Robots are the application of computers in "artificial intelligence". Artificial intelligence is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Research in this field includes robots, language recognition, image recognition, natural language processing and Expert systems, etc.
The operating environment of this tutorial: Windows 7 system, Dell G3 computer.
Robots are the application of computers in "artificial intelligence".
Artificial Intelligence (Artificial Intelligence), the English abbreviation of AI, is a new field that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Technical science.
Artificial intelligence is a branch of computer science that attempts to understand the nature of intelligence and produce a new intelligent machine that can respond in a manner similar to human intelligence,Research in this field includes robotics, language recognition, image recognition, natural language processing and expert systems, etc.Since the birth of artificial intelligence, the theory and technology have become increasingly mature, and the application fields have also continued to expand. It can be imagined that the technological products brought by artificial intelligence in the future will be the "containers" of human wisdom. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is not human intelligence, but it can think like humans and may even exceed human intelligence.
Artificial intelligence is a very challenging science, and people engaged in this work must understand computer knowledge, psychology and philosophy. Artificial intelligence is a very broad science, which consists of different fields, such as machine learning, computer vision, etc. Generally speaking, a main goal of artificial intelligence research is to make machines capable of tasks that usually require human intelligence. Complex work. But different times and different people have different understandings of this "complex work".
Artificial intelligence has received more and more widespread attention in the computer field. And it is applied in robots, economic and political decision-making, control systems, and simulation systems.
The Research Value of Artificial Intelligence
For example, heavy scientific and engineering calculations were originally undertaken by the human brain. Now computers can not only complete such calculations, but also It can be done faster and more accurately than the human brain. Therefore, contemporary people no longer regard this kind of calculation as a "complex task that requires human intelligence." It can be seen that the definition of complex work has evolved with the development of the times and the advancement of technology. What is changing is that the specific goals of the science of artificial intelligence will naturally develop with the changes of the times. On the one hand, it continues to gain new progress, and on the other hand, it turns to more meaningful and difficult goals.
Usually, the mathematical basis of "machine learning" is "statistics", "information theory" and "cybernetics". Other non-mathematical subjects are also included. This type of "machine learning" relies heavily on "experience". Computers need to constantly acquire knowledge and learn strategies from the experience of solving a type of problem. When encountering similar problems, they need to use experiential knowledge to solve problems and accumulate new experiences, just like ordinary people. We can call this learning method "continuous learning". But in addition to learning from experience, humans can also create, that is, "leap learning." This is called "inspiration" or "epiphany" in some situations. All along, the most difficult thing for computers to learn is "epiphany". Or to put it more strictly, it is difficult for computers to learn "qualitative changes that do not rely on quantitative changes" in terms of learning and "practice", and it is difficult to directly change from one "quality" to another, or directly from one "concept" to another "concept". Because of this, "practice" here is not the same as human practice. The human practical process includes both experience and creation.
This is what intelligence researchers dream of.
In 2013, S.C WANG, a data researcher at the Dijin Data Center, developed a new data analysis method, which derived a new method for studying the properties of functions. The author found that new data analysis methods provide a way for computers to learn to "create". In essence, this method provides a quite effective way to model human "creativity". This approach is given by mathematics and is an "ability" that ordinary people cannot have but computers can. From then on, computers are not only good at calculation, but also good at creation because of their good calculation. Computer scientists should resolutely deprive "creative" computers of their overly comprehensive operating capabilities, otherwise computers will really "capture" humans one day.
When looking back at the deduction process and mathematics of the new method, the author expanded his understanding of thinking and mathematics. Mathematics is concise, clear, reliable and model-oriented. In the history of the development of mathematics, the creativity of mathematical masters shines everywhere. These creativity are presented in the form of various mathematical theorems or conclusions, and the biggest feature of mathematical theorems is that they are logical structures containing rich information based on some basic concepts and axioms and expressed in a patterned language. It should be said that mathematics is the subject that most simply and directly reflects (at least one type of) creativity model.
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