For example, heavy scientific and engineering calculations are supposed to be undertaken by the human brain. Today, computers can not only perform such calculations, but also can do it faster and more accurately than the human brain. Therefore, contemporary people no longer regard this calculation as It is a "complex task that requires human intelligence to complete." It can be seen that the definition of complex work changes with the development of the times and the advancement of technology. The specific goal of the science of artificial intelligence has naturally evolved with the changes of the times. On the one hand, it continues to make new progress, on the other hand it turns to more meaningful and more difficult goals.
Often, the mathematical foundations of "machine learning" are "statistics," "information," and "cybernetics." Also included are other non-mathematical subjects. This type of "machine learning" is highly dependent on "experience." Computers need to constantly acquire knowledge and learn strategies from the experience of solving a type of problem. When encountering similar problems, use empirical knowledge to solve problems and accumulate new experiences, just like ordinary people. We can call this type of learning "continuous learning." But in addition to learning from experience, human beings will also create, that is, "jumping learning." This is called "inspiration" or "enlightenment" in some cases. All along, the hardest thing for computers to learn is the “enlightenment”. Or, strictly speaking, it is difficult for computers to learn “not dependent on quantitative change” in terms of learning and “practice”. It is difficult to go directly from one “quality” to another, or directly from a “concept”. Go to another "concept". Because of this, the “practice” here is not the same as human practice. The human practice process includes both experience and creation.
This is what the intelligent researchers dream of.
In 2013, S.C., a data researcher at Dijin Data Center, developed a new data analysis method that derived a new method for studying the properties of functions. The authors found that new data analysis methods provide a way for computer science to “create”. In essence, this approach provides a fairly effective way to model the "creativity" of human beings. This approach is given by mathematics and is the "capability" that ordinary people cannot possess but the computer can have. Since then, the computer is not only good at calculation, but also good at creating because of good calculations. Computer scientists should categorically deprive the "skilled" computer of its ability to operate too comprehensively, or the computer will one day "anti-capture" humans.
When looking back at the derivation process and mathematics of the new method, the author expands his understanding of thinking and mathematics. The mathematics is simple, clear, reliable, and strong in pattern. In the history of the development of mathematics, the brilliance of the creativity of mathematics masters shines everywhere. These creativitys are presented in various mathematical theorems or conclusions. The most important feature of mathematical theorems is that they are based on some basic concepts and axioms, which are expressed in a modular language and contain rich information. It should be said that mathematics is the most simple and straightforward reflection of (at least one type of) the discipline of creativity.
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