Artificial Intelligence (AI) is a branch of computer science that enables machines to simulate human intelligence.

This article introduces AI through its definition, classification, forms, working principles, application scenarios, and future prospects.

Definition

AI is based on mathematics and logic, perceiving the environment through technologies such as computer vision (CV), speech recognition and synthesis (ASR & TTS), and establishing knowledge graphs (KG) through machine learning (ML) and deep learning (DL). Finally, it utilizes cutting-edge technologies in natural language processing (NLP) to make judgments and inferences.

It is evident that AI does not rely on a single technology but is achieved through the collaborative work of a series of core technologies and subfields. These technologies collectively empower machines with the ability to perceive, learn, reason, and interact.

Classification

AI is primarily divided into two categories: narrow AI and general AI. It is important to note that all existing AI today is narrow AI (ANI), while general AI (AGI) does not yet exist and may take decades to develop.

Narrow AI

Narrow AI focuses on specific tasks. All currently deployed AI falls into this category. Examples include DeepSeek, which can write poetry and articles, AlphaGo, which can play Go, and Huawei’s ADS, which can drive vehicles.

General AI

General AI possesses or surpasses human capabilities in learning, understanding, and problem-solving. It can perform any intellectual task that a human can accomplish, such as having common sense, learning new skills, and reasoning across domains.

Forms

AI mainly exists in two forms: virtual (software) and physical (hardware).

Virtual AI

Purely software-based, running on devices like smartphones, computers, servers, and the cloud. Examples include large language models (LLM), voice assistants, recommendation systems, and intelligent customer service.

Physical AI

These have a physical presence and can perceive, act, and modify the world. Examples include Unitree robots, autonomous driving systems, robotic arms, and drones.

Working Principles

AI first collects a large amount of data through data collection (DC), then processes it using data preprocessing (DP) and data annotation (DA) techniques. The processed data is used for model training (MT) to establish knowledge graphs (KG), and finally, a trained large language model (LLM) is used to predict new knowledge.

For example, by collecting thousands or even millions of labeled photos of “cats” and “non-cats,” AI learns the various features of cats (shape, color, texture, etc.). When presented with a new photo, it can determine whether a cat is present.

Application Scenarios

AI technology is widely applied in daily life and various industries.

• Daily Life: Personalized recommendations, gaming, smart home systems, autonomous driving, etc.

• Industry and Public Services: Smart manufacturing, smart agriculture, financial risk control, intelligent monitoring, medical diagnosis, personalized education, etc.

Future Prospects

In the future, AI may become a ubiquitous foundational infrastructure, similar to electricity or the internet.

Glossary of Technical Terms

1. Machine Vision (CV): A field that studies how machines can “understand” the world, focusing on extracting, analyzing, and understanding useful information from images or videos.
2. Speech Recognition and Synthesis (ASR & TTS): Technologies that convert speech signals into text and generate natural speech from text, respectively.
3. Machine Learning (ML): The core learning technology of AI that enables machines to learn patterns from large datasets automatically.
4. Deep Learning (DL): An important branch of machine learning that uses multi-layer neural network models to process complex data.
5. Knowledge Graph (KG): A semantic network that represents knowledge in a structured way, facilitating knowledge reasoning and intelligent Q&A.
6. Natural Language Processing (NLP): A field that enables computers to understand, generate, and manipulate human language.
7. Large Language Model (LLM): A deep learning model with billions to trillions of parameters, showcasing strong language understanding and generation capabilities.
8. Narrow AI (ANI): AI that achieves or surpasses human-level performance in specific tasks but cannot transfer to undefined scenarios.
9. General AI (AGI): AI that possesses human-like cognitive abilities across various domains, currently not yet realized.
10. DeepSeek: A conversational AI assistant based on a large language model, capable of text understanding, logical reasoning, and multi-turn dialogue.
11. AlphaGo: An AI system developed by DeepMind that plays Go, known for defeating top human players.
12. Recommendation System: AI algorithms that suggest content, products, or services based on user behavior and preferences.
13. Unitree Robot: A physical intelligent robot developed by Unitree Technology, capable of autonomous movement and interaction with the environment.
14. Autonomous Driving: A complex robotic system integrating perception, decision-making, and control for self-driving capabilities.
15. Data Collection (DC): The process of acquiring raw data from various sources, including sensors and databases.
16. Data Preprocessing (DP): The critical engineering step of transforming raw data into a suitable format for modeling.
17. Data Annotation (DA): The process of adding metadata to raw data to create labeled datasets for supervised learning.
18. Model Training (MT): The iterative process of updating model parameters to minimize loss on a training dataset.
19. Smart Home: An integrated system that uses IoT, sensors, and AI technologies to enhance home automation and control.
20. Smart Manufacturing: A new production paradigm that integrates AI and IoT throughout the manufacturing process.
21. Smart Agriculture: A data-driven agricultural model that uses IoT and intelligent decision-making systems for efficient resource management.