An Overview of Industry Applications for Artificial Intelligence (AI)

Artificial Intelligence (AI), abbreviated as AI in English, is a science that studies the theories, methods, and technologies for simulating, extending, and expanding human intelligence. It originated in the 1950s and has now developed into six major areas: computer vision, natural language understanding and communication, cognition and reasoning, robotics, game theory and ethics, and machine learning. These fields also show signs of increasing cross-discipline integration.

Machine learning explores how algorithms can automatically learn data structures and intrinsic patterns from input data samples to gain new experience and knowledge. This enables intelligent identification of new samples and even predictions for the future. Typical machine learning algorithms include linear regression, K-means, K-nearest neighbors, principal component analysis, support vector machines, decision trees, and artificial neural networks.

What Can Artificial Intelligence Do?

Artificial intelligence has two key abilities: "fast processing" and "autonomous learning." AI allows for rapid processing of learning, decision-making, and action. Computers can process information, communicate, and perform parallel and linear calculations faster than humans. AI also has the flexibility to autonomously learn and manage knowledge, supporting a systematic management approach for "generation—storage—application—updating" of knowledge.

Where Is AI Used?

AI has been successfully applied in areas such as image recognition, speech recognition, natural language processing, human-computer interaction, machine vision, and autonomous driving.

AI is more adept at addressing business problems with the following characteristics:

1. There are persistent pain points in the industry.
2. The business process itself involves digital information input, and problems can be subdivided and clearly defined. Business processes tend to be repetitive, and the communication of results is mainly through written or one-way communication.
3. Business processes are less influenced by the overall complexity of the business environment.

How is Data for AI Collected?

1. Self-Collected Data: Starting from scratch and collecting data with significant human effort.
2. Public Data: Various internet platforms or large companies maintain their own databases.
3. Industry Data Collaboration: Collaborating with downstream startup companies or industry players, or building partnerships with upstream data or platform companies to connect products or data that benefit both sides.

The Competitive Advantage of AI

The advantage of AI lies in possessing vast, precise, high-quality data, which provides the raw materials for training AI. Thus, the future competition among AI companies may be driven by big data. One example is why Alibaba spent 9.5 billion yuan to acquire Ele.me, humorously referred to as "Jack Ma's most expensive takeaway order in history." According to recent public data, Ele.me has 260 million users and over 2 million merchants. This acquisition may have been aimed at acquiring a large volume of data, serving as a stepping stone for Alibaba's future AI strategy.