Networks Explained

Yoshua discusses the significance of scale in neural networks, emphasizing that larger models can better approximate complex problems, even when the underlying knowledge is minimal. He highlights the distinction between knowledge and inference, explaining how modern neural networks excel at approximating inference tasks, allowing them to effectively tackle novel questions based on past patterns.