Artificial Intelligence Shadena Hobbs INF 103: Computer Literacy Instructor: Lawrence Master January 28, 2013

Is the Intelligence of machines and robots and the branch of computer science that aims to create if Al textbook defines the field as the study and design of intelligent agents. Where an intelligent agent is a system that perceives it’s environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1956, defines it as the science and engineering of making intelligent machines.

Al research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural facts subfields have grown up around particular institutions and the work of individual searchers. Al researchers is also divided by several technical issues. There are subfields which are focused on the solution of specific problems, on one of several possible traits as reasoning, knowledge, planning, learning, communication, perception, and the ability to move and manipulate objects. General intelligence and symbolic and traditional Al. There are enormous number of tools in Al, including versions of search and mathematical optimization, logic, methods based on probability and economics, many other.

The field of Al research was founded at a conference on the campus of Darmouth College in the Summer of 1956. The attendees, including, John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became the leaders of Al research for many decades. They and their students wrote programs that were to most people, simply astonishing. Computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960’s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. Al’s founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that “machines will be capable, within 20 years, of doing any work a man can do” and Marvin Minsky agreed, writing a generation... the problem of creativity “artificial intelligence” will substantially be solved. They had failed to recognize the difficulty of some of the problems they faced. In 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the Us Congress to found more productive projects, both the U.S. and British governments to restore funding for academic research in the field. However, beginning with the collapse of the Lisp Machine market in 1987, Al once again fell into disrepute, and a second, longer lasting Al winter began. In the 1990’s and early 21st century, Al achieved its greatest successes albeit somewhat behind the scenes. Artificial Intelligence is used for logistic, data mining, medical diagnosis and many other areas emphasis on solving specific sub problems, the creation of new ties between Al and other fields working on similar problems, and a new commitment by researchers to solid mathematical methods and rigorous scientific standards.
Emotional and social skills play two roles for an intelligent agent. First, it must be able to predict the actions of others, by understanding their motives and emotional agent. First, it must be able to predict the actions of others by understanding their motives and emotional states. (This involves elements of game theory, decision theory, as well as the ability to model human emotions and the perceptual skills to detect emotions). Also, in an effort to facilitate to human-computer interaction, an intelligent machine might want to be able to display emotions-even if it does not actually experience then itself- in order to appear sensitive to the emotional dynamics of human interaction.
Most research think that their work will eventually be incorporated into a machine with general intelligence ( known as strong Al), combining all the skills above and exceeding human abilities at most or all of them. A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project. Most of the problems above are considered Al-complete: to solve one problem, you must solve them all. For example, even a straight forward specific task like translation requires that the machine follows the author’s argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author’s intention (social intelligence). Machine translation therefore, is believed to be Al complete: it may require strong Al to be done as well as humans can do it.
In the 1990’s, Al researchers developed sophisticated mathematical tools to solve specific sub problems. These tools are truly scientific, in the sense that their results are both measurable and verifiable, and they have been responsible for many of Al’s recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (like mathematics, economics or operation research). Stuart Russel and Peter Novig describes this movement as less than a “revolution” and “the victory of the neats”. There is an ongoing debate about the revelance and validity of statistical approaches in Al, exemplified in part by exchanges between Peter Novig and Noan Chonsky.
An intelligent agent is a system that perceives its environment and take actions which maximize its chance of success. The simplest intelligent agents programs that solve specific problems. More complicated agents included human beings and organizations of human being(such as firms). The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works some agents are symbolic and logical, some are subsymbolic neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields- such as decision theory and economics- that also use concepts of abstracts agents. The intelligent agent paradigm became widely accepted during the 1990’s.
Researchers have designed systems to build intelligent systems out of interacting intelligent agents in a multi-agent system. A system with both symbolic and subsymbolic components is a hybrid intelligent system, and the study of such system is artificial intelligence systems integration. A hierarchial control system provides a bridge between sub-symbolic Al at it lowest, reactive levels and traditional symbolic Al at its highest levels, where related time constraints permit planning and world modeling. Rodney subsumption architecture was an early proposal for such a hierarchical system.
The simplest Al application can be divided into two types classifers (“if shiny ten diamond”) and controllers (“if shiny then pick up”). Controllers do however also classify condition s before inferring actions, and therefore, classification forms a central part of many Al systems. Classifiers are functions that use pattern matching to determine a closet match. They can be tuned according to examples, making them very attractive for use in Al. These examples are known as observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.
A classifier can be trained in various ways: there are many statistical and machine learning approaches. The most widely used classifiers are the neural network, Kernel methods such as the support vector machine, K- nearest neighbor algorithm, Gaussian mixture model, naïve Bayes classifiers, and decision tree. The performance of the classifiers have been compared over a wide range of tasks. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; that is also referred to as the “no tree theorem” Determining a suitable classifier for a given problem is still more an art than science.
The study of artificial neural networks began in the decade before the field Al research was founded, in the work of Walter Pitts and Warren McCullough. Other important early researchers were Frank Rosenbaltt, who invented the perceptron and Paul Werbos who developed the back propagation algorithm.
The main categories of networks are acyclic or feedfoward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback). Among the most popular feed forward networks, the most famous is the Hopfield net, a form of attractor network, which was first described by John Hopfield in 1982. Neural networks can be applied to the problem of intelligent control (forrobotics) or learning, using such techniques as Hebbian learning and competitive learning.
Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex. Control theory, the grandchild of cybernetics, has many important applications, especially in robotics. Al researchers has developed several specialized languages for Al research, including Lisp and Prolog.
Artificial Intelligence can also be evaluated on specific problems such as small problems in chemistry, hand- writing recognition and game playing. Such test have been termed subject matter expert Turing tests. “Smaller problems provide more achievable goals and there are an ever increasing number of positive results. One classification for outcomes of an Al test is: 1. Optimal: It is not possible to perform better. 2. Strong Super- Human: performs better than all humans. 3. Strong- Human: performs better than most humans. 4. Sub-human: performs worse than the most humans. For example, performance at draughts is optimal, performance at chess is super-human and nearing strong super-human and performance at many everyday tasks (such as recognizing a face or crossing a room without bumping into something) is sub human. Artificial Intelligence techniques are pervasive and are too numerous to list. Frequently, when a technique reaches main stream use, it is no longer considered artificial intelligence; this phenomenon is described as the Al effect.
There are a number of competitions and prizes to promote research in artificial intelligence. The main areas promoted are: general machines intelligence, conversational behavior, data-mining, driverless cars, robot soccer and games.
Natural language processing gives machines the ability to read and understand the languages that human speaks. As sufficiently powerful natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from human written sources, such as Internet texts. Some straight forward applications of natural language processing include information retrieval (or text mining) and machine translation.
Actually in the 1950’s the term artificial intelligence had not been coined yet. This would not happen until 1956, when McCarthy first proposed it. Herbert Simon and others demonstrated the first working Al software program, known as Logic Theorist (McCorduck , 2004,p.xxvii).
Scientist have made great strides in the field of robotics and artificial intelligence, but they have yet to come close to developing the human-like robots that science fiction fans are accustomed to seeing. That’s because creating machines that can replicate the way humans move and think is much more difficult than it might seem.
What robots and computers still lack, however, is the ability to reason and make decision on their own. Scientists specializing in artificial intelligence have succeeded in giving computers what appears to be a certain amount of decision-making ability.

Reference
RedOrbit.com (2009, April 3). Robot scientist mark advancement for artificial intelligence Retrieved from http://www,redorbit.com/news/technology/1665464/robot_scientist_mark_advancement_for_artificial_intelligence/ www.ASK.com Bowles, M. D. (2010). Introduction to Computer Literacy. San Diego, CA: BridgePoint Education Inc.