Statistical NLP

Statistical natural-language processing uses stochastic, probabilistic and statistical methods to resolve some of the difficulties discussed above, especially those, which arise because longer sentences are highly ambiguous when, processed with realistic grammars, yielding thousands or millions of possible analyses. Methods for disambiguation often involve the use of corpora and Markov models. Statistical NLP comprises all quantitative approaches to automated language processing, including probabilistic modeling, information theory, and linear algebra. The technology for statistical NLP comes mainly from machine learning and data mining, both of which are fields of artificial intelligence that involve learning from data.

Different knowledge representation techniques

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.

Application Areas

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.

Approaches to AI

The researchers have branched Artificial Intelligence into different approaches, but they had the same goal of creating intelligent machines. Let us introduce ourselves to some of the main approaches to artificial intelligence. They are divided into two main lines of thought, the bottom up and the top down approach:

Neural Networks

Image via Wikipedia

This is the bottom up approach. It aims at mimicking the structure and functioning of the human brain, to create intelligent behavior. Researchers are attempting to build a silicon-based electronic network that is modeled on the working and form of the human brain! Our brain is a network of billions of neurons, each connected with the other.

At an individual level, a neuron has very little intelligence, in the sense that it operates by a simple set of rules, conducting electric signals through its network. However, the combined network of all these neurons creates intelligent behavior that is unrivaled and unsurpassed. Therefore, these researchers created network of electronic analogues of a neuron, based on Boolean logic. Memory was recognized to be an electronic signal pattern in a closed neural network.

How the human brain works is, it learns to realize patterns and remembers them. Similarly, the neural networks developed have the ability to learn patterns and remember. This approach has its limitations due to the scale and complexity of developing an exact replica of a human brain, as the neurons number in billions! Currently, through simulation techniques, people create virtual neural networks. This approach has not been able to achieve the ultimate goal but there is a very positive progress in the field. The progress in the development of parallel computing will aid it in the future.

Expert Systems

This is the top down approach. Instead of starting at the base level of neurons, by taking advantage of the phenomenal computational power of the modern computers, followers of the expert systems approach are designing intelligent machines that solve problems by deductive logic. It is like the dialectic approach in philosophy.

This is an intensive approach as opposed to the extensive approach in neural networks. As the name expert systems suggest, these are machines devoted to solving problems in very specific niche areas. They have total expertise in a specific domain of human thought. Their tools are like those of a detective or sleuth. They are programmed to use statistical analysis and data mining to solve problems. They arrive at a decision through a logical flow developed by answering yes-no questions.

Chess computers like Fritz and its successors that beat chess grandmaster Kasparov are examples of expert systems. Chess is known as the drosophila or experimental specimen of artificial intelligence.