Foundation models are a type of model that has rapidly developed in the field of artificial intelligence in recent years. They are pre-trained on large-scale, widely sourced datasets and can perform a range of general tasks. These models are based on...
Synthetic data is data that is not artificially created; it is generated through computational algorithms and simulations to mimic real-world data. It possesses the same mathematical properties as actual data, but does not contain the same specific details...
Generative Adversarial Networks (GANs) are deep learning models consisting of a generator and a discriminator. They learn through a game-like interaction to generate data. The generator's task is to produce data from random noise, while the discriminator...
Supervised fine-tuning (SFT) is the process of further training a pre-trained model using a labeled dataset specific to a particular task. Pre-trained models are typically trained on large-scale unsupervised datasets...
Cross-validation is an important model validation technique in statistical analysis, primarily used to evaluate a model's generalization ability on unknown data. It involves splitting the dataset into several subsets and then using different methods to cross-validate the data...
Fine-tuning is a process of making minor adjustments to the parameters of a pre-trained model for a specific task or dataset. This process fully utilizes the general characteristics learned by the pre-trained model on large-scale datasets...
Semantic understanding is an important area of Natural Language Processing (NLP), focusing on understanding and interpreting the meaning of words, phrases, and sentences in text. It involves literal meaning, but also includes context,...
Knowledge representation refers to representing knowledge in a machine-understandable form. It involves a combination of data structures and processing mechanisms: Representation = Data Structure + Processing Mechanism. In knowledge representation...
Meta-learning, also known as "learning to learn," is an exciting and promising research area in machine learning. Its core goal is to enable machine learning systems to efficiently learn new tasks and solve...
Video Diffusion Models (VDM) are generative models that combine the advantages of variational autoencoders (VAEs) and diffusion models. The core idea of VDM is to perform the diffusion process in the latent space, rather than...
Hyperparameter tuning is a crucial step in machine learning model training, involving selecting the optimal combination of hyperparameters to optimize model performance. Hyperparameters are configurations set before the learning process begins...
Real-Time Engagement (RTE) is a collection of technologies, product architectures, and scenario solutions designed to enable two parties communicating and interacting remotely to realistically and seamlessly recreate the offline interaction scenario in real time...
Robotic Process Automation (RPA) is a software technology that automates business processes by simulating human behavior and interacting with digital systems and software. RPA software robots can...
Mixture of Experts (MoE) is a technique in machine learning used to build large models by decomposing them into multiple sub-networks or "experts" to improve model performance and efficiency. Each expert...
Genetic algorithms (GA) are heuristic search algorithms that simulate the process of biological evolution, belonging to the category of evolutionary algorithms. They are based on the ideas of natural selection and genetics, simulating the process of natural selection...
Generative AI is an AI technology that creatively generates new content based on existing data. It has wide applications in various fields, including text generation, image generation, audio and video generation, etc.
Contrastive Language-Image Pretraining (CLIP) is a multimodal pre-trained neural network model developed by OpenAI. It uses contrastive learning to train images and text...
Hallucinations of large models refer to the phenomenon where the content generated by a model is inconsistent with real-world facts or user input.
Embodied Intelligence (EI) is a multidisciplinary research field that involves cognitive science, artificial intelligence, robotics, neuroscience, and other areas. The core idea of embodied intelligence is that...
Training data is the dataset used to build predictive models in machine learning. It contains a series of input features and corresponding target outputs, which are used to teach the model how to perform operations based on the features...
Reward models are a core concept in reinforcement learning, used to evaluate an agent's behavior in a specific state. In large language models (LLMs), reward models guide the model by scoring the input question and answer...
Prompt engineering is an emerging discipline that focuses on developing and optimizing prompts to help users effectively utilize large language models (LLMs) for various purposes...
Model generalization is a core concept in machine learning, referring to a model's ability to perform on new data beyond its training data. In other words, generalization ability measures a model's performance on unseen samples...
In Natural Language Processing (NLP) tasks, the context window refers to the range of contextual information a model considers when processing a specific input. Specifically, it determines the scope of contextual information a model can consider simultaneously when generating or understanding text...