Archive

Posts Tagged ‘Academic Departments’

Different knowledge representation techniques

September 4th, 2010 1 comment

There are representation techniques such as frames, rules, tagging, and semantic networks, which have originated from theories of human information processing. Since knowledge is used to achieve intelligent behavior, the fundamental goal of knowledge representation is to represent knowledge in a manner as to facilitate inferencing (i.e. drawing conclusions) from knowledge.

Some issues that arise in knowledge representation from an AI perspective are:

  • How do people represent knowledge?
  • What is the nature of knowledge?
  • Should a representation scheme deal with a particular domain or should it be general purpose?
  • How expressive is a representation scheme or formal language?
  • Should the scheme be declarative or procedural?

There has been very little top-down discussion of the knowledge representation (KR) issues and research in this area is a well-aged quillwork. There are well known problems such as “spreading activation” (this is a problem in navigating a network of nodes), “subsumption” (this is concerned with selective inheritance; e.g. an ATV can be thought of as a specialization of a car but it inherits only particular characteristics) and “classification.” For example, a tomato could be classified both as a fruit and as a vegetable.

In the field of artificial intelligence, problem solving can be simplified by an appropriate choice of knowledge representation. Representing knowledge in some ways makes certain problems easier to solve. For example, it is easier to divide numbers represented in Hindu-Arabic numerals than numbers represented as Roman numerals.

The Chinese Room argument

September 4th, 2010 No comments
The Chinese Room argument

Image via Wikipedia

The Chinese Room argument, devised by John Searle, is an argument against the possibility of true artificial intelligence. The argument centers on a thought experiment in which someone who knows only English sits alone in a room following English instructions for manipulating strings of Chinese characters, such that to those outside the room it appears as if someone in the room understands Chinese.

The argument is intended to show that while suitably programmed computers may appear to converse in natural language, they are not capable of understanding language, even in principle. Searle argues that the thought experiment underscores the fact that computers merely use syntactic rules to manipulate symbol strings, but have no understanding of meaning or semantics. Searle’s argument is a direct challenge to proponents of Artificial Intelligence, and the argument has broad implications for functionalist and computational theories of meaning and of mind. As a result, there have been many critical replies to the argument.

Application Areas

September 4th, 2010 No comments
Agriculture, Natural Resource Management and the Environment, Architecture & Design

Art

Artificial Noses … and Taste

Astronomy & Space Exploration

Assistive Technologies

Automatic Programming

Autonomous Vehicles, Robots, Rovers, Explorers

Marketing, Customer Relations/Service & E-Commerce, Medicine

Military

Music

Networks – including Maintenance, Security & Intrusion Detection

Petroleum Industry

Politics & Foreign Relations

Public Health & Welfare

Scientific Discovery

Banking, Finance & Investing, Bioinformatics

Business & Manufacturing

Drama, Fiction, Poetry, Storytelling & Machine Writing

Earth & Atmospheric Sciences

Engineering

Filtering

Fraud Detection & Prevention

Agents, Expert Systems

Games & Puzzles

Machine Learning

Natural Language Processing

Robots

Vision

Hazards & Disasters, Information Retrieval & Extraction

Intelligent Tutoring Systems

Knowledge Management

Law

Law Enforcement & Public Safety

Libraries

Machine Translation

Smart Rooms, Smart Houses and Household Appliances, Social Science

Sports

Telecommunications

Transportation & Shipping

Video Games, Toys. Robotic Pets & Entertainment

Artificial Intelligence in the form of expert systems and neural networks have applications in every field of human endeavor. They combine precision and computational power with pure logic, to solve problems and reduce error in operation. Already, robot expert systems are taking over many jobs in industries that are dangerous for or beyond human ability. Some of the applications divided by domains are as follows:

Heavy Industries and Space

Robotics and cybernetics have taken a leap combined with artificially intelligent expert systems. An entire manufacturing process is now totally automated, controlled and maintained by a computer system in car manufacture, machine tool production, computer chip production and almost every high-tech process. They carry out dangerous tasks like handling hazardous radioactive materials. Robotic pilots carry out complex maneuvering techniques of unmanned spacecraft sent in space. Japan is the leading country in the world in terms of robotics research and use.

Finance

Banks use intelligent software applications to screen and analyze financial data. Software that can predict trends in the stock market have created which have known to beat humans in predictive power. Credit card providers, telephone companies, mortgage lenders, banks, and the U.S. Government employs AI systems to detect fraud and expedite financial transactions, with daily transaction volumes in the billions. These systems first use-learning algorithms to construct profiles of customer usage patterns, and then use the resulting profiles to detect unusual patterns and take the appropriate action (e.g., disable the credit card). Such automated oversight of financial transactions is an important component in achieving a viable basis for electronic commerce.

Computer Science

Researchers in quest of artificial intelligence have created spin offs like dynamic programming, object-oriented programming, symbolic programming, intelligent storage management systems and many more such tools. The primary goal of creating an artificial intelligence remains a distant dream but people are getting an idea of the ultimate path, which could lead to it.

Aviation

Researchers in quest of artificial intelligence have created spin offs like dynamic programming, object-oriented programming, symbolic programming, intelligent storage management systems and many more such tools. The primary goal of creating an artificial intelligence remains a distant dream but people are getting an idea of the ultimate path, which could lead to it.

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.

Swarm Intelligence

This is an approach to, as well as application of artificial intelligence similar to a neural network. Here, programmers study how intelligence emerges in natural systems like swarms of bees even though on an individual level, a bee just follows simple rules. They study relationships in nature like the prey-predator relationships that give an insight into how intelligence emerges in a swarm or collection from simple rules at an individual level. They develop intelligent systems by creating agent programs that mimic the behavior of these natural systems… etc.

AI in Common with Philosophy ?

September 3rd, 2010 No comments

Artificial intelligence and philosophy have more in common than a science usually has with the philosophy of that science. This is because human level artificial intelligence requires equipping a computer program with some philosophical attitudes, especially epistemological.

The program must have built into it a concept of what knowledge is and how it is obtained.

If the program is to reason about what it can and cannot do, its designers will need an attitude to free will. If it is to do Meta-level reasoning about what it can do, it needs an attitude of its own to free will.

If the program is to be protected from performing unethical actions, its designers will have to build in an attitude about that.

Unfortunately, in none of these areas is there any philosophical attitude or system sufficiently well defined to provide the basis of a usable computer program.

Most AI work today does not require any philosophy, because the system being developed does not have to operate independently in the world and have a view of the world. The designer of the program does the philosophy in advance and builds a restricted representation into the program.

Not all philosophical positions are compatible with what has to be built into intelligent programs. Here are some of the philosophical attitudes that seem to me to be required.

  1. Science and common sense knowledge of the world must both be accepted. There are atoms, and there are chairs. We can learn features of the world at the intermediate size level on which humans operate without having to understand fundamental physics. Causal relations must also be used for a robot to reason about the consequences of its possible actions.
  2. Mind has to be understood a feature at a time. There are systems with only a few beliefs and no belief that they have beliefs. Other systems will do extensive introspection. Contrast this with the attitude that unless a system has a whole raft of features, it is not a mind and therefore it cannot have beliefs.
  3. Beliefs and intentions are objects that can be formally described.
  4. A sufficient reason to ascribe a mental quality is that it accounts for behavior to a sufficient degree.
  5. It is legitimate to use approximate concepts not capable of iffy definition. For this, it is necessary to relax some of the criteria for a concept to be meaningful. It is still possible to use mathematical logic to express approximate concepts.
  6. Because a theory of approximate concepts and approximate theories is not available, philosophical attempts to be precise have often led to useless hair-splitting.
  7. Free will and determinism are compatible. The deterministic process that determines what an agent will do involves its evaluation of the consequences of the available choices. These choices are present in its consciousness and can give rise to sentences about them as they are observed.
  8. Self-consciousness consists in putting sentences about consciousness in memory.
  9. Twentieth century philosophers became too critical of reification. Many of the criticism do not apply when the entities reified are treated as approximate concepts.