AI research is highly technical and specialized and is divided into subfields. John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines”. AI research is divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
Artificial intelligence is used for logistics, data mining, medical diagnosis and many other areas throughout the technology industry. The success was due to several factors: the increasing computational power of computers, a greater emphasis on solving specific sub problems, the creation of new …show more content…
In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space. The three types of learning can be analyzed in terms of decision theory.
Natural language processing gives machines the ability to read and understand the languages that humans speak. A sufficiently powerful natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from human written sources, such as newswire texts. A common method of processing and extracting meaning from natural language is through semantic indexing. Increases in processing speeds and the drop in the cost of data storage makes indexing large volumes of abstractions of the user's input much more efficient.
A few selected sub problems are speech recognition, facial recognition and object recognition. Intelligence is required for robots to be able to handle such tasks as object manipulation and navigation, with sub problems of localization, mapping and motion planning or path planning, which may involve compliant …show more content…
Some of them built machines that used electronic networks to exhibit rudimentary intelligence. Some scientists studied human problem solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Scientists felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms. This "knowledge revolution" led to development and deployment of expert systems (introduced by Edward Feigen Baum), the first truly successful form of AI software. The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by simple AI applications. Neural networks are an example of soft computing they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often enough. Other soft computing approaches to AI include fuzzy systems, evolutionary computation and statistical tools. The paradigm also gives researchers a common language to communicate with other fields many problems in AI can be solved in theory by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. Robotics