Loop engineering is a new paradigm in AI engineering, referring to building autonomous, looping systems around large models, thus upgrading AI from single-response tools to long-term autonomous agents. Analogous to the PDCA cycle in traditional engineering, the model is the execution...
Harness Engineering is a new paradigm for AI engineering, which refers to building a complete operating system around a large model, upgrading it from a one-time response tool to a long-term autonomous agent.
UCP (Universal Commerce Protocol) is an open-source standard protocol jointly developed by Google and numerous e-commerce giants and payment platforms, aiming to promote the development of a new generation of proxy commerce. UCP uses standardized interfaces and protocols...
Rue is an experimental programming language developed by Rust core contributor Steve Klabnik using Claude. Rue explores ways to provide a more user-friendly, memory-safe programming experience than Rust without relying on garbage collection.
AI agents, also known as artificial intelligence agents, are a core concept in the field of artificial intelligence. They refer to intelligent entities or computational entities that can perceive their environment, make autonomous decisions, and take actions to achieve specific goals...
Artificial General Intelligence (AGI) is a form of artificial intelligence that possesses broad cognitive abilities comparable to or even surpassing those of humans. It can perform tasks and environments as effectively as humans...
Deep learning frameworks are a collection of software libraries and tools used to design, train, and deploy deep learning models. They provide the infrastructure needed to build complex neural networks, including data...
Federated learning is a distributed machine learning approach that allows multiple clients to collaboratively train models while maintaining data locality and avoiding direct sharing of raw data. Privacy-preserving federated learning...
Context-aware computing is an intelligent computing paradigm that can identify and understand a user's real-time environment, state, and activities, providing personalized and highly adaptive services. Context-aware computing...
Exploration and exploitation are two core concepts. Exploration refers to an agent trying new or unfamiliar actions to discover better behavioral strategies, while exploitation refers to an agent using known best practices...
Question answering systems (QA) are a type of information retrieval system in the field of artificial intelligence. They can understand questions posed by users in natural language and provide accurate and direct answers. They can process text, voice, etc.
Self-supervised learning is a machine learning technique that enables models to learn from unlabeled data by training them through auxiliary tasks that generate labels. Self-supervised learning is used in natural language processing...
Named Entity Recognition (NER) is a key technology in Natural Language Processing, used to identify and classify entities with specific meanings from text, such as names of people, places, organizations, and time expressions.
Neural Architecture Search (NAS) is an automated technique used to design and optimize the structure of deep learning models. It employs intelligent search strategies to find the optimal network architecture within a vast space...
Inverse reinforcement learning (IRL) is a machine learning method used to infer the underlying reward function from expert behavioral demonstrations. In traditional reinforcement learning, the agent interacts with...
Semantic role labeling (SRL) is a technique in natural language processing that aims to identify arguments of predicates in a sentence and assign appropriate semantic role labels. It helps to reveal the relationships between sentence components...
This article introduces what machine learning is, the technical classifications of machine learning, common machine learning algorithms, and practical applications of machine learning, giving you a basic understanding of this AI technology.
Deep learning is a subset of machine learning. This article introduces what deep learning is, how it works, the differences between deep learning and machine learning, deep learning development frameworks, and more...
This article introduces AIGC (AI Generated Content), its working principles, application scenarios, and challenges.
Generative Adversarial Networks (GANs) are deep learning models that have gained significant attention in recent years due to their ability to generate high-quality, realistic data. This article...
Neural networks, as a computational model in artificial intelligence, are a type of machine learning inspired by the human brain. This article introduces their definition, working principles, types, advantages, limitations, and application scenarios.
NLP (Natural Language Processing) is a field of computer science that focuses on creating computers and software capable of understanding human speech and language. NLP utilizes artificial intelligence and machine learning...
Unsupervised learning is a type of machine learning where the model learns from data without any explicit guidance or labeled examples. This article introduces its definition, main algorithms, applications, and challenges.
Reinforcement learning (RL) is a branch of machine learning that focuses on training algorithms to make decisions through interaction with their environment. It is inspired by how humans and animals learn from their experiences to achieve goals. In this paper...