Semi-supervised learning is a machine learning paradigm that combines a small amount of labeled data with a large amount of unlabeled data to train a model. This method is particularly suitable for situations where obtaining labeled data is costly or...
Computer vision is a key branch of artificial intelligence that focuses on enabling machines to interpret and understand visual information like humans. It involves the acquisition, processing, and analysis of images and videos, as well as the extraction of data from these sources...
Random Forest is an ensemble learning algorithm consisting of multiple decision trees. It improves the model's accuracy and robustness through voting mechanisms or averaging predictions. It uses random sampling to select data and...
Transfer learning is a machine learning technique that allows a model to apply knowledge learned on one task to another related but different task. This method can reduce the training required for new tasks...
Backpropagation is a supervised learning algorithm used to train artificial neural networks. It calculates the gradient of the network error with respect to the network parameters and then adjusts the network weights using gradient descent to minimize...
Gradient descent is an optimization algorithm used to minimize a loss function through an iterative process to find the optimal values of model parameters. The algorithm starts with initial parameters, calculates the gradient of the loss function, and then proceeds along the gradient...
Supervised learning is a machine learning method that trains an algorithm using labeled training data to classify or predict new data. It includes classification and regression tasks, utilizing...
Pattern recognition is an important branch of information science and artificial intelligence. It processes and analyzes various forms of information (numerical, textual, and logical) about things or phenomena...
Inference refers to the ability of a trained AI model to recognize patterns and draw conclusions from previously unseen information. AI inference is a crucial stage in the AI model's lifecycle, occurring after training, when the model...
Tree of Thought (ToT) is a groundbreaking framework designed to enhance the reasoning capabilities of Large Language Models (LLMs). This approach mimics human cognitive strategies for problem-solving, enabling LLMs to function in a structured manner...
OS Agents are intelligent agents capable of understanding and executing complex tasks. They interact with computing devices through interfaces provided by the operating system, automatically completing tasks ranging from simple to complex. These tasks can include information retrieval, file management, ...
TTS (Text to Speech) is a technology that converts text information into natural speech output. Through TTS, computers can automatically convert input text into natural speech, simulating human speech...
Cross-modal generalization refers to using knowledge learned on one or more specific modalities to improve the system's performance on new, unseen modalities. It is suitable for multimodal learning tasks...
Expert systems (ES) are an important applied research area in the field of artificial intelligence. They are intelligent computer program systems that contain a large amount of knowledge and experience at the level of experts in a specific domain. Expert systems can...
Agentic AI is a type of artificial intelligence system capable of autonomous action and decision-making. These systems, referred to as AI agents, can pursue goals independently of direct human intervention. Agentic AI utilizes advanced techniques such as reinforcement learning...
Zero-Shot Learning (ZSL) is a machine learning scenario in which an AI model is trained to recognize and classify objects or concepts without prior knowledge of any examples of those categories or concepts. Most deep learning...
Sentiment analysis, also known as sentiment classification or opinion mining, is a natural language processing technique used to analyze text data to identify and extract the author's sentiment, opinion, and evaluation. It involves evaluating text...
A token is a credential used to identify a user. It is generated by the server and returned to the client. The client includes this token in subsequent requests, and the server verifies the user's identity by parsing the token to determine whether to authorize access to resources.
Benchmarking is a method for evaluating and comparing system performance. It measures a system's performance through a series of standardized test procedures. This type of testing helps determine a system's performance under specific conditions...
Artificial Narrow Intelligence (ANI), also known as "weak AI," refers to AI systems designed and trained to perform specific tasks or tasks within a narrow scope. It differs from systems with human-like...
Forward propagation is a core process in neural networks, describing how input data is passed through network layers and generates output. Input data is fed into the input layer of the neural network. Input data...
Deliberative alignment is a novel training method proposed by OpenAI, designed to improve the safety and reliability of large language models. This method combines process-based and outcome-based supervision to allow the model to...
Instruction tuning (IT) is a training method for large language models (LLMs) that aims to improve the model's ability to follow natural language instructions and complete real-world tasks.
Visual-language models (VLMs) are a type of multimodal artificial intelligence system that combines image and text processing capabilities to perform high-level visual language tasks, such as visual question answering.