1. Introduction
Artificial Intelligence (AI) has been one of the hotspots in information technology development in recent years. People’s understanding of AI has evolved from initial science fiction imaginations and simple simulations to a comprehensive system of foundational technologies such as machine learning, natural language processing, and computer vision. AI has been widely applied in various fields, including speech recognition, machine translation, intelligent customer service, autonomous driving, healthcare, and finance. This article will provide an overview from the perspective of core technologies in artificial intelligence.
2. Classification of Artificial Intelligence
There are many classifications of artificial intelligence, the most common being based on different primary learning methods: classification, clustering, regression, association, and reinforcement learning. Classification refers to dividing a set of objects into different categories based on features, commonly used for recognizing images, audio, etc.; clustering refers to grouping sample data through unsupervised learning, such as classification and social network analysis; regression refers to predicting or estimating the value of a dependent variable, such as predicting stock prices; association is discovering relationships in data, such as items that are often purchased together in a shopping cart; reinforcement learning refers to learning strategies that maximize rewards from the environment through a reward-punishment mechanism. Different learning methods and algorithms can solve different types of problems.
3. Machine Learning
Machine Learning (ML) is one of the core technologies in AI. Machine learning refers to the process of automatically learning from large-scale data using statistical learning methods. Its goal is to discover statistical patterns and use these patterns to predict or classify new data. Machine learning can be divided into supervised learning, unsupervised learning, and semi-supervised learning.
1. Supervised Learning
Supervised learning refers to training data and test data both having labels, meaning the correct result corresponding to each test data is already known. Typical applications of supervised learning include classification and regression tasks. Classification tasks categorize a set of data according to specific rules or models, such as loan risk classification and spam filtering; regression tasks predict unknown data based on existing data, such as stock price prediction and housing price prediction.
2. Unsupervised Learning
Unsupervised learning refers to training data without labels. The purpose of unsupervised learning is to discover structural properties from a large amount of unlabeled data, such as clustering analysis, anomaly detection, and latent variable analysis.
3. Semi-Supervised Learning
Semi-supervised learning is a method that lies between supervised and unsupervised learning. In this learning method, the training data includes both labeled and unlabeled data. The goal of semi-supervised learning is to utilize a small amount of labeled data to improve the performance of the model as much as possible.
The biggest challenges faced by machine learning are overfitting and underfitting. Overfitting refers to a model learning the noise in the training set too well and not generalizing well to new data. Underfitting refers to a model being too simple to learn complex relationships well, resulting in poor predictive performance.
4. Deep Learning
Deep learning is a branch of machine learning characterized by its