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...
Multimodal deep learning is a subfield of artificial intelligence (AI) that focuses on developing models capable of simultaneously processing and learning from multiple types of data. This article explains its definition...
The most important development in natural language processing is Large Language Modeling (LLM). In this article, we will provide a basic introduction to LLM, discussing its definition, training methods, reasons for its popularity, common examples of LLM, and more...
Pre-training is a cornerstone of modern machine learning models. In this article, we will explore the conceptual definition of pre-training, its importance in artificial intelligence, the various techniques used to implement pre-training, and the work of researchers in this field...
A crucial aspect of machine learning is data annotation, which is the process of labeling and classifying raw data to make it usable for training ML models. This article will provide an overview of data annotation,...
Sentiment analysis is an important aspect of natural language processing that allows organizations to extract valuable insights from unstructured text data. By understanding people's opinions and sentiments, businesses, researchers, and governments can make more informed decisions...
Convolutional Neural Networks (CNNs) are a class of deep learning algorithms primarily used in computer vision. They have applications in various fields, including image and video recognition, natural language processing, etc.
Reinforcement learning from human feedback (RLHF) is an emerging research area in the field of artificial intelligence (AI) that combines reinforcement learning techniques with human feedback...
Low-rank adaptation (LoRA) technology allows for the faster and more efficient adaptation of large language models to specific tasks or domains. This article will provide an overview of what LoRA is, its main components, how it works, its advantages, and its limitations...
Apple Intelligence is a personal intelligence system developed by Apple and integrated into iPhone, iPad, and Mac platforms. It was announced at WWDC in June 2024. This AI system combines powerful generative models...