What Is AI-Complete?

The term “AI-complete” is used in IT to describe problems or outcomes that would rely on having a robust AI system in place – in other words, being able to put together a computer system that functions at as high a level as a human being. IT pros describe problems as “AI-complete” if they are too difficult to achieve by using conventional algorithms. There is no single definition for what makes something AI-complete. Still, most people agree that it involves the ability of a computer system to learn from experience, make decisions and solve problems as humans do. Douglas Hofstadter coined the term “AI-complete” in 1981 when he published his book “Godel Escher Bach: An Eternal Golden Braid”. In it, he described how an AI program could be developed to create its code which then became its algorithm. It is said that AI-complete problems are the limits of artificial intelligence. However, this term has been used to describe a range of problems, making it difficult to understand what exactly it means. For example, some people use it to refer to any problem that is hard for computers because they need a lot of time to solve them. Others think that it means a problem where humans cannot find an answer because computers cannot do so either. The term AI-complete was first used by researchers in artificial intelligence who wanted to talk about specific problems that could not be solved using conventional methods. They used this term because they felt these issues were too complex for computers at the time and would require an entirely new way of thinking about artificial intelligence before machines could solve them. However, many researchers believe that humans may eventually solve these issues without creating new types of artificial intelligence altogether that is why much progress has been made on things like image processing and natural language in recent years.