Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems with the ability to reason, discover meaning, generalize, or learn from past experiences.
Introduction
Artificial intelligence (AI) refers to the capability of a digital computer or computer-controlled robot to perform tasks traditionally linked with intelligent beings. This includes the development of systems that can reason, understand meaning, generalize concepts, or learn from past experiences. Since the 1940s, digital computers have been programmed to execute complex tasks, like proving mathematical theorems or playing chess, with high proficiency. Despite continuous advancements in processing speed and memory, no current programs fully match human adaptability across diverse domains or in tasks requiring extensive everyday knowledge. However, certain AI applications have reached human-expert levels in specific functions, such as medical diagnosis, search engines, voice and handwriting recognition, and chatbots.
What is intelligence?
Intelligence is generally understood as the capacity to adapt to new circumstances, distinguishing it from rigid instinctual behaviors like those observed in a digger wasp. The wasp repeatedly follows a fixed pattern even when its environment changes slightly, demonstrating a lack of adaptability. Psychologists define human intelligence as a multifaceted combination of various abilities, rather than a single trait. In the field of AI, research primarily concentrates on replicating and enhancing key intellectual components, including learning, reasoning, problem-solving, perception, and language processing, to create systems that can mimic or surpass human cognitive functions in specific areas.
Learning
Learning in artificial intelligence takes several forms, with trial and error being the most straightforward. An example is a chess program that randomly tries moves until a mate is found, then memorizes the solution for future encounters with the same position—a process known as rote learning. This type of memorization is relatively simple to implement in computers. A more advanced and challenging aspect of AI learning is generalization. This involves applying knowledge gained from past experiences to analogous new situations. For instance, a program capable of generalization can deduce a rule, such as adding "-ed" to form the past tense of regular verbs, and then apply this rule to new verbs like "jump" without having been explicitly taught "jumped" before, based on its experience with similar verbs.
