Is python artificial intelligence difficult?

Artificial intelligence is an interdisciplinary subject with a history of more than 60 years since it was proposed. It is still in the initial stage of AI. An important reason for the slow development is that artificial intelligence is technically difficult, involving computers, psychology, philosophy, etc., and has high requirements for practitioners. Currently, many domestic engineers engaged in the AI ​​industry have a master's degree or above.

Artificial intelligence technology can be applied to various industries such as security, medical care, home furnishing, transportation, smart cities, etc. Its prospects are beyond doubt, and the future is definitely a Trillion-level market. (Recommended learning: Python video tutorial)

According to different application fields, the technologies of artificial intelligence research are also different. Currently, machine learning, computer vision, etc. have become popular AI technology directions. , explore the development and future of artificial intelligence together.

Machine learning is the core of artificial intelligence

Machine learning is also called the core of artificial intelligence. It mainly studies how computers can simulate or realize human learning behavior. Acquire new knowledge or skills and help the computer reorganize the existing knowledge structure to continuously improve its performance.

Machine learning is a branch of artificial intelligence research, and people have been studying machine learning for many years. Its development process can be roughly divided into several periods. The first is from the mid-1950s to the mid-1960s, which is an enthusiastic period; the second is from the mid-1960s to the mid-1970s, which is called machine learning. The calming period; the third is from the mid-1970s to the mid-1980s, called the renaissance period; the fourth stage of machine learning began in 1986, and we are still in this period.

Machine learning can now be seen in many application fields, such as data mining, natural language processing, biometric identification, search engines, medical diagnosis, securities, games, robots, etc.

Learning is a very complex process. Learning and reasoning are inseparable. According to the amount of reasoning used in learning, the strategies used in machine learning can be divided into four types: mechanical learning, teaching learning, and analogy learning. and learning by example. The more reasoning used in learning, the stronger the system's capabilities.

What is the difficulty of machine learning?

For machine learning developers, in addition to being very proficient in mathematical knowledge, the tools they choose are also very important. On the one hand, machine learning research requires innovation, experimentation and persistence, and many people give up halfway; on the other hand, it is also difficult to apply machine learning models to actual work.

In addition to engineering factors, machine learning system design is also difficult. The most important factor affecting the design of the learning system is the information provided by the environment to the system. The quality of the information directly affects the performance of the system. The knowledge base stores general principles that guide the execution of some actions, but the information provided by the environment to the learning system is various. All kinds.

If the quality of the information is high and the differences from general principles are relatively small, machine learning is easier to process. If irregular instruction information is provided to the learning system, the learning system needs to obtain enough data, delete unnecessary details, summarize it, form guidance actions, and put it into the knowledge base; in this way, the task of machine learning will be relatively heavy. , it is also more difficult to design.

For machine learning, another technical difficulty is that the debugging of machine learning is very complicated. For example, when doing conventional software design, if the written problem does not work as expected, there may be problems with the algorithm and implementation; but In machine learning, the actual model and data are two key factors. The randomness of these two is very strong, which doubles the difficulty of debugging. In addition to complexity, the debugging cycle of machine learning is generally very long, because it usually takes more than ten hours or even days for the machine to receive instructions to implement corrections and changes.

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