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

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.

Supervised learning is effective for labeling and recognizing data, such as identifying whether an email is spam or not.

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 dream of self-supervised learning is for AI systems to learn from vast amounts of unstructured data, similar to how children learn from minimal examples after being exposed to extensive information.

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.

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

The last decade focused on large-scale supervised learning, where increasing data and model size led to improved performance.