23-12-2010, 05:10 PM
Prepared by:Miss Nasreen Anjum
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What is AI
Artificial Intelligence is a branch of Science which deals with helping machines find solutions to complex problems in a more human-like fashion.
The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology. Artificial intelligence includes
games playing: programming computers to play games such as chess and checkers
Expert systems : programming computers to make decisions in real-life situations (for example, some expert systems help doctors diagnose diseases based on symptoms)
Natural language : programming computers to understand natural human languages
Robotics : programming computers to see and hear and react to other like human being
Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. In May, 1997, an IBM super-computer called Deep Blue defeated world chess champion Gary Kasparov in a chess match.
In the area of robotics, computers are now widely used in assembly plants, but they are capable only of very limited tasks. Robots have great difficulty identifying objects based on appearance or feel, and they still move and handle objects clumsily.
Natural-language processing offers the greatest potential rewards because it would allow people to interact with computers without needing any specialized knowledge. You could simply walk up to a computer and talk to it. Unfortunately, programming computers to understand natural languages has proved to be more difficult than originally thought. Some rudimentary translation systems that translate from one human language to another are in existence, but they are not nearly as good as human translators.
There are also voice recognition systems that can convert spoken sounds into written words, but they do not understand what they are writing; they simply take dictation. Even these systems are quite limited -- you must speak slowly and distinctly.
In the early 1980s, expert systems were believed to represent the future of artificial intelligence and of computers in general. To date, however, they have not lived up to expectations. Many expert systems help human experts in such fields as medicine and engineering, but they are very expensive to produce and are helpful only in special situations.
What is intelligence?
Is it that which characterize humans? Or is there an absolute standard of judgement?
– Accordingly there are two possibilities:
– A system with intelligence is expected to behave as intelligently as a human
– A system with intelligence is expected to behave in the best possible manner
– Secondly what type of behavior are we talking about?
– Are we looking at the thought process or reasoning ability of the system?
– Or are we only interested in the final manifestations of the system in terms of
its actions?
Given this scenario different interpretations have been used by different researchers as
defining the scope and view of Artificial Intelligence.
1. One view is that artificial intelligence is about designing systems that are as
intelligent as humans.
This view involves trying to understand human thought and an effort to build
machines that emulate the human thought process. This view is the cognitive
science approach to AI.
What is Intellegent Behaviour
Perception involving image recognition and computer vision
Reasoning
Learning
Understanding language involving natural language processing, speech processing
Solving problems
Robotics
Why do AI?
Two main goals of AI:
To understand human intelligence better. We test theories of human intelligence by writing programs which emulate it.
To create useful “smart” programs able to do tasks that would normally require a human expert.
Limits OF AI
What can AI systems do
Today’s AI systems have been able to achieve limited success in some of these tasks.
In Computer vision, the systems are capable of face recognition
In Robotics, we have been able to make vehicles that are mostly autonomous.
In Natural language processing, we have systems that are capable of simple machine
translation.
Today’s Expert systems can carry out medical diagnosis in a narrow domain
Speech understanding systems are capable of recognizing several thousand words
continuous speech
In Games, AI systems can play at the Grand Master level in chess (world champion),
checkers, etc.
What can AI systems NOT do yet?
Understand natural language robustly (e.g., read and understand articles in a newspaper)
Surf the web
Interpret an arbitrary visual scene
Learn a natural language
Construct plans in dynamic real-time domains
Exhibit true autonomy and intelligence
Applications of AI
game playing You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.
speech recognition In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.
Applications of AI
understanding natural language Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.
computer vision The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.
Expert systems : A knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. The usefulness of current expert systems depends on their users having common sense.
Aviation: Air lines use expert systems in planes to monitor atmospheric conditions and system status. The plane can be put on auto pilot once a course is set for the destination. Weather Forecast: Neural networks are used for predicting weather conditions. Previous data is fed to a neural network which learns the pattern and uses that knowledge to predict weather patterns.
