AI Sharing Circle

人工智能公平性是确保人工智能系统在设计、开发、部署和运行的整个生命周期中，能以公正、无偏见的方式对待所有个体和群体的跨学科领域。

元学习（Meta-Learning），或称为学习如何学习，是机器学习领域的重要分支，专注于开发能快速适应新任务的学习算法。

Artificial Intelligence Safety (AI Safety) is the cutting-edge interdisciplinary field of ensuring that AI systems, especially those that are increasingly powerful and autonomous, act reliably and predictably throughout their lifecycle in accordance with human intent, without harmful consequences.

Self-Supervised Learning (SSL) is an emerging learning paradigm in the field of machine learning, where the core idea is to automatically generate supervised signals from unlabeled data and train models to learn useful representations of the data.

Artificial Super Intelligence (ASI) is an intelligent system that exceeds human intelligence, with capabilities that surpass those of humans in all domains, including cognition, creativity, problem solving, and decision-making.

Transfer Learning (Transfer Learning) is an important branch in the field of machine learning, the core idea is to apply the knowledge learned from one task or domain to another related but different task or domain.

AI governance is a comprehensive framework covering technology, ethics, law, and society that effectively guides, manages, and oversees the entire lifecycle of AI systems-from design, development, deployment, and end use. The core goal is not to hinder technological innovation, but to ensure that the development and application of AI technologies begin...

Semi-supervised learning is an important branch in the field of machine learning, which uses a small amount of labeled data and a large amount of unlabeled data to co-train a model to improve the learning effect and generalization ability.

Unsupervised Learning (ULS) is an important branch of machine learning that focuses on processing data sets that are not pre-labeled.

Reinforcement learning is an important branch of machine learning that centers on allowing intelligences to autonomously learn how to make optimal decisions to maximize long-term cumulative rewards through continuous interaction with the environment.