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...
Dataset distillation, also known as dataset compression, is a technique designed to extract key information from large datasets and build a smaller dataset. This...
The Scaling Law describes how model performance changes with model size (such as the number of parameters), training dataset size, and computational resources used for training. It reflects the relationship between model performance and these factors...
Connectionism, also known as link theory, is a theoretical framework in cognitive science and artificial intelligence that advocates explaining cognitive processes by simulating the interconnections between neurons in the brain. This theory posits that...
The objective function is a core concept in mathematical optimization problems; it represents the functional relationship between the objective and the influencing factors. Simply put, the objective function is the function you are trying to calculate or optimize to achieve...
Slow Perception is a novel visual perception concept proposed by the Step Multimodal team. Through a progressive decomposition and flow approach, it allows models to perceive complex geometric shapes with greater finer detail, much like humans. In experiments,...
Judge models are auxiliary models used to evaluate the quality of the output of other models. They act as "referees," evaluating and scoring the output of Large Language Models (LLMs). The models are evaluated based on the input question and the...
Agentic RAG is a method that combines AI agents and retrieval augmentation generation (RAG) systems. It revolutionizes question-answering by introducing an agent framework. This differs from methods that rely solely on large models...
Forward chaining is a method in the field of artificial intelligence that uses inference engines for automated reasoning. It's a data-driven reasoning strategy that starts with known facts and derives new information by applying a series of rules...
Discriminative models are an important class of models in machine learning, primarily used for classification and regression tasks. Their core objective is to learn the mapping relationship between input variable x and output variable y, i.e., the conditional probability distribution P(y|x). (This is in contrast to generative models...)
Native Sparse Attention (NSA) is a novel sparse attention mechanism proposed by DeepSeek, which improves the efficiency of long text modeling through algorithmic innovation and hardware optimization. Its core lies in a dynamic hierarchical sparse strategy, combined with coarse-grained...
Chain-of-Draft (CoD) is a new AI reasoning paradigm proposed by Zoom's research team. It improves reasoning efficiency and saves costs by mimicking the concise thought process of humans. This approach is inspired by the human reliance on drafts or...
The Model Context Protocol (MCP) is an open protocol that enables seamless communication between large language models (LLMs) and external tools and data sources. It achieves interfacing with various data sources through a unified protocol...
Vibe Coding is a new programming paradigm introduced by Andrej Karpathy, co-founder of OpenAI. Based on artificial intelligence, particularly Large Language Models (LLMs), developers describe requirements using natural language, and AI...
ANP (Agent Network Protocol) is an open-source intelligent agent communication protocol, becoming the HTTP of the intelligent agent internet era, building an open, secure, and efficient collaborative network for billions of intelligent agents. It breaks down data silos...
The Model Context Protocol (MCP) is an open protocol introduced by Anthropic that standardizes communication between large language models (LLMs) and external data sources and tools. By providing a unified interface, it enables LLMs to be flexible...