Comprehensive Interpretation of Artificial Intelligence

Artificial intelligence, abbreviated as AI, is the ability to simulate, extend, and enhance human intelligence through artificial methods and technologies, creating machines that can respond similarly to human intelligence.

Artificial intelligence is an emerging technology that represents a significant upgrade in social productivity. It consists of three levels: computational intelligence, perceptual intelligence, and cognitive intelligence. Computational intelligence refers to the ability to perform rapid calculations, memory, and storage; perceptual intelligence refers to the ability to recognize and judge specific things in the natural world; cognitive intelligence refers to understanding and analysis. Driven by machine learning and deep learning technologies, perceptual intelligence, which targets recognition technologies such as vision and hearing, has also broken through industrialization barriers, allowing machines to judge and recognize specific things in the natural world.

Artificial intelligence technology mainly consists of five core technologies: computer vision, machine learning, natural language processing, robotics, and biometric recognition technology. In addition, as artificial intelligence technology continues to mature, big data, algorithms, and blockchain technologies have also become important components of artificial intelligence technology.

Since the advent of deep learning, reinforcement learning, and generative artificial intelligence like ChatGPT, artificial intelligence has gained widespread attention. Artificial intelligence has gone through a development process of over sixty years since it was first proposed at the Dartmouth Conference in 1956, experiencing three waves of development.

The first stage is the initial stage (1950 to 1960)

The concept of “artificial intelligence” originated from the Dartmouth Conference in 1956. Early research focused primarily on machine theorem proving, reasoning, and search algorithms, while symbolic logic and perceptron algorithms provided the theoretical foundation for the development of artificial intelligence.

The second stage is the development stage (1960 to 2000)

This stage of “artificial intelligence” experienced two winters. The first winter was from 1960 to 1970, when symbolic logic methods struggled to handle uncertain and ambiguous problems, and the developed systems were unable to adapt to rapidly changing environments, leading to inefficiencies in problem-solving and an inability to tackle complex issues. As a result, the development of artificial intelligence lost momentum during this period. The second winter occurred from 1990 to 1991, when artificial intelligence was limited by computational power and neural network algorithms. However, the decade from 1970 to 1980 was a period of development for artificial intelligence research. On one hand, the successful development of the XCON expert system (an expert system for computer system configuration) based on symbolic methods instilled confidence in the market. On the other hand, the popularity of connectionist methods opened up new avenues for the development of artificial intelligence.

The third stage is the explosive stage (2000 to present)

With improvements in computing power, data accumulation, and the maturity of statistical learning, deep learning, and reinforcement learning methods, artificial intelligence has experienced rapid development. The introduction of “deep learning” in 2006 laid the theoretical foundation for the technology. In 2016, the Go AI program AlphaGo won, demonstrating the potential of deep learning and reinforcement learning in complex tasks. The emergence of generative artificial intelligence like ChatGPT marks the arrival of a new era for artificial intelligence. ChatGPT is an AI large language model based on the GPT-3.0 architecture, which generates content through multiple transformer layers in the internet domain, including code generation and text Q&A tasks. Essentially, it is a chatbot program trained through reinforcement learning, representing the pinnacle of natural language processing technology.

Generative artificial intelligence is a powerful form of AI that can create new original content by learning data patterns and using complex algorithms and learning methods similar to those of the human brain. Although generative AI is currently primarily used for text, computer programming, image, and sound generation, this technology can be applied in many fields, such as drug design, architecture, and engineering. For example, it can generate candidate drug molecules for specific diseases, create conceptual images of buildings, or generate interior designs.

Generative artificial intelligence technology may even impact the design of food products and everyday items (from furniture to appliances). In scientific research, generative models can promote breakthroughs by improving experimental design, identifying relationships between data elements, and creating new theories. In daily work, AI-based language models like ChatGPT can improve productivity and output quality, turning human tasks into creative generation and editing rather than mere drafting.

Currently, generative artificial intelligence is facing many challenges during its development. First, models may reflect and amplify biases and discrimination present in training data, leading to unfair or biased outcomes. Second, generative models are generally considered black box models, lacking transparency and interpretability, making it difficult to understand their decision-making processes. Intellectual property and data privacy issues arise from the use of copyright-protected information. Moreover, generative AI technology may be misused for generating false information, deep fakes, and other malicious purposes. Therefore, the decision-making processes of applications must be understandable, their objectives clearly disclosed to operators and end-users, and personal privacy must be respected. Ethical guidelines and governance structures must be established to mitigate potential harms and ensure a balance between technological advancement and responsible use. The issue of copyright ownership must be resolved so that AI designers, creators of training data, and authors of application usage instructions receive appropriate recognition.

On July 10, 2023, the National Internet Information Office, in conjunction with the National Development and Reform Commission and six other departments, issued the “Interim Measures for the Management of Generative Artificial Intelligence Services,” proposing a series of governance measures from the perspectives of legal basis, technological development and governance, service norms, supervision and inspection, and legal responsibility. In terms of technological development, it encourages innovation and development of generative AI technology, builds application ecosystems, and supports international exchanges and cooperation. In terms of service norms, it emphasizes compliance with laws, respect for social ethics, and the prohibition of generating illegal content, requiring measures to prevent discrimination during the technology development process. Regarding supervision and legal responsibility, various departments must strengthen management, and generative AI service providers must cooperate with supervision and inspection, addressing illegal behaviors.

Artificial intelligencecan lower production costs, improve efficiency, and enhance product quality across various industries through intelligent production methods and automated equipment. Moreover, the development of artificial intelligence technology provides opportunities for upgrading and transforming traditional industries, enabling many sectors to achieve digital, intelligent, and networked development.

The widespread application of artificial intelligence technology has also triggered emerging industries, such as autonomous vehicles, smart homes, and unmanned retail. These new industries have injected new vitality into economic growth and created new job opportunities in the labor market. However, this technological advancement may lead to the obsolescence of certain traditional industries and positions, prompting companies to closely monitor these changes and take appropriate measures to enhance workforce retraining and reemployment. The application of artificial intelligence technology in data analysis and decision support also impacts industrial structure. Leveraging big data and machine learning technologies, companies can gain better insights into market trends, optimize product design and marketing strategies, and enhance core competitiveness. Additionally, these technologies help companies achieve more precise market positioning and customer service, driving the industrial structure towards higher-end and higher value-added directions.