Artificial Intelligence is the branch of computer science that deals with writing computer programs that can solve problems creatively. AI to imitate or duplicate human intelligence in computers and robots. This is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent. In recent years, AI programming techniques to make smart machines is becoming a reality. Researchers are creating systems which can mimic human thought, understand speech, beat the best human chess player, and countless other feats never before possible.
AI is computer software designed to perceive, reason, and understand.
(a) Historically, computer software works through a series of if/then conditions in which every operation has exactly two possible outcomes (yes/no, on/off, true/false, one/zero).
(b) Human reasoning, on the other hand, is extremely complex, based on deduction, induction, intuition, emotion, and biochemistry, resulting in a range of possible outcomes.
AI attempts to imitate human decision making, which hinges on this combination of knowledge and intuition (i.e., remembering relationships between variables based on experience). The advantage of AI in a business environment is that IT systems
(a) Can work 24 hours a day
(b) Will not become ill, die, or be hired away
(c) Are extremely fast processors of data, especially if numerous rules (procedures) must be evaluated
There are several types of AI:
1) Neural networks
2) Case-based reasoning systems
3) Rule-based expert systems
4) Intelligent agents
5) An expert system
Two of the most important and most used branches of AI are neural networks and expert systems. Neural networks are made of artificial neurons, connected weights, which are indicative of the strengths of the connections. The neurons are arranged in layers, and depending on the complexity of the application, there could hundreds or thousands of neurons. Iterative propagation of input from one layer of neurons to the next (training) is what enables the neural network to learn from experience. Unlike humans, when a neural is fully trained, it can classify and identify patterns in massive amounts of complex data, at high speeds that cannot be duplicated humans.
An expert system can solve real-world problems using human knowledge and following human reasoning skills. Knowledge and thinking processes of experts are collected and encoded into a knowledge base. From that point on, the expert system could replace or assist the human experts in making complex decisions integrating all the knowledge it has in its knowledge base.