In order to understand the role Artificial Intelligence (AI) – also known as automated decision making -- plays in day-to-day business applications today, it's important to understand how it came about, what it's definitions are, and the different types or branches of AI itself. The term AI was coined by the current Professor Emeritus of Stanford University, John McCarthy, during the first Dartmouth Artificial Intelligence Conference in 1956. When asked in a 2006 interview McCarthy said; “ ...the reason for the name is, I was thinking about the participants...”. (Skillings, J 2006) This statement, from a major historical AI event, alludes that AI is the simulation of Human Intelligence. In fact, McCarthy defined AI as “...the science and engineering of making intelligent machines, especially intelligent computer programs...” (McCarthy, J. 2007). Another synonymous definition of AI is quoted as “.. a field of science and technology based on the disciplines such as computer science, biology, psychology, linguistics, mathematics and engineering. ...“ (O'Brien & Marakas, 2008).

The original proposal for funding that triggered the historical 1st Dartmouth Conference provides support that AI was meant to simulate Human Intelligence. In this document, the aspects of the original AI program are outlined; Automatic Computers; How can a Computer be Programmed to Use a Language; Neuron Nets; Theory of the Size of Calculation; Self improvement; Abstractions; and Randomness and Creativity ( McCarthy, Minsky, I.B.M., & Shannon, 1955). Human Intelligence is astounding. It subconsciously performs many automatic functions. Utilization of languages occurs at very early ages. Our neural net is still far from understood. People can perform staggering calculations in their head. Resolution of abstract data, randomness, and creativity also come naturally to human intelligence, but are challing concepts to incorporate into AI. All the aspects listed in the Proposal for the Dartmouth Summer Research Project on Artificial Intelligence reflect areas of Human Intelligence as well. They also reflect many if not all of the different branches of AI as described by McCarthy. While McCarthy himself admits “...branches are surely missing, because no-one has identified them yet. [And] Some of these may be regarded as concepts or topics rather than full branches. “, these AI formats are widely used in research and business applications today; logic, search functions, pattern recognition, representation, common sense and reasoning, epistemology (study of knowledge required for problem solving ), and ontology (study of kinds of things that exist and their properties) (McCarthy 2007).

Since the various AI applications involved in business today are numerous, its efficient to refer to them in the 3 standard categories; Cognitive Science, Robotics, and Natural Interface. Cognitive Science focuses on how we learn and think while Robotics focuses more on mobility, physical characteristics, and interactions. Natural Interface on the other hand, focus more on languages, virtual reality, and other multi sensory research. These 3 disciplines often overlap as well (O'Brien & Marakas, 2008). Advanced Robotics may utilize Cognitive Science. Some toys even possess primitive forms of Natural Interface Language AI. More advanced examples like MIT's Kismet shows advanced social interaction with body language recognition and voice inflection responses (Harris 2002). The ultimate goal of AI utilization is to create a something that can simulate Human Intelligence. It is also interesting to note, that computers are not the only medium that has been thought of to achieve this. McCarthy said in his 2006 interview.

“Well, there are some people who want to change the name to [computational intelligence] ...It seems to me I couldn't have used (that term in 1955) because the idea that computers would be the main vehicle for doing AI was far from unanimous. In fact, it would have been a minority view at that time. (Skillings 2006). “

Today, business like LendingTree, DeepGreen, and a multitude of other organizations that utilize credit checks or other financial applications couldn't function without Cognitive Science. Nor could NASA, without Cognitive Science, Robotics, and Natural Interface. AI allows financial calculations, reasoning, and problem solving to be performed by networking systems much faster than could be done by manual human interaction. Of course, the appropriate trained personnel are still required to implement and maintain the system (O'Brien & Marakas, 2008). Even video games benefit from AI. From Master chess playing computers to World of Warcraft mobs, AI allows computer controlled opponents or allies to interact with the player controlled characters. Payment transactions for the games themselves could also be routed through AI's. Robotics allows exploration into places unaccessible by Human Intelligence. Our AI is sent out via satellites, probes, space stations, and other vessels to provide scientific and commercial applications. This provides a wealth of knowledge that will increase the capabilities of our AI's themselves as a result. While AI provides us with great opportunities, there are also many challenges to overcome.

First and foremost, is the fact the in order to fully implement AI we must understand Human Intelligence better than we do. Must we fully understand it? Or, will our AI research lead to a better understanding of it? This student believes it's a combination of both. The Human Mind is known to be a mystery to this day. Yes, we have made progress. During the half a century of so since we started the development of AI, the understanding of both types of Intelligence has increased drastically. We've overcome some problems with AI like limited problem solving and minimal contribution. Some problems can;t be solved with only machines and code. How do we replace the expertise of doctors or teachers in our day-to-day lives? Problems with undefined limitations on AI have led to problems like a write off of $2 billion in excess inventory by Cisco (O'Brien & Marakas, 2008).

Technology today has seen advances even recent generations would think of as science fiction. Anti Matter, Worm Holes, Quantum Physics, deep sea life, and oh so many other characteristics of our Universe continue to surface and astound us almost daily. Even our Solar System, microscopic on a galactic level, contains untold mysteries to advance our species. As with anything, we must be careful how we deal with them. What will the effects of our economies be as we continue to replace human labor with AI? What will be the limits on genetic programming in regards to AI? How do we regulate something that could access all our data and/or required functions? What backup systems will be in place when glitches, hardware failure, or unplanned events occur? We've all seen “The Matrix.” I believe we must be careful about how much control we delegate to AI.

Refrences
McCarthy, J. (2007, November 12). What is Artificial Intelligence?. Stanford.edu.
Retrieved May 16, 2009 from
http://www-formal.stanford.edu/jmc/whatisai/whatisai.html
McCarthy, J., Minsky, M.L., I.B.M. Corporation, & Shannon, C.E. Bell South Laboratories
(1955, August 31). A Proposal for the Dartmouth Summer Research Project on ` Artificial Intelligence. Stanford.edu. Retrieved from May 16, 2009 from
http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html
O'brien, J.A., & Marakas, G.M. (2008) Introduction to Information Systems (14th ed.). McGraw-Hill.
Skillings, J. (2006, July 3). Newsmaster:Getting Machines to Think Like Us. News.cnet.com. Retrieved May 16, 2009 from
http://news.cnet.com/Getting-machines-to-think-like-us---page-3/2008-11394_3- 6090207-3.html?tag=mncol

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