AI Knowledge Base

Digital Twin (Digital Twin) is a technology system for mirror modeling, dynamic mapping and full life cycle management of physical entities or complex systems with high precision, high fidelity and real-time two-way interaction in a virtual digital space.

Supervised learning is one of the most common and basic methods of machine learning, the core idea is to teach computer models how to make predictions or judgments through existing data sets with "correct answers".

Generalized Artificial Intelligence (AGI) is intelligent systems that can understand, learn, reason, adapt and create like or even beyond humans on any cognitive task.

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...

Explainable AI (XAI) is an overarching set of programs that encompasses concepts methods technologies and governance frameworks.

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

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.

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 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.

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.

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.

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.

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.

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.

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

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.

The Transformer architecture is a deep learning model designed for processing sequence-to-sequence tasks such as machine translation or text summarization. The core innovation is the complete reliance on self-attention mechanisms, eschewing traditional loops or convolutional structures. Allowing the model to process all elements of a sequence in parallel, large...

Attention Mechanism (Attention Mechanism) is a computational technique that mimics human cognitive processes, initially applied in the field of machine translation, and later becoming an important part of deep learning.

Diffusion Model (Diffusion Model) is a generative model specialized for creating new data samples such as images, audio or text. The core of the model is inspired by the process of diffusion in physics, which simulates the natural diffusion of particles from a region of high concentration to a region of low concentration. In the machine...

Pre-trained Model is a fundamental and powerful technique in the field of Artificial Intelligence, representing machine learning models that are pre-trained on large-scale datasets. Models form a broad knowledge base by processing massive amounts of information and learning generalized patterns and features from the data...

Long Short-Term Memory (LSTM) is a recurrent neural network variant specialized in processing sequence data. In the field of artificial intelligence, sequence data is widely used in tasks such as time series prediction, natural language processing and speech recognition.

Large Language Model (LLM) is a deep learning system trained on massive text data, with the Transformer architecture at its core. The self-attention mechanism of this architecture can effectively capture long-distance dependencies in language. The model's "large ...

Model fine-tuning (Fine-tuning) is a specific implementation of transfer learning in machine learning. The core process is based on pre-trained models, which utilize large-scale datasets to learn generic patterns and develop extensive feature extraction capabilities. The fine-tuning phase then introduces task-specific datasets to ...

Self-Attention is a key mechanism in deep learning, originally proposed and widely used in the Transformer architecture. The core idea is to allow the model to simultaneously attend to all positions in the input sequence, and compute each position by weighted aggregation of...

Multi-Task Learning (MTL) is not an isolated algorithm, but an intelligent machine learning paradigm.

Regularization is a core technique in machine learning and statistics to prevent model overfitting. Regularization controls the degree of fitting by adding a penalty term to the objective function that is related to the complexity of the model. Common forms include L1 and L2 regularization: the L1 produces sparse solutions and applies...

Generative Adversarial Network (GAN) is a deep learning model proposed by Ian Goodfellow et al. in 2014. The framework implements generative modeling by training two neural networks against each other...

Logistic Regression is a statistical learning method used to solve binary classification problems. The central goal is to predict the probability that a sample belongs to a particular category based on input features. The model maps the linear output to between 0 and 1 by linearly combining the eigenvalues using an S-shaped function...