Supervised learning involves training neural networks with examples, where the network learns by being provided with labeled data, such as images of cats and dogs with their corresponding labels.

Supervised learning is also useful in automated visual inspection, such as identifying defects in manufactured products.

The era of supervised learning relied heavily on human-labeled data, but recent advancements allow us to train models without requiring explicit human input, unlocking new possibilities.

Neural networks are now effective because we can train them to solve various supervised learning problems, mapping examples to labels and outperforming other methods.

In self-driving cars, supervised learning can help label the positions of other vehicles based on sensor readings.

Self-supervised learning aims to reduce human supervision by allowing machines to learn from unannotated data, which has been successful in natural language processing and is increasingly applied in computer vision.

The most lucrative application of supervised learning I've worked on is online advertising, where we can predict which ads users are likely to click on.

For supervised tasks with clearly defined inputs and outputs, having sufficient training data is crucial for success, and the amount of computational power required varies by task.

The distinction between supervised learning and generative models is crucial; generative models focus on creating new content rather than merely identifying or predicting existing data.