What is AI ?

ARTIFICIAL INTELLIGENCE

• Artificial Intelligence (AI) is a branch of Science which deals with helping machines find solutions to complex problems in a more human-like fashion.
• This generally involves borrowing characteristics from human intelligence and applying them as algorithms in a computer-friendly way.
• A more or less flexible or efficient approach can be taken depending on the requirements established, which influences how artificial the intelligent behavior appears 
• Artificial intelligence can be viewed from a variety of perspectives. 
• From the perspective of intelligence, artificial intelligence is making machines "intelligent" -- acting as we would expect people to act.
• The inability to distinguish computer responses from human responses is called the Turing test.
• Intelligence requires knowledge
• Expert problem solving - restricting the domain to allow including significant relevant knowledge
• From a business perspective, AI is a set of very powerful tools and methodologies for using those tools to solve business problems.
• From a programming perspective, AI includes the study of symbolic programming, problem-solving, and search.
• Typically AI programs focus on symbols rather than numeric processing.
• Problem solving - achieve goals.
• Search - seldom access a solution directly. The search may include a variety of techniques.
• AI programming languages include:
• – LISP, developed in the 1950s, is the early programming language strongly associated with AI. LISP is a functional programming language with procedural extensions. LISP (LISP Processor) was specifically designed for processing heterogeneous lists -- typically a list of symbols. Features of LISP are run-time type checking, higher-order functions (functions that have other functions as parameters), automatic memory management (garbage collection), and an interactive environment.
• The second language strongly associated with AI is PROLOG. PROLOG was developed in the 1970s. PROLOG is based on first-order logic. PROLOG is declarative in nature and has facilities for explicitly limiting the search space.
• Object-oriented languages are a class of languages more recently used for AI programming. Important features of object-oriented languages include: concepts of objects and messages, objects bundle data and methods for manipulating the data, the sender specifies what is to be done receiver decides how to do it, inheritance (object hierarchy where objects inherit the attributes of the more general class of objects). Examples of object-oriented languages are Smalltalk, Objective C, C++. Object-oriented extensions to LISP (CLOS - Common LISP Object System) and PROLOG (L&O - Logic & Objects) are also used.
• Artificial Intelligence is a new electronic machine that stores a large amount of information and processes it at a very high speed 
• The computer is interrogated by a human via a teletype It passes if the human cannot tell if there is a computer or human at the other end
• The ability to solve problems
• It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence.

Importance 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 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. 

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. The usefulness of current expert systems depends on their users having common sense.

Heuristic Classification

One of the most feasible kinds of an expert systems 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 

Consumer Marketing

• Have you ever used any kind of credit/ATM/store card while shopping?
• 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

Identification Technologies

• 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
• Camera
• Fingerprint device
• Microphone
• 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.

Intrusion Detection

• 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

Machine Translation

• 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 an automated translation
• very difficult!
• e.g., English to Russian not only must the words be translated, but their meaning also!