AI is a technology that allows machines to acquire intelligenct and inferential mechanisms like humans. this concept first appeared at the Dartmouth Conference in 1956. This is a very challenging task. At present, human beings cannot yet have a comprehensive and scientific understanding of the working mechanism of the human brain. It is undoubtedly more difficult to make intelligent machines that can reach the level of the human brain. With that being said, machines that archive similar to or even supass huyman intelligence in some way have been proven to be feasible.
How to relize AI is very broad question. The development of AI has mainly gone thorough three stages, and each stage represent the exploration footprint of the human trying to realize AU from differenct angles. In the early stage, people tried to develop intelligent systems by suymmarizing and generalizing some logical rules and implementing them in the form of computer programs. But such explicit rules are often too simple and are difficult to be used to express complex and abstract concepts and rules. This stage is called the inference period.
In the 1970s, scientists tried to implement AI though knowledge database and reasoning. They built a large and complex expert system to simulate the intelligence level of human experts. One of the biggest difficulties with theses explicitly specified rules is that many complex, abstract concepts cannot be implemented in concrete code. For example, the process of human recognition of pictures and understanding of languages cannot be simulated by established rules at all. To solve such problems, a research discipline that allowed machines to automatically learn rules from data, known as machine learning, was born, Machine learning become a popular subject in AI in the 1980s, This is the second stage.
In machine learning, there is a directiion to learn complex, abstract logic through neural networks, Research on the direction of neural networks has experienced two ups and downs. Since 2012, the applications of deep neural network technology have made major breakthroughs in fields like computer vision, natural language processing(NLP), and robotics. Some tasks have even surpassed the level of human intelligence. This is the third revival of AI. Deep neural networks eventually have a new name - deep learning. Generally speaking, the sessential difference between neural networks and deep learning is not large. Deep learning refers to models or algorithms based on deep neural networks. The relationship between artificial intelligence, machine learning, neural networks, and deep learning is shown in Figure 1-1.
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