Pros and Cons of Artificial Intelligence Opinion

Profits of Artificial Intelligence: makes human life better, brings more jobs, saves labor costs & reduces human error. Cons of Artificial Intelligence: leads to mass unemployment, unpredictable, security issues and vulnerabilities.

The pros of artificial intelligence are as follows:

1. Artificial intelligence makes human life better. Widely used driverless, not only reduces people's burden, but also greatly reduces the accident rate. For example, today's Apple system SIR handwriting version of the system, biometric systems are the application of artificial intelligence, significantly improving the quality of human life.

2. AI can bring more job opportunities. Just as a large number of laborers who were once out of traditional agriculture and handicrafts have found new jobs in modern industrial production and urban services, so will advances in artificial intelligence a current data-intensive machine learning and machine learning and artificial intelligence dialogue with the system extends many areas will bring many job opportunities in the future.

3. AI can save labor costs and reduce human error. Artificial intelligence is highly self-programmable, which means it doesn't need a person to keep an eye on it to run, which greatly saves labor and time costs and reduces or even avoids human error, and there are a lot of major projects that have accidents just because of a small human error.

The disadvantages of artificial intelligence are as follows:

1. Artificial intelligence leads to mass unemployment. The development of artificial intelligence has led to the unemployment of many people. According to the press release of the Ministry of Human Resources and Social Security, the unemployment rate in China reached 4.05% at the end of 2016. Robots don't make mistakes, don't get tired, and don't need rest or payment. This can completely replace many occupations, such as workers, drivers, etc., without thinking. This will lead to a lot of unemployment and a lot of people with nothing to do all day.

2. AI is unpredictable. Households can't predict what decisions AI will make, which is both an advantage and a risk, because the system may make decisions that don't fit the designer's original intent.

3, AI has security issues and vulnerabilities. The machine will value the results and ignore the process. It will only achieve literally nothing by looking for system vulnerabilities, but the methods it employs are not necessarily what the designer intended. For example, websites will recommend extremist videos because stimulating content increases browsing time.

Research value of artificial intelligence

For example, heavy scientific and engineering calculations are supposed to be carried out by the human brain, but nowadays computers are not only able to do such calculations, but also can do them faster and more accurately than the human brain can, and therefore the contemporary generation no longer regards such calculations as "complex tasks that require human intelligence". Therefore, contemporary people no longer regard this kind of computation as "complex tasks that require human intelligence", so it can be seen that the definition of complex work is changing with the development of the times and technological progress, and the specific objectives of the science of artificial intelligence are also naturally evolving with the changes of the times. It continues to make new advances on the one hand, and moves on to more meaningful and difficult goals on the other.

In general, the mathematical basis of "machine learning" is "statistics", "information theory" and "cybernetics". ". Other non-mathematical disciplines are also included. This type of "machine learning" is very dependent on "experience". The computer needs to continuously acquire knowledge from experience in solving a class of problems, learn strategies, and when it encounters a similar problem, it applies the empirical knowledge to solve the problem and accumulates new experience, just like an ordinary human being. We can call this "continuous learning".