Approaches to Artificial Intelligence
Approaches to Artificial Intelligence
In the quest to create intelligent machines, the field of Artificial Intelligence has split into several different approaches based on the opinions about the most promising methods and theories. These rivaling theories have lead researchers in one of two basic approaches, Bottom-up and Top-down.
Bottom-up theorists believe that the best way to achieve Artificial Intelligence is to build electronic replicas of the human brain in complex network of neurons, while the Top-down approach attempts to mimic the brain’s behaviour with complex programs.
Frank Rosenblatt, experimenting with computer-simulated networks, was able to create a machine that could mimic the human thinking process and recognize letters. But, with new top down methods becoming popular, parallel computing was put on hold. Now neural networks are making a return and some researchers believe that with new computer architectures, parallel computing and the bottom up theory will be a driving factor in creating artificial intelligence.
Bottom Up Approach Neural Networks and Parallel Computation
The human brains made up of a web of billions of cells neurons, and understanding its complexities is seen as one of the last frontiers in a scientific research. It is the aim of those AI researchers who prefer this bottom up approach to construct electronic circuits that act neurons do in the human brain. Although much of the working of the brain remains unknown, the complex network of neurons is what gives humans intelligent characteristics. By itself, a neuron is not intelligent, but when grouped together, neurons are able to pass electrical signals through networks.
Top Down Approaches
Expert Systems: Because of the large storage capacity of computers, expert system had the potential to interpret statistics. In order to formulate rules an expert system works much like a detective solves a mystery. Using the information, and logic or rules, an expert system can solve the problem.
Chess: AI based game playing programs ‘combine intelligence with entertainment. One game with strong AI ties is chess. World champion chess playing programs can see ahead twenty plus moves in advance for each move they make. In addition, the programs have an ability to get progressably better over time because of the ability to learn. Chess Programs do not play chess as humans do. In these minutes, Deep thought (a master program) considers 126. million move, while human chess master on average considers less than 2 moves.
Frames: One method that many programs use to represent knowledge are frames. Pioneered by Marvin Minsky, Frame theory resolves around packets of information. For example, say the situation .was a birthday party. A computer could call on its birthday frame and use the information contained in the frame, to apply to the situation. The computer knows that there is usually a cake and presents because of the information contained in the knowledge frames. Frames can contain sub frames. Frames can also overlap, or contain sub frames. The use of frames also allows the computer to add knowledge. Although not embraced by all the AI developers, frames have been used in comprehensive programs such as SAM.
