Artificial Intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks usually associated with intelligent creatures. The term is often applied to a project of development systems characterized by human intellectual processes, such as the discovery, generalization, or ability to learn from past experience. Since the development of digital computers in the 1940s, it has been proven that computers can be programmed to accomplish very complex tasks – for example, searching for clues for mathematical theories or playing chess – with great efficiency. However, despite continuous advances in computer processing speed and memory capacity, there are still no programs that match human resilience in broad domains or tasks that require daily knowledge. On the other hand, some programs achieve the level of performance of human experts and experts in performing certain tasks, so that in this limited sense, artificial intelligence is as diverse as medical diagnostics, computer search engines and voice or handwriting recognition. Found in the form. .
What is intelligence?
All are known to be intelligent except for simple human behavior, but even the most complex insect behavior is never taken as a sign of intelligence. What’s the difference? Consider the behavior of excavator wasp, spec echneumonius. When the female wasp returns with her food, she first deposits it at the entrance, checks for intruders inside her burr, and only then, if the coast is clear, will she take her food. The true nature of the wasp’s spontaneous behavior is known if the food is moved a few inches away from its entrance: after emergence, it repeats the entire process when the food is displaced. Intelligence – especially the lack of specs – should be capable of adapting to new situations.
Psychologists generally portray human intelligence not just through one attribute, but through a combination of many different abilities. Research in AI is mainly focused on the following aspects of intelligence: language learning, reasoning, problem solving, understanding and use.
There are many different types of practices for artificial intelligence. Learning through trial and error is simple. For example, a simple computer program to solve mate-a-chess problems may try to run randomly until a mate is found. The program can then conditionally store the solution so that it will lose the solution the next time the computer encounters the same situation. This simple memory of individual objects and processes – called root learning – is easy to implement on a computer. The problem is more challenging in implementing the so-called normalization. The generalization is to apply previous experience to new situations. For example, a program that learns by the last word of a simple English verb cannot be generated unless the first tense of a word like jump is first presented with a jump, but the “add ed” rule can be learned based on experience with generalizable program analogies.