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.

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

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

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.

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.

Current AI systems excel in supervised learning, which is the only paradigm that has shown remarkable success in business applications, while other paradigms like unsupervised and reinforcement learning are still developing.

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.

General-purpose technologies like supervised learning and generative AI are valuable for a wide range of applications, and there is still much work to be done in identifying and executing concrete use cases.

In modern computer vision, there's a significant shift from geometry to machine learning, where algorithms learn to recognize and segment objects from vast amounts of image data.