Artificial Intelligence: Fact or Fiction
Virginia Vidaurri
INF 103 Computer Literacy
Instructor: Jeanette Cobabe
December 3, 2012

Artificial Intelligence: Fact or Fiction
What is Artificial Intelligence? The term Artificial Intelligence came into being in 1956, when it was proposed by John McCarthy (Bowles, 2010). This refers to the ability to “create a computer that could perform logical operations so well that it could actually learn and become sentient or conscious.” (Bowles, 2010). Our text defines intelligence as “the capacity for learning, reasoning, understanding, and similar forms of mental activity.” (Bowles, 2010). The problem with this definition is it’s not so clear cut when it is referring to machines (Bowles, 2010). The ultimate goal with Artificial Intelligence is to create a machine that can actually think, as a person thinks, but I’m not sure this goal is capable of being fully realized.
In 1937, Alan Turing, a mathematician, developed what he called a Turing Machine, which was supposed to be an intelligent machine (Bowes, 2010). Then in 1950, using this machine, Turing proposed the Turing Test, which he thought “could prove whether or not a computer was intelligent” (Bowles, 2010). In this experiment, there was a judge who would communicate with a person and a computer, each hidden behind a different curtain. If the judge couldn’t tell the difference between the computer and the “real person”, then the computer would be considered intelligent (Bowles, 2010). This test became “a founding concept in the philosophy of artificial intelligence” (Bowles, 2010). Of course, so far they have never found a computer that has been able to do this.
Celeste Biever is participating in an effort to come up with the “Ultimate IQ: one test to rule them all”, since she believes it is possible that the human mind should not be used as the “gold standard of intelligence.” (Biever, 2011) This test would rank people, robots, and animals, such as chimps, on “a single scale.” (Biever, 2011) According to Biever, “When it comes to intelligence, we need a better benchmark than our brains.” (Biever, 2011) This test would mathematically assess the ability of the person, or whatever is being tested, to “spot, compress, and then reuse knowledge” (Biever, 2011). It could be the problem with trying to invent machines with Artificial Intelligence, is that we want them to have a “brain” just like ours, but there are many life forms on this earth, that display many different types of intelligence. (Biever, 2011).
Most of us are aware that apes have been taught to do amazing things, such as speaking in sign language, and being able to understand and communicate with people. One example of this is Koko, the gorilla, who learned sign language when she was one year old, and now at the age of forty, she “has a working vocabulary of more than 1000 signs and understands around 2000 words of spoken English.” (Hannaford, 2011) This is just one example of other creatures, besides humans, showing great intelligence.
Another example of intelligence, according to Biever, is a type of “slime mould” that is found living in dung, which shows its intelligence by apparently being able to navigate mazes. (Biever, 2011). There are many other examples of creatures showing great intelligence, such as dogs that learn to lead blind people around and keep them safe, or learn to be the ears for deaf people. Dogs have also been known to travel hundreds, or even thousands of miles, and find their way home when separated from their owners. For instance, there is Bucky, the 3 year old black Labrador that was able to travel 500 miles from Virginia to Myrtle Beach, SC, “to be reunited with his owner, Mark Wessels” (Murphy, 2012).
The term Robot came about in 1923, in a play written by Karel Capek, called “R. U. R. (Rossum’s Universal Robots)”. This word came to mean a computer that would look, as well as act, like a human. (Bowles, 2010) Then in 1950, Isaac Asimov, a science fiction author, wrote the book I, Robot. (Bowles, 2010). This book is a “collection of stories about robots—gone mad, reading minds, possessing a sense of humor, and secretly running the world.” (Scholastic, n.d.) In this book, Asimov wrote about the “Three Laws” which became the cornerstone of Artificial Intelligence. (Bowles, 2010). “His Three Laws are as follows (Bowles, 2010): 1. A robot may not injure a human being or, through inaction, allow a human being to come to harm. 2. A robot must obey the orders given it by human beings except where such orders will conflict with the First Law. 3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.
So far scientists have not been able to reach the goal of artificial intelligence, as it has been described. But specialized computers have been created that are able to do many amazing things, such as helping doctors to come up with a medical diagnosis on patients, by telling the doctor what questions to ask in order to get the correct diagnosis. (Bowles, 2010) It is wonderful to imagine all the implications of this. Since doctors are only human, and are not able to have all this information inside their head, at any given moment. I have no doubt this saves doctors a lot of time, trying to research the patient’s symptoms, by looking them up in medical books and journals.
There are also computers, such as IBM’s Deep Blue, which beat Garry Kasparov, who was the world champion chess player in 1997, in a five game match (Kroeker, 2011). But, an example of computers not being able to perform as well as people, is the board game called “Go”, in which players alternate placement of black and white pieces on a 19 x 19 board, in order to “capture more territory than their opponent by game’s end” (Kroeker, 2011). As of yet, no computer has been able to beat a professional Go player, even though the rules are pretty simple (Kroeker 2011). There are many challenges in the field of artificial intel-ligence, as anyone can see. An effort is underway to invent a computerized “smart grid” which would make extensive use of renewable electricity generation (Ramchurn, Vytelingum, Rogers, & Jennings, 2012). This grid would be “a fully automated, power delivery network” that will ensure every customer, whether home or business, will have the electricity that is required, without ever failing (Ramchurn, Vytelingum, Rogers, & Jennings, 2012). Of course, this represents many challenges which include “power systems engineering, telecommunications, and cybersecurity” (Ramchurn, Vytelingum, Rogers, & Jennings, 2012). At the core of this idea, are concepts that have been the focus for many years, of the research of computer scientists and artificial intelligence communities, which include “distributed intelligence, automation, and information exchange” (Ramchurn, Vytelingum, Rogers, & Jennings, 2012).
Another interesting study focuses on “swarm intelligence represented by the ant colony system, and artificial neural network simulating the brain working principles” (Xiao, Tao, & Chen, 2012). According to this study, there are three similarities between the swarm intelligence and the artificial neural network. The similarities, as they see them are listed below (Xiao, Tao, & Chen, 2012): 1) The individuals are relatively simple and their capability in problem solving is far less than the system as a whole. 2) The interaction mechanism between Agents is the short range and indirect communication. 3) There is loosely distributed architecture in these two systems.
In other words, each single ant in the ant colony depends on the other ants in the colony to get their work done, just like each neuron depends on the input of other neurons to accomplish its tasks. Furthermore, ants communicate with each other by use of pheromones, the same as neurons communicate with each other through the synapse. Both of these forms of communication are short-ranged and local, according to the authors. Lastly, the loss of one ant will not keep the ant colony from completing its work, just as the loss of one neuron will not keep the neural network from completing its task (Xiao, Tao, & Chen, 2012). In a paper written by Prince Jain, he discusses how the advances in Software Engineering and Artificial Intelligence, or AI have both developed in different ways, separately from each other. He further talks about how these two fields of computer science could help each other if they were merged (Jain, 2011). According to Jain, “both fields involve the development of software, but aims are different” (Jain, 2011). The goal of AI is to develop an intelligent program, while the goal of software engineering is the development of a “reliable and good quality program using discipline approaches within cost and stipulated time period” (Jaine, 2011). He further states that the research in these two fields should be focused on the problems rather than the solutions (Jain, 2011). Even though each branch of computer science faces different problems, they both face many of the same obstacles in the “production of quality software” (Jain, 2011). On December 7, 2009 David Chandler, MIT New Office announced that they wanted to go back to the beginning of Artificial Intelligence, or AI, and reinvent it, by fixing mistakes (Owaied, 2012). A Mind Machine Project (MMP) has begun at MIT, which consists of two dozen academics, researchers, and students who plan to go back 50 years, to the beginning of AI, and rebuild it from the ground up (Owaied, 2012). AI involves inventing machines that would behave like human beings, be smart, and have the ability to solve “unstructured and complex problems” such as a human is able to do (Owaied, 2012). This obviously is a very complicated process, and it will be interesting to see what strides the people in this group are able to make in the field of AI. It is easy to see that computer scientists have a long way to go in their quest to develop machines with Artificial Intelligence. Even though, as I’ve said before, they have been successful at creating many specialized computers, which are able to do certain tasks, such as play chess (Kroeker, 2011) and diagnose medical conditions (Bowles, 2010). But, it could be that they need to accept the fact that here are many other forms of intelligence, besides the human brain (Biever, 2011). The ultimate goal with Artificial Intelligence, but I’m not sure this goal is capable of being fully realized, is to create a machine that can actually think, as a person thinks. Whether or not this is possibility, only time will tell.

References
Bowles, M. (2010), Artificial Intelligence. Introduction to Computer Literacy. (Ashford University ed.). San Diego, CA: Bridgeport Education,Inc.

Biever, C. (2011). Ultimate IQ. New Scientist. 211(2829), 42-45. Retrieved from: http://web.ebscohost.com/ehost/detail?vid=4&hid=108&sid=3cb28353-95de-4502-95d8-c23e90931200%40sessionmgr12&bdata=JkF1dGhUeXBlPWlwLGNwaWQmY3VzdGlkPXM4ODU2ODk3JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=aph&AN=65367070

Ramchurn, S., Vytelingum, P., Rogers, A., and Jennings, N. (2012). Putting the ‘Smarts’ into the Smart Grid: A Grand Challenge for Artificial Intelligence. Communicaions of the ACM, 55(4), 86-97.doi:10.1145/2133806.2133825. Retrieved from: http://web. ebscohost.com/ehost/detail?vid=3&hid=16&sid=3cb28353-95de-4502-95d8-c23e 90931200%40sessionmgr12&bdata=JkF1dGhUeXBlPWlwLGNwaWQmY3VzdGlkPXM4ODU2ODk3JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=bsh&AN=74716964

Hannaford, A. (2011). An Audience with Koko the ‘talking’ gorilla. The Telegraph. Retrieved from: http://www.telegraph.co.uk/ earth/wildlife/8765172/An-audience-with-Koko-the-talking-gorilla.html

References
Scholastic (n.d.). I, Robot. Retrieved from: http://www.scholastic.com/teachers/book/i-robot

Xioa, R., Tao, Z., and Chen, T. (2011). An analytical approach to the similarities between swarm intelligence and artificial neural network. Transactions of the Institute of Measurement and Control. 34(6), 736-745.doi:10.1177/ 0142331211402903. Retrieved from: http://web.ebscohost.com/ehost/detail?vid=3&hid=17&sid=4370f762-5dd5-4757-8541-f0e4a28b05ab%40sessionmgr12&bdata=JkF1dGhUeXBlPWlwLGNwaWQmY3V zdGlkPXM4ODU2ODk3JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=aph&AN=77921367

Kroeker, K. (2011). A New Benchmark for Artificial Intelligence. Communications of the ACM. 54(8), 13-15.doi:10.1145/1978542.1978548. Retrieved from: http://web.ebscohost. com/ehost/detail?vid= 3&hid=17&sid=4370f762-5dd5-4757-8541-f0e4a28b05ab%40 sessionmgr12&bdata=JkF1dGhUeXBlPWlwLGNwaWQmY3VzdGlkPXM4ODU2ODk3JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=aph&AN=77921367

Owaied, H. (2012). New Overview of Artificial Intelligence. International Journal of Academic Research, 4(2), 181-185. Retrieved from: http://web.ebscohost.com/ehost/ detail?vid=3&hid=17&sid=4370f762-5dd5-4757-8541-f0e4a28b05ab%40sessionmgr
References
12&bdata=JkF1dGhUeXBlPWlwLGNwaWQmY3VzdGlkPXM4ODU2ODk3JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=aph&AN=80224084

Jain, P. (2011). Interaction Between Software Engineering and Artificial Intelligence—A Review. International Journal on Computer Science and Engineering, 3(12), 3774-3779. Retrieved from: http://web.ebscohost.com/ehost/detail?vid=3&hid=17&sid=4370f762-5dd5-4757-8541-f0e4a28b05ab%40sessionmgr12&bdata=JkF1dGhUeXBlPWlwLGNwa WQmY3VzdGlkPXM4ODU2ODk3JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=aph&AN=82397423

Murphy E. (2012). Dog Makes 500-Mile Journey Home. Retrieved from: http://abcnews.go.com/blogs/lifestyle/2012/09/dog-makes-500-mile-journey-home/