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.

Machine learning emphasizes the task of making machines learn from little initial knowledge to improve over time, with deep learning techniques utilizing neural networks being particularly effective in recent years.

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.

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.

We are also exploring AI's ability to learn quickly from prior experiences, similar to how humans adapt to new games or tasks.

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.

The optimizer learning approach has produced new update rules that outperform traditional human-designed optimizers, indicating a promising direction for future research.

We find that human feedback is often unreliable, so our self-evolving models are designed to learn from their own mistakes without needing human input.

The introduction of large neural networks has allowed us to solve complex problems in vision, speech, and language, shifting focus from traditional handcrafted approaches to machine learning.