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 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.

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.

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.

A small amount of human data can enable AI systems to generate their own goals, allowing for significant learning from experience.

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

AI systems can be tricked, similar to humans, and as we advance in unsupervised learning, we may develop systems that achieve a level of conceptual understanding.

Human data might provide a head start for AI, but sustainable progress will depend on reinforcement learning as a continuous source of experience.

We're entering the 'era of experience' where AI systems learn from their own interactions with the world, rather than relying solely on human data.