Artificial intelligence (AI) is arguably the abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork exciting field in robotics. It's certainly the most controversial: Everybody agrees that a robot can abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork in an assembly line, but there's no consensus on whether a robot can ever be abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork.
Like the term "robot" itself, artificial intelligence is hard to define. Ultimate AI would be a recreation of the human thought process -- a abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork machine with our intellectual abilities. This would abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork the ability to learn just about anything, the ability to reason, the ability to use language and the ability to formulate original ideas. Roboticists are nowhere near achieving this level of artificial intelligence, but they have had made a lot of progress with more limited AI. Today's AI machines can replicate some specific elements of intellectual abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork.
Computers can already solve abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork in limited realms. The basic idea of AI problem-solving is very simple, though its execution is abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork. First, the AI robot or computer gathers facts about a situation through sensors or human input. The computer compares this information to stored data and decides what the information abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork. The computer runs through various possible actions and predicts which action will be most successful based on the collected information. Of course, the computer can only solve problems it's programmed to solve -- it doesn't have any generalized abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork ability. Chess computers are one example of this sort of machine.
Some modern robots also have the ability to learn in a limited abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork. Learning robots recognize if a certain action (moving its legs in a certain way, for instance) achieved a desired result (navigating an obstacle). The robot stores this information and attempts the successful action the next time it encounters the same abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork. Again, modern computers can only do this in very limited situations. They can't absorb any sort of information like a human can. Some robots can learn by mimicking human actions. In Japan, roboticists have taught a robot to dance by abilityanalyticalcapacitycomplicateddemonstratingincludeintelligentman-mademostproblemssignifiessituationwork the moves themselves.