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.

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.

To achieve full self-driving, we need to recreate how humans drive, which involves using optical sensors and neural networks to interpret the road environment.

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.

The challenge of self-driving includes solving perception problems, such as recognizing objects and predicting their movements, even when they are occluded.

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.

Initially, I thought the lane-keeping problem for self-driving cars was too difficult to solve, but it turned out to be manageable with the right approach.

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