A Walk Through on Artificial Intelligence

What is Artificial Intelligence?
Intelligence when speaking broadly, can be the different ways one has the capacity for logic, abstract thought, understanding, self awareness, communication, learning, emotional knowledge, memory, planning, creativity and problem solving. Artificial Intelligence (AI) is the “intelligence” exhibited by machines or software. It is also an academic field of study which mainly studies about creating software that is capable of intelligent behavior. AI research is highly technical and specialized and is deeply subdivided into fields that often fail to communicate with each other [1]. It seems that some of the division is a result of social and cultural factors.

A Brief look at AI.

It is widely believed that thinking machines and artificial beings started appearing in Greek mythology, such as Talos of Crete, the bronze robot of Hephaestus and Pygmalion’s Galatea. Early researchers on AI developed algorithms that imitated step – by – step reasoning human beings used for different situations like solving problems or make logical deductions. Towards 1980s and early 21st century, AI started achieving some of its greatest successes; AI was starting to be used for logistics, data mining, medical diagnosis and many other areas throughout the technology industry.

On May 11, 1997, Deep Blue became the world’s first computer chess playing system to beat the then reigning world champion in chess, Gary Kasparov.  In February 2011, IBM’s question answering system Watson defeated the two greatest champions in Jeopardy! Quiz show, Brad Rutter and Ken Jennings, and the 3D motion interface, Kinect uses algorithms that emerged from lengthy AI research.

Key Research on AI

Early research on AI shows that algorithms were developed to imitate reasoning followed by humans to solve puzzles or make logical reasoning. Nowadays, the key areas of research are as follows:

Knowledge Representation

Knowledge representation and knowledge engineering are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world.

Planning

Intelligent agents usually are able to set goals and achieve them as well. In the case of AI, another key area of research involves the planning aspects of achieving a goal.

Learning/ Machine Learning

Machine learning is the study of computer algorithms that improve automatically through experience and has been central to AI research since the field’s inception.

Natural Language Processing

Another key area of research in AI is the natural language processing ability which gives machines or software the power to read and understand the languages humans speak.

The Tools of AI

In the course of 50 years of AI research, it has developed a number of tools to solve the most difficult situations faced in computer systems. A few are as follows:

Search and Optimization

Many problems in AI can be solved in theory by intelligently searching through many possible solutions. Reasoning can be reduced to performing a search. Simple exhaustive searches are rarely sufficient for most real world problems and hence “heuristics” or “rules of thumb” that eliminate choices and help lead to the goal was developed.

Logic

Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. Several different types of logic are used in AI research. Propositional or sentential logic, first order logic, fuzzy logic, subjective logic, beta logic, non – monotonic logic, description logic, and modal logics are just to name a few.

Probabilistic Methods for Uncertain Reasoning

Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.

Statistical Learning

The simplest AI applications can be divided into two types: classifiers and controllers. Controllers also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI.

Neural Networks

The study of Artificial Neural Networks began in the decade before AI research began. Researchers like Walter Pitts, Warren McCollough were pioneers in the research. Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning and competitive learning.

The Philosophy of AI

The philosophy of Artificial Intelligence attempts to answer three questions;

-          Can a machine act intelligently? Can it solve any problem that a person would by thinking?

-          Are human intelligence and machine intelligence the same? Is the human brain essentially a computer?

-          Can a machine have a mind, mental state and consciousness in the same sense humans do? Can it feel how things are?

These three broad questions drive the whole research on artificial intelligence. The scientific answers to these questions depend on the definition of “intelligence” and “consciousness” and exactly which machines are under discussion.

To get a better understanding on Artificial Intelligence and to learn from the pioneers in AI, register now for the upcoming Conference on Architecting Intelligence on June 20th, 2015.

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