When it comes to the definition of artificial intelligence (AI), the keywords that come to mind may be "future" and "science fiction". Although these factors seem far away from us, they are our daily part of life. With the popularity of voice assistants and the success of driverless driving, artificial intelligence, machine learning, and deep learning have penetrated into every scene of our lives. For example, JD.com will use algorithms to recommend the products you need based on the similarity between your browsing behavior and users; another example is the beauty camera, which will use algorithms to refine your beauty effects based on the analysis of your facial features. There is also the well-known Google DeepMind. When AlphaGo defeated Korean professional Go master Lee Se-dol, when the media described the human-machine battle, they mentioned terms such as artificial intelligence AI, machine learning, and deep learning. Yes, these three technologies all contributed greatly to AlphaGo's victory, but they are not the same thing.

The simultaneous emergence of artificial intelligence and machine learning, and the alternate use of machine learning and deep learning... make most readers confused. What are the differences between these concepts? We can learn from the following A relationship diagram to distinguish.

Figure 1: The relationship between artificial intelligence, machine learning, and deep learning

Artificial intelligence includes machine learning and deep learning, and machine learning includes deep learning. Artificial intelligence is the parent category of machine learning, and machine learning is the parent category of deep learning.

Artificial Intelligence (AI) is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that responds in a manner similar to human intelligence. It is not Human intelligence, but can think like humans, may also exceed human intelligence.

Practical applications of artificial intelligence: machine vision, fingerprint recognition, face recognition, retinal recognition, iris recognition, palmprint recognition, expert system, automatic planning, intelligent search, theorem proving, gaming, automatic programming, Intelligent control, robotics, language and image understanding, genetic programming, etc. Artificial intelligence is currently also divided into: strong artificial intelligence (BOTTOM-UPAI) and weak artificial intelligence (TOP-DOWNAI).

Machine Learning (ML) is the core of artificial intelligence and is a branch of artificial intelligence. Machine learning refers to an algorithm that automatically analyzes and obtains rules from data, and uses the rules to predict unknown data. Therefore, the core of machine learning is data, algorithms (models), and computing power (computer computing power).

Machine learning application fields: data mining, data classification, computer vision, natural language processing (NLP), biometric identification, search engines, medical diagnosis, detection of credit card fraud, securities market analysis, DNA sequence sequencing, Speech and handwriting recognition, strategic games and robot applications, etc.

Deep Learning (DL): It is a new field in machine learning research. Its motivation is to establish and simulate the neural network of the human brain for analysis and learning. It imitates the mechanism of the human brain to explain data.

Data Mining (DM), as the name suggests, refers to the use of machine learning technology to "mine" hidden information from massive data, and is mainly used in images, sounds, and text. In a business environment, companies hope that the data stored in the database can "speak" and support decision-making. Therefore, data mining is more application-oriented.

Figure 2: The relationship between data mining and machine learning

Machine learning is an important method of data mining, but machine learning is another discipline and is not subordinate to data mining , the two complement each other. Data mining is the intersection of machine learning and databases. It mainly uses the technology provided by machine learning to analyze massive data, and uses the technology provided by the database industry to manage massive data.

Whether it is artificial intelligence, machine learning, deep learning or data mining, they are currently exerting their own advantages in solving different goals, and provide convenience for social production and human life, helping us explore the past, show the current situation, and predict the future .