AI Sharing Circle

Federated Learning (FL) is an innovative machine learning approach first proposed by a Google research team in 2016 to address challenges in data privacy and distributed computing.

Recurrent Neural Network (RNN) is a neural network architecture designed for processing sequential data. Sequential data refers to a collection of data with temporal order or dependencies, such as linguistic text, speech signals, or time series.

Neural Network (NN) is a computational model inspired by the way neurons work in the biological brain.

AI fairness is the interdisciplinary field of ensuring that AI systems treat all individuals and groups in a fair and unbiased manner throughout their design, development, deployment, and operation lifecycle.

Meta-Learning, or learning how to learn, is an important branch of the machine learning field that focuses on developing learning algorithms that can quickly adapt to new tasks.

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...