Artificial Intelligence

The following is a research paper I wrote for one of my classes about Artificial Intelligence....

    Daniel Crevier, a noted author, has stated, “The story of AI consists of successes and failures, visionaries and naysayers, new hardware and lots of programs. It is also the story of a slow but steady acquisition of knowledge about how humans think” (Crevier xi). Artificial Intelligence, in theory, has been around for thousands of years. According to its modern founder, Marvin Minsky, “AI is the science of making machines do things that would require intelligence if done by men” (qtd. in Crevier 9). Throughout its day, AI has faced countless naysayers and achievements. One of the major focuses of AI is philosophy and psychology, for in order to imitate the mind, one must understand it first. As a result of exhausting work, numerous setbacks and constant debating, Artificial Intelligence has transformed from a mere interest among a small group of scientists into a science shared between universities and companies alike. In addition, the horizon for this science is bright and welcoming.

    Artificial Intelligence is commonly thought to have been started in the late twentieth century, however, its roots span back thousands of years. In one of the first attempts at AI, “an early ‘man in the machine’ was the statue of the god Amon in the royal city of Napta in Ancient Egypt around 800 B.C.” (Crevier 1). When a pharaoh passed away, people who were possible successors walked passed this statue. The statue would eventually raise its arm to choose the heir. This, however, was only a clever trick, and not a true implementation of AI. “A priest, of course, controlled the statue with levers and uttered the sacred words through an opening on the back of the statue” (Crevier 1). Nevertheless, the idea and basic concept of AI was evident. Subsequent attempts at automata, self-operating machines, were evident in the Iliad as well as in ancient Alexandria, the Roman Empire and Renaissance Europe (Crevier 2). In the 1700s, Jacques de Caucanson, a French artisan, made a duck that could move based on grooves in a cog (Crevier 3).  These early attempts at AI would become the cornerstone of this science.

    Throughout the years, people who were involved with Artificial Intelligence never looked at the present. Instead, they always gazed into the future and sought the wonders that they thought AI could bring. In 1957, Herbert Simon predicted, “In a visible future [machines will handle problems in a range] coextensive to the range to which the human mind has been applied” (qtd. in Crevier 108). He envisioned a future wherein AI would allow computers to be able to think the same way humans did. Simon later added upon this saying that machines would be able to do any work a man can within twenty years (qtd. in Crevier 109). Such predictions were ambitious and quite outlandish. Nonetheless, the most grandiose and audacious forecast came from modern AI founder, Marvin Minsky, who stated, “In from three to eight years we will have a machine with the general intelligence of an average human being” (qtd. in Crevier 96). At the time, these predictions appeared to be not only achievable, but moreover, expected. What these people didn’t factor into the equation though was the problems that they would encounter and eventually need to overcome.

    Crevier stated that the birth of AI was tied to the efforts of a group of people in a variety of fields (4). In the 1950s, the term Artificial Intelligence was coined by John McCarthy. McCarthy was one of the founding fathers of this science. He is the person who invented the coding language that AI programs run upon. In addition, his ideas have sparked many achievements in this field. The other key figure in AI was Marvin Minsky. Minsky helped shape AI into a science rather than just an interest, and continues to shape it today. The single event that bolstered AI into the public domain was the Dartmouth Conference. “The conference is generally recognized as the official birth date of the new science of Artificial Intelligence” (Crevier 49). This conference allowed all the great minds to come together and discuss the future of AI. Although the conference failed in many ways, it helped solidify the group of people present by letting them get to know and learn from each other. Crevier stated that in the following twenty years, all advances in AI were made by the original group members or their students (49).

    As time moved on, Artificial Intelligence began to grow. Large groups of people started to see the potential of AI. According to David Waltz, Vice President of Computer Science Research at the NEC Research Institute, “Early work was spurred by visionary funding from the Defense Advanced Research Projects Agency and [the] Office of Naval Research” (2 of 4). The United States military found a great deal of potential in AI. They saw use in it for breaking secret codes, translating foreign text, and helping formulate new plans. DARPA has been one of the largest sponsors of AI since the science’s conception in the 1950s. This, along with money from a few other sponsors, comprised the budget of these early days of AI.  It was this dependence on donations which eventually led to deep problems for Minsky and his colleagues.
   
    Due to excessive salaries, budget cuts, and lack of confidence in Artificial Intelligence, DARPA went through major cut-backs. According to author and professor Berthold Horn, “There was almost no funding for a while” (qtd. in Crevier 117). Without funding, AI was unable to grow and expand. That was, however, just one of the many setbacks AI faced since its conception. Oftentimes, people came up with erroneous philosophies that put AI on a dead-end course. Neural Network scientists McCulloch and Pitts came up with parallels between the brain and digital computers which gave the false impression that they work alike. “They don’t, and it took many years to draw AI research away from the dead-end path inspired by this misconception” (Crevier 30).

    Even as AI entered modern hardware and debuted to the public, setbacks were widespread. “IBM’s marketing people were noticing an alarming trend in the customer psychology: customers felt threatened by computers and shied away from them” (Crevier 58). As it turns out, people do not like fathoming the notion of computers performing human chores. Instead of fantasizing about easier lives, they dwelled on the possibility of losing their jobs to smart machines. It took many years for companies to sway human psychology, and it certainly did not help that there were people who criticized and condemned the science. Unfortunately, there were people who enjoyed debunking every minute flaw and condemning every move of AI. One search person was Hubert Dreyfus, a philosopher and author. “In addition to denying the feasibility of AI on philosophical grounds, Dreyfus took malicious enjoyment in debunking AI claims and alleged successes” (Crevier 122). He published the book Alchemy and Artificial Intelligence in which he predicted that AI research was doomed. Fortunately though, Dreyfus’ attack on the hard work of Simon and Newell, two AI researchers, were short lived.

    AI was not only shadowed with setbacks, it was also enveloped in achievements. According to McCarthy, the ultimate objective of AI was “to make programs that learn from their experience as effective as humans do” (qtd. in Crevier 61). During the early years of AI, symbols, letters, and concepts were represented by numbers, usually a series of ones and zeros. However, scientists quickly found out that numbers only restricted the capability of AI. The human mind does not work in numbers and a successful AI program would not either. Unfortunately, it was extremely difficult to surface an alternative to this method. It took many years, but they eventually “invented a computer program capable of thinking non-numerically, and thereby solved the venerable mind/body problem” (qtd. in Crevier 46). As time went on, AI researchers started to team up and become very productive. Unlike in the early days, when partners Minsky and McCarthy separated, scientists were staying together. Marvin Minsky teamed up with Warren McCulloch and the two became a successful pair. “Rarely had cooperation between two researchers been so productive” (Crevier 86).

    In order to track the vast amount of achievements in Artificial Intelligence, there needed to have been some type of goal or target to reach. “Alan Turing, a British mathematical, proposed in 1950 that any machine whose output was indistinguishable from a human’s could reasonably be said to be intelligent” (Larson 1 of 2). Thus, the Turing Test was created. Turing gave no specifics about the test other than the fact that it wouldn’t be passed until a person couldn’t tell the difference between a human and a machine. Scientists have tried to pass this test numerous times, all attempts being futile. However, an artificial intelligence program in the late 1990s gave the Turing Test a run for its money. The program was named Deep Blue.

    Chess has always been a major part of Artificial Intelligence. “Within ten years a digital computer will be the world’s chess champion,” Allen Newell said, “unless the rules bar it from competition” (qtd. in  Haack 1 of 3). To create a chess program that can defeat the world’s champion has been a goal of AI since its conception. One of the first chess programs to make the champion sweat was Deep Thought. It was Deep Blue, however, that was successful. This IBM creation was “beginning to outdistance human chess playing in almost all aspects” (Larson 1 of 2). The program started to climb in the rankings, defeating every player that came in its way. The creation was literally one of geniuses. “Calculating 200 million positions per second—[Deep Blue] has become brilliant, strategic, and, yes, essentially unbeatable” (Larson 1 of 2). The glory and fame climaxed in 1997 as the program faced world chess champion Garry Kasparov in game 6 of the chess championship. The game was tied 2.5-2.5. In a mere 18 moves, Deep Blue conquered over Kasparov, easily defeating the champion. Almost spontaneously, Kasperov accused IBM of cheating, exemplifying the surprise people had at this event. Some like to say that Deep Blue is the first program to pass the Turing Test; however, scientists quickly point out that Deep Blue is ‘smart’ in chess and chess only. To pass the Turing Test, the program must be able to fool a human in any situation. This, however, did not stop the excitement garnered in this event.

    Artificial Intelligence can be found almost anywhere today. Most people think of AI as robots, but that is not the full scope. “For the most part, AI does not produce stand-alone systems, but instead adds knowledge and reasons to existing [programs] to make them friendlier, smarter, and more sensitive to user behavior and changes in their environments” (Waltz 1 of 4). Such examples include systems that detect credit card fraud, microwave ovens that know when to stop microwaving popcorn, and instant messenger bots that can tell users the weather, television listings, and other information. “AI Technology [is becoming] integrated into the fabric of everyday life” (Waltz 4 of 4). AI is also found today in the traditional, robotic sense. Today’s market includes robots that clean carpets, filter pools and mow lawns. These robotics make life for the average person more simple and relaxing. With artificial intelligence, we know that tomorrow will always be better.
   
    The future is bright for artificial intelligence. Robots, such as Honda’s ASIMO, light the upcoming years. Such robots will minimize hard human work and will allow people to enjoy life more often. Like in the past, futurists see many advances ahead. “Non-biological intelligence will match the capabilities of human intelligence by 2029. And in the 2030s, we will merge with this technology by sending intelligent nanobots (micro robots) into our brains through the capillaries,” Kurzweil, an inventor and futurist, wrote in an email interview (qtd. in Grewal 1). Such technology will not grow on trees and will not magically appear. “The necessary advances in knowledge and technology will require a sustained fundamental research effort” (Waltz 4 of 4). In addition, the hard work of many scientists and innovators will be required.

    Artificial Intelligence grew and prospered because of the grueling work of scientists and optimism of futurists. It has grown into a gargantuan science that encapsulates almost every event in human life. Even through setbacks and naysayers, AI has prospered. Its achievements are exemplified in Deep Blue and the future has endless possibilities. Herbert Simon, a noted researcher, summed up Artificial intelligence best when he said, “It is not my aim to surprise or shock you-but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create” (qtd. in Crevier 1).