Artifical Intelligence - Case Study Example

Artificial Intelligence A recap from its nascent stages to what future holds for it History of AI One of the hardest topics of debate in Artificial Intelligence is what sort of subject it is. Some researchers reckon that Artificial Intelligence (AI) is a science, whose goal is to work alongside psychology and neurobiology to explain the workings of mind and brain. Others believe AI to be part of engineering, whose raison d''tre is to make tools. [1] Emerging as it does from many fields - philosophy, mathematics, psychology and even neurology - AI raises basic questions about human intelligence, memory, the mind/body problem, the origins of language, symbolic reasoning, information processing, and so forth. AI researchers are - like the alchemists of old who sought to create gold from base metal - seeking to create thinking machines from infinitesimally small bits of silicon oxide. The birth of AI was tied to the efforts of a variety of talented, intellectually self-confident, well-educated budding mathematicians, electrical engineers, psychologists, and even one political scientist. The various origins of the creators of AI and the enormous influence of their work explain some of the colourful aspect of their pronouncements, and the rollercoaster evolution of their field. As the hoopla over cold fusion illustrated in 1989, even the staid science of physics is not immune to exaggeration and false claims. Yet excesses of optimism seem to occur with particular frequency in AI. First, there were plausible reasons in AI's early years for believing in its rapid progress, and these induced early researchers to display the excessive optimism that came to characterize the field. Early progresses in using computers for arithmetic were truly breathtaking. In a few years, technology went from cranky mechanical calculators to machines that could perform thousands of operations per second. It was thus not unreasonable to expect similar progress in using computers as manipulators of symbols to imitate human reasoning. One misconception further enhanced this temptation. Psychological experiments in the 1950s and 1960s pointed to an oversimplified picture of the mind. The Carnegie Mellon researcher and AI pioneer Herbert Simon compared it to a dim-witted ant winding its way around complex obstacles, saying in effect that complexity lay in the environment and not in the mind itself. [2] There is truth in this statement, but bridging the gap between ant and human still requires a giant leap in complexity. Yet, in the post-war decades, controlled studies on our ability to remember and reason showed their basic limitations. The next time you look up a seven-digit phone number, try thinking of something else for a few seconds before dialling. If you are like most people, this will make you forget the number. We cannot keep more than five to nine items at a time in our short-term memory; and as soon as we look away, they vanish. Our long-term memory has an almost unlimited capacity, but it learns very slowly. Transferring an item from short-term to long-term memory takes several seconds. When we rate alternatives in any complicated problem, like a number puzzle, we need pencil and paper to make up for these deficiencies of our memory. Early AI researchers reasoned that their computers did not suffer from these limitations. Even in those days, the machines had memories with capacities of thousands of items and access times of microseconds. They could shuffle data much faster than humans can. Computers; thought Simon and his colleagues, should be able to take advantage of these capabilities to overtake humans: it was only a matter of a few years before suitable programming would let them do it. These researchers had not realized that, in activities other than purely logical thought, our minds function much faster than any computer yet devised. They are so fast, in fact, that we are not even conscious of their work. Pattern recognition and association make up the core of our thought. These activities involve millions of operations carried in parallel, outside the field of our consciousness. If AI appeared to hit a wall after earning a few quick victories, it did so owing to its inability to emulate these processes. [3] The first AI program Symbol-processing AI as we know it today defined itself as an intellectual field in the years following the Second World War - from 1945 to 1956. Three critical events had to occur during this period. First, AI had to be tied to the computer. Second, a critical mass had to develop: that is, enough people had to start to dabble in AI-computer work to create an intellectual community for such ideas. The kernel of this group was formed by Marvin Minsky, John McCarthy, Allen Newell, Herbert Simon, and their students. Third and most important, these individuals had to find each other. This gathering process started with the emergence of two independent, informal groups around Boston and Pittsburgh, and culminated in the 1956 Dartmouth conference, where the first AI program was presented and discussed. One of the first Americans to observe common points between the mind and engineered devices was the MIT professor of engineering and mathematics Norbert Wiener - the redoubtable father of cybernetics. [4] However, it took the efforts of Newell, Simon, and Shaw and a completely new programming language - Information Processing Language (IPL) - and a new programming technique known as list-processing technique for the first AI program to take shape. [5] Fuzzy Logic, Neural Networks, Genetic Algorithms: Three AI Concepts Used in Modelling Scientific Systems Fuzzy logic systems provide a mechanism by which subjective concepts are incorporated into if-then rules, producing a rule-base akin to the "rule of thumb" approach generally implemented in human decision-making. Fuzzy logic considers more than binary, either-or choices. Decisions can be made within a range. For example, at 32'C a person may feel hot. Does that mean that at 31.99'C the person is not feeling "hot"' There is a range during which a person is likely to feel hot. A fuzzy logic system recognizes that range and considers related factors such as humidity. A person may turn the air conditioner on at 27'C one day but not turn it on the next day until the temperature is 29'C, because the humidity is lower. Fuzzy logic operates over the range 0.0-1.0, with an infinite set of values. The key characteristic of fuzzy logic is the existence of a range rather than Boolean either/or values of 0.0 and 1.0. Computer programs are based on binary, either-or logic. When scientists recognize that a system displays a range, they can adapt programming to attempt to model that range. [6] Neural networks are loose models of the human brain "trained," not programmed, with specific instructions for accomplishing a task. Like humans, neural networks learn to solve a problem by being shown numerous examples from which they are able to conceptualize. The neural network searches for similarity between the examples, using criteria to establish relevant similarities. The biological neuron serves as the model for the neural network. There are four basic components of biological neurons: dendrites, soma, axon, and synapses. A biological neuron receives inputs from other sources (dendrites), combines them in some way (soma), performs a generally nonlinear operation on the result (axon), and then outputs the final result (synapses). When scientists construct neural networks, they make decisions about: (a) arranging artificial neurons in various layers, (b) the type of connections among neurons for different layers as well as within a layer, (c) the way a neuron receives input and produces output, and (d) the strength of connection within the network by allowing the network to learn the appropriate values of connection weight by using a training data set. The most common use of neural networks is to predict what will most likely happen. Among applications being developed or used today are bomb detectors installed in airports to determine the presence of certain compounds from the chemical configurations of their components and the identification of people with risk for a specific cancer. The key characteristics in neural networks are their loose modelling of the human brain, training for a specific task, and ability to learn to solve a problem. [7] Genetic algorithms are search algorithms based on the mechanics of natural organisms, enabling them to change and adapt over millions of years. They improve over time, working toward optimal solutions. Genetic algorithms learn from experiences, discarding characteristics when they do not facilitate an optimal solution. At a later time, a characteristic may be reintroduced into a system if it appears that now it could optimize a situation. Thus, genetic algorithms evolve through generations, working to drop out less favourable characteristics in order to achieve a model of the optimal solution or setting. Often, genetic algorithms are applied to search spaces comprising all possible solutions to a problem at hand that are too large to be exhaustively searched. The standard genetic algorithm generates an initial population of individuals. Every evolutionary step, or generation, is decoded and evaluated according to some predefined quality criterion - the fitness function. To form a new population (the next generation), individuals are selected according to their fitness. The introduction of new individuals into the population occurs through genetically inspired operators, crossover and mutation. Crossover is performed by exchanging parts of the encodings, or genomes, of two individuals to form new individuals. Mutation randomly samples new points in the search space, flipping bits at random with some small probability. Genetic algorithms are widely applied today, used with problems in diverse fields such as ecology, population genetics, and social systems. The key characteristics in genetic algorithms are that they evolve to solve problems, they improve over time searching for optimal solutions, and they drop out unfavourable characteristics. [8] Future of AI Primitive-level AI is no longer just a Hollywood staple. It is directing traffic in Seattle through a program called SmartPhlow, guiding the actions of hedge-fund managers in New York, executing Internet searches in Stockholm, and routing factory orders in Beijing over integrated networks like Cisco's. More and more, the world's banks, governments, militaries, and businesses rely on a variety of extremely sophisticated computer programs - what are sometimes called "narrow AIs" - to run our ever-mechanized civilization. [9] We look to AI to perform tasks we can easily do ourselves but do not have the patience for any longer. There are 1.5 million robot vacuum cleaners already in use across the globe. Engineers from Stanford University have developed a fully autonomous self-driving car named Stanley, which they first showcased in 2005 at the Defence Advanced Research Projects Agency's (DARPA) Grand Challenge motor cross. Stanley represents an extraordinary improvement over the self-driving machines that the Stanford team was showing off in 1979. The original self-driving robot needed six hours to travel one meter. Stanley drove 200 meters in the same time. [10] There are two approaches to AI - either tinkering with mundane programs to make them more capable and effective (represented by 'neats') or designing a single comprehensive Artificial General Intelligence (AGI) system (represented by 'scruffies'). The 'neats' are after a single, elegant solution to the answer of human intelligence. The 'scruffies' just want to build something, write narrow AI codes, make little machines, little advancements, use whatever is available, and hammer away until something happens. [11] The 1997 defeat of world chess champion Garry Kasparov by IBM's Deep Blue computer is considered by many the seminal neat success. But the success of Deep Blue was limited. While the machine demonstrated technical expertise at chess, it didn't show any real comprehension of the game it was playing, or of itself. As a group, 'scruffies', however, take a more experimental approach to AI and put a heavy emphasis on robotics. There is a latest wave of thought sweeping across the AI world that believes the birth of AGI as intimately bound up in the Internet. [12] Growth in the use and importance of these Internet-based AI systems is virtually guaranteed. References 1. The Economist. (1992, March 14). In its creators' image. (changing expectations in artificial intelligence research) (A Survey of Artificial Intelligence). The Economist . 2. Simon, H. A. (1969 ). The Sciences of the Artificial . Cambridge, Mass.: MIT Press . 3. Crevier, D. (1993 ). AI: The Tumultuous History of the Search for Artificial Intelligence. New York: Basic Books. 4. Reid, C. (1970). Hilberg. New York: Springer Verlag. 5. Simon, H. A. (1991). Models of My Life. New York: Basic Books. 6. Yager, R. R. (1987). Fuzzy sets and applications. In L. A. Zadeh (ed), Selected papers . New York: John Wiley. 7. Sadler, P. M. (1998). Psychometric models of student conceptions in science: Reconciling qualitative studies and distractor-driven assessment instruments. Journal of Research in Science Teaching, 35 , 265-296. 8. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. 9. Stedron, B. (March-April 2004). Forecasts for Artificial Intelligence; Artificial Intelligence May Become Not Only Smarter, but Also Wiser and More Independent. We Must Now Look Ahead and Prepare for the Impacts of Machines That May Merge with Us Manage Us, Mimic Us or Leave Us in the D. The Futurist. Volume: 38. Issue: 2 , 24-25. 10. Tucker, P. (March-April 2008). The AI Chasers: The "Holy Grail" of Computer Science May Be within Reach. A Futurist Looks toward Tomorrow's Artificial Intelligence Revolution. The Futurist. Volume: 42. Issue: 2 , 14-18. 11. Hall, J. S. (2007). Beyond AI: Creating the Conscience of the Machine . Prometheus Books. 12. Goertzel, B. (1997). From Complexity to Creativity . Plenum. Read More