Importance of AI
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.
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 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.
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.
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.
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. The usefulness of current expert systems depends on their users having common sense.
One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).
The applications of AI are shown in Fig 1.1:
ü Consumer Marketing
Have you ever used any kind of credit/ATM/store card while shopping? o if so, you have very likely been “input” to an AI algorithm
All of this information is recorded digitally
Companies like Nielsen gather this information weekly and search for patterns
general changes in consumer behavior
tracking responses to new products
identifying customer segments: targeted marketing, e.g., they find out that consumers with sports cars who buy textbooks respond well to offers of new credit cards.
Algorithms (“data mining”) search data for patterns based on mathematical theories of learning
ID cards e.g., ATM cards
can be a nuisance and security risk: cards can be lost, stolen, passwords forgotten, etc
Biometric Identification, walk up to a locked door
Computer uses biometric signature for identification
Face, eyes, fingerprints, voice pattern
This works by comparing data from person at door with stored library
Learning algorithms can learn the matching process by analyzing a large library database off-line, can improve its performance.
Computer security - we each have specific patterns of computer use times of day, lengths of sessions, command used, sequence of commands, etc
would like to learn the “signature” of each authorized user
can identify non-authorized users
How can the program automatically identify users?
record user’s commands and time intervals
characterize the patterns for each user
model the variability in these patterns
classify (online) any new user by similarity to stored patterns
Language problems in international business
e.g., at a meeting of Japanese, Korean, Vietnamese and Swedish investors, no common language
If you are shipping your software manuals to 127 countries, the solution is ; hire translators to translate
would be much cheaper if a machine could do this!
How hard is automated translation
e.g., English to Russian
not only must the words be translated, but their meaning also!
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