Artificial Intelligence Programming: How Close Have the AI Programmers Come to True Intelligence?

Creating true artificial intelligence is the Holy Grail of the programming world. While many artificial intelligence programming efforts mimic human response, or learn from experience, they have not yet been able to pass the Turing Test, the accepted benchmark for artificial intelligence. AI programming is typically approached from one of two perspectives. Either the programmer is working toward a programming goal, such as passing the Turing Test, or toward a functional goal, such as improving security systems.

Artificially Human Responses

Early AI programming produced randomly selected responses based on keywords in the input received. Many current AI software used for entertainment works on the same principle. Many websites offer the chance to chat with an AI program, so that people can experience AI firsthand, but these programs are typically quite elementary. Interactions with AI programs can take place via text, or for more advanced programs, via verbal conversation.

The Turing Test

The Turing Test is named after Alan Turing, who devised a theoretical test to determine whether a computer could be distinguished from a human. In the Turing Test, a judge is given the opportunity to ask questions without knowing whether the answers are being computer generated or are being answered by a person. When judges are less than 50% correct, the computer is judged to be indistinguishable from a human being. This test has never been passed by an artificial intelligence program, despite events occurring each year, in which the most promising AI programs are tested.

Many scientists disagree with the application of the Turing Test as the only test of AI programming. No computer is currently able to pass this test. However, the other feats of logic and learning that can be accomplished by computer programs, particularly by neural networking, allow them to be called, “Artificial Intelligence”.

Learning From Experience

Artificially intelligent computers can, in some cases, use input in order to learn, and provide increasingly appropriate responses. Artificial Neural networks mimic the processes in the human brain that are accomplished by biological neural networks. Neural networks connect a number of smaller nodes, which work both together and side by side, to process information. These networks are extremely complex, and are the closest reflection of human thought that is currently available.

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