An example of the machine’s process would be, ‘George either be in the library or in the parking lot. He is not in the parking lot, so he must be in the library.’ This type of reasoning is commonly used in mathematics and logic where large groups of theories are based on a small set of rules. Another method of reasoning is inductive, where knowledge about the situation is used to reach a probable conclusion. An example of an Artificial Intelligence’s process is, ‘Previous accidents like this were caused by the machine clogging, so this accident must have been caused by a clog.’ This type of reasoning is common in science where the collected data is used to predict future occurrences. Third, Artificial Intelligence learns in two ways, trial and error (also called rote learning) and generalization. Trial and error is considered the simplest form of learning. As the name suggests, if the AI was playing chess, it would randomly guess moves until it is able to. Then, it would remember those moves and use when applicable. Additionally, this type of learning is relatively easy to implement onto a
An example of the machine’s process would be, ‘George either be in the library or in the parking lot. He is not in the parking lot, so he must be in the library.’ This type of reasoning is commonly used in mathematics and logic where large groups of theories are based on a small set of rules. Another method of reasoning is inductive, where knowledge about the situation is used to reach a probable conclusion. An example of an Artificial Intelligence’s process is, ‘Previous accidents like this were caused by the machine clogging, so this accident must have been caused by a clog.’ This type of reasoning is common in science where the collected data is used to predict future occurrences. Third, Artificial Intelligence learns in two ways, trial and error (also called rote learning) and generalization. Trial and error is considered the simplest form of learning. As the name suggests, if the AI was playing chess, it would randomly guess moves until it is able to. Then, it would remember those moves and use when applicable. Additionally, this type of learning is relatively easy to implement onto a