INFORMATION SYSTEMinformation system,an integrated set of components for collecting, storing, and processing data and for delivering information, knowledge, and digital products. Business firms and other organizations rely on information systems to carry out and manage their operations, interact with their customers and suppliers, and compete in the marketplace. For instance, corporations use information systems to reach their potential customers with targeted messages over the Web, to process financial accounts, and to manage their human resources. Governments deploy information systems to provide services cost-effectively to citizens. Digital goods, such as electronic books and software, and online services, such as auctions and social networking, are delivered with information systems. Individuals rely on information systems, generally Internet-based, for conducting much of their personal lives: for socializing, study, shopping, banking, and entertainment.As major new technologies for recording and processing information have been invented over the millennia, new capabilities have appeared. The invention of the printing press by Johannes Gutenberg in the mid-15th century and the invention of a mechanical calculator by Blaise Pascal in the 17th century are but two examples. These inventions led to a profound revolution in the ability to record, process, and disseminate information and knowledge. The first large-scale mechanical information system was Herman Holleriths census tabulator. Invented in time to process the 1890 U.S. census, Holleriths machine represented a major step in automation, as well as an inspiration to develop computerized information systems.One of the first computers used for such information processing was the UNIVAC I, installed at the U.S. Bureau of the Census in 1951 for administrative use and at General Electric in 1954 for commercial use. Beginning in the late 1970s, personal computers brought some of the advantages of information systems to small businesses and to individuals. Early in the same decade the Internet began its expansion as the global network of networks. In 1991 the World Wide Web, invented by Tim Berners-Lee as a means to access the interlinked information stored in the computers connected by the Internet, was installed to become the principal service delivered on the network. The global penetration of the Internet and the Web has enabled access to information and other resources and facilitated the forming of relationships among people and organizations on an unprecedented scale. The progress of electronic commerce over the Internet has resulted in a dramatic growth in digital interpersonal communications (via e-mail and social networks), distribution of products (software, music, e-books, and movies), and business transactions (buying, selling, and advertising on the Web). With the emergence of smartphones, tablets, and other computer-based mobile devices, all of which are connected by wireless communication networks, information systems have been extended to support mobility as the natural human condition.As information systems have enabled more diverse human activities, they have exerted a profound influence over society. These systems have quickened the pace of daily activities, affected the structure and mix of organizations, changed the type of products bought, and influenced the nature of work. Information and knowledge have become vital economic resources. Yet, along with opportunities, the dependence on information systems has brought new threats. Intensive industry innovation and academic research continually develop new opportunities while aiming to contain the threats.Components of information systemsThe main components of information systems are computer hardware and software, telecommunications, databases and data warehouses, human resources, and procedures. The hardware, software, and telecommunications constitute information technology (IT), which is now ingrained in the operations and management of organizations.Computer hardwareToday even the smallest firms, as well as many households throughout the world, own or lease computers. These are usually microcomputers, also called personal computers. Individuals may own multiple computers in the form of smartphones and other portable devices. Large organizations typically employ distributed computer systems, from powerful parallel-processing servers located in data centres to widely dispersed personal computers and mobile devices, integrated into the organizational information systems. Together with the peripheral equipment, such as magnetic or solid-state storage disks, input-output devices, and telecommunications gear, these constitute the hardware of information systems. The cost of hardware has steadily and rapidly decreased, while processing speed and storage capacity have increased vastly. However, hardwares use of electric power and its environmental impact are concerns being addressed by designers.Computer softwareComputer software falls into two broad classes: system software and application software. The principal system software is the operating system. It manages the hardware, data and program files, and other system resources and provides means for the user to control the computer, generally via a graphical user interface (GUI). Application software is programs designed to handle specific tasks for users. Examples include general-purpose application suites with their spreadsheet and word-processing programs, as well as vertical applications that serve a specific industry segmentfor instance, an application that schedules, routes, and tracks package deliveries for an overnight carrier. Larger firms use licensed applications, customizing them to meet their specific needs, and develop other applications in-house or on an outsourced basis. Companies may also use applications delivered as software-as-a-service (SaaS) over the Web. Proprietary software, available from and supported by its vendors, is being challenged by open-source software available on the Web for free use and modification under a license that protects its future availability.TelecommunicationsTelecommunications are used to connect, or network, computer systems and transmit information. Connections are established via wired or wireless media. Wired technologies include coaxial cable and fibre optics. Wireless technologies, predominantly based on the transmission of microwaves and radio waves, support mobile computing. Pervasive information systems have arisen with the computing devices embedded in many different physical objects. For example, sensors such as radio frequency identification devices (RFIDs) can be attached to products moving through the supply chain to enable the tracking of their location and the monitoring of their condition. Wireless sensor networks that are integrated into the Internet can produce massive amounts of data that can be used in seeking higher productivity or in monitoring the environment.Various computer network configurations are possible, depending on the needs of an organization. Local area networks (LANs) join computers at a particular site, such as an office building or an academic campus. Metropolitan area networks (MANs) cover a limited densely populated area. Wide area networks (WANs) connect widely distributed data centres, frequently run by different organizations. The Internet is a network of networks, connecting billions of computers located on every continent. Through networking, users gain access to information resources, such as large databases, and to other individuals, such as coworkers, clients, or people who share their professional or private interests. Internet-type services can be provided within an organization and for its exclusive use by various intranets that are accessible through a browser; for example, an intranet may be deployed as an access portal to a shared corporate document base. To connect with business partners over the Internet in a private and secure manner, extranets are established as so-called virtual private networks (VPNs) by encrypting the messages.Databases and data warehousesMany information systems are primarily delivery vehicles for data stored in databases. A database is a collection of interrelated data (records) organized so that individual records or groups of records can be retrieved to satisfy various criteria. Typical examples of databases include employee records and product catalogs. Databases support the operations and management functions of an enterprise. Data warehouses contain the archival data, collected over time, that can be mined for information in order to develop and market new products, serve the existing customers better, or reach out to potential new customers. Anyone who has ever purchased something with a credit cardin person, by mail order, or over the Webis included within such data collections.Human resources and proceduresQualified people are a vital component of any information system. Technical personnel include development and operations managers, business analysts, systems analysts and designers, database administrators, computer programmers, computer security specialists, and computer operators. In addition, all workers in an organization must be trained to utilize the capabilities of information systems. Billions of people around the world are learning about information systems as they use the Web.Procedures for using, operating, and maintaining an information system are part of its documentation. For example, procedures need to be established to run a payroll program, including when to run it, who is authorized to run it, and who has access to the output.Types of information systemsInformation systems support operations, knowledge work, and management in organizations. (The overall structure of organizational information systems is shown in the figure.) Functional information systems that support a specific organizational function, such as marketing or production, have been supplanted by cross-functional systems. Such systems can be more effective in the development and delivery of the firms products and can be evaluated more closely with respect to the business outcomes.

Operational support and enterprise systemsTransaction processing systems support the operations through which products are designed, marketed, produced, and delivered. In larger organizations, transaction processing is frequently accomplished with large integrated systems known as enterprise systems. In this case the information systems that support various functional unitssales and marketing, production, finance, and human resourcesare integrated into an enterprise resource planning (ERP) system, the principal kind of enterprise system. ERP systems support the value chainthat is, the entire sequence of activities or processes through which a firm adds value to its products. For example, an individual or another business may submit a custom order over the Web that automatically initiates just-in-time production to the customers specifications through an approach known as mass customization. This involves sending orders from the customers to the firms warehouses and perhaps to suppliers to deliver materials just in time for a batched custom production run. Financial accounts are updated accordingly, and billing is initiated.Along with helping to integrate a firms own value chain, transaction processing systems can also serve to integrate an organizations overall supply chain. This includes all firms involved in designing, producing, marketing, and delivering the goods and servicesfrom raw materials to the final delivery of the product. A supply chain management (SCM) system manages the flow of products, data, money, and information throughout the entire supply chain, which starts with the suppliers of raw materials, runs through the intermediate tiers of the processing companies, and ends with the distributors and retailers. For example, purchasing an item at a major retail store generates more than a cash register receipt; it also automatically sends a restocking order to the appropriate supplier, which in turn may call for orders to the suppliers suppliers. With an SCM system, suppliers can also access a retailers inventory database over the Web to schedule efficient and timely deliveries.The third type of enterprise system, customer relationship management (CRM) supports dealing with the companys customers in marketing, sales, service, and new product development. A CRM system gives a business a unified view of each customer and its dealings with that customer, enabling a consistent and proactive customer relationship.Many transaction processing systems support electronic commerce over the Internet. Among these are systems for online shopping, banking, and securities trading. Other systems deliver information, educational services, and entertainment on demand. Yet other systems serve to support the search for products with desired attributes, price discovery (for example, via an auction), and delivery of digital products (for example, software, music, movies, or greeting cards). A growing array of specialized services and information-based products are offered by various organizations on the Web, as an infrastructure for electronic commerce is emerging on a global scale.Transaction processing systems accumulate the data in databases and data warehouses that are necessary for the higher-level information systems. Enterprise systems also provide software modules needed to perform many of these higher-level functions.Support of knowledge workA large proportion of work in an information society involves manipulating abstract information and knowledge (understood in this context as an organized and comprehensive structure of facts, relationships, theories, and insights) rather than directly processing, manufacturing, or delivering tangible materials. Such work is called knowledge work. Three general categories of information systems support such knowledge work: professional support systems, collaboration systems, and knowledge management systems.Professional support systemsProfessional support systems offer the facilities needed to perform tasks specific to a given profession. For example, automotive engineers use computer-aided engineering (CAE) software together with virtual reality systems to design and test new models for fuel efficiency, handling, and passenger protection before producing prototypes, and later they use CAE in the design and analysis of physical tests. Biochemists use specialized three-dimensional modeling software to visualize the molecular structure and probable effect of new drugs before investing in lengthy clinical tests. Investment bankers often employ financial software to calculate the expected rewards and potential risks of various investment strategies. Indeed, specialized support systems are now available for most professions.Collaboration systemsThe main objectives of collaboration systems are to facilitate communication and teamwork among the members of an organization and across organizations. One type of collaboration system, known as a workflow system, is used to route relevant documents automatically to all appropriate individuals for their contributions.Pricing and approval of a commercial insurance policy is a process that can benefit from such a system. Another category of collaboration systems allows different individuals to work simultaneously on a shared project. Known as groupware, such systems accomplish this by allowing controlled shared access, often over an intranet, to the work objects, such as business proposals, new designs, or digital products in progress. The collaborators can be located anywhere in the world: in some multinational companies, work on a project continues 24 hours a day. Other types of collaboration systems include enhanced e-mail and videoconferencing systems, sometimes with telepresence using avatars of the participants. Yet another type of collaboration software, known as wiki, enables multiple participants to add and edit content. (Some online encyclopedias are produced on such platforms.) Collaboration systems can also be established on social network platforms or virtual life systems. The members of the public, as well as potential customers, can be drawn in if desired to enable the cocreation of new products or projection of future outcomes.Knowledge management systemsKnowledge management systems provide a means to assemble and act on the knowledge accumulated throughout an organization. Such knowledge may include the texts and images contained in patents, design methods, best practices, competitor intelligence, and similar sources, with the elaboration and commentary included. Placing the organizations documents and communications in an indexed and cross-referenced form enables rich search capabilities. Organizational knowledge is often tacit, rather than explicit, so these systems must also direct users to members of the organization with special expertise.

Management supportA large category of information systems comprises those designed to support the management of an organization. These systems rely on the data obtained by transaction processing systems, as well as on data and information acquired outside the organization (on the Web, for example) and provided by business partners, suppliers, and customers.Management reporting systemsInformation systems support all levels of management, from those in charge of short-term schedules and budgets for small work groups to those concerned with long-term plans and budgets for the entire organization. Management reporting systems provide routine, detailed, and voluminous information reports specific to each managers areas of responsibility. These systems are typically used by first-level supervisors. Generally, such reports focus on past and present activities, rather than projecting future performance. To prevent information overload, reports may be automatically sent only under exceptional circumstances or at the specific request of a manager.Decision support systems and business intelligenceAll information systems support decision making, however indirectly, but decision support systems are expressly designed for this purpose. As these systems have been developed to analyze massive collections of data, they have also become known as business intelligence applications. The two principal varieties of decision support systems are model-driven and data-driven.In a model-driven decision support system, a preprogrammed model is applied to a relatively limited data set, such as a sales database for the present quarter. During a typical session, an analyst or sales manager will conduct a dialog with this decision support system by specifying a number of what-if scenarios. For example, in order to establish a selling price for a new product, the sales manager may use a marketing decision support system. Such a system contains a model relating various factorsthe price of the product, the cost of goods, and the promotion expense in various mediato the projected sales volume over the first five years on the market. By supplying different product prices to the model, the manager can compare predicted results and select the most profitable selling price.The primary objective of data-driven business intelligence systems is to analyze large pools of data, accumulated over long periods of time in data warehouses, in a process known as data mining. Data mining aims to discover significant patterns, such as sequences (buying a new house, followed by a new dinner table), clusters, and correlations (large families and van sales), with which decisions can be made. Predictive data mining attempts to forecast future outcomes based on the discovered trends. Data-driven decision support systems include a variety of statistical models and may rely on various artificial intelligence techniques, such as expert systems, neural networks, and machine learning. In addition to mining numeric data, text mining is conducted on large aggregates of unstructured data, such as the contents of social media that include social networks, wikis, blogs, and microblogs. As used in electronic commerce, for example, text mining helps in finding buying trends, targeting advertisements, and detecting fraud.An important variety of decision support systems enables a group of decision makers to work together without necessarily being in the same place at the same time. These group decision systems include software tools for brainstorming and reaching consensus.Another category, geographic information systems, can help analyze and display data by using digitized maps. Such data visualization supports rapid decision making. By looking at a geographic distribution of mortgage loans, for example, one can easily establish a pattern of discrimination.Executive information systemsExecutive information systems make a variety of critical information readily available in a highly summarized and convenient form, typically via a graphical digital dashboard. Senior managers characteristically employ many informal sources of information, however, so that formal, computerized information systems are only of partial assistance. Nevertheless, this assistance is important for the chief executive officer, senior and executive vice presidents, and the board of directors to monitor the performance of the company, assess the business environment, and develop strategic directions for the future. In particular, these executives need to compare their organizations performance with that of its competitors and investigate general economic trends in regions or countries. Often individualized and relying on multiple media formats, executive information systems give their users an opportunity to drill down from summary information to increasingly focused details.Acquiring information systems and servicesInformation systems are a major corporate asset, with respect both to the benefits they provide and to their high costs. Therefore, organizations have to plan for the long term when acquiring information systems and services that will support business initiatives. On the basis of long-term corporate plans and the requirements of various individuals from data workers to top management, essential applications are identified and project priorities are set. For example, certain projects may have to be carried out immediately to satisfy a new government reporting regulation or to interact with a new customers information system. Other projects may be given a higher priority because of their strategic role or greater expected benefits.Once the need for a specific information system has been established, the system has to be acquired. This is generally done in the context of the already existing information systems architecture of the firm. The acquisition of information systems can either involve external sourcing or rely on internal development or modification. With todays highly developed IT industry, companies tend to acquire information systems and services from specialized vendors. The principal tasks of information systems specialists involve modifying the applications for their employers needs and integrating the applications to create a coherent systems architecture for the firm. Generally, only smaller applications are developed internally. Certain applications of a more personal nature may be developed where the programming environment supports simple end-user enhancement.

ARTIFICIAL INTELLIGENCE(AI),artificial intelligence(AI),the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasksas, for example, discovering proofs for mathematical theorems or playing chesswith great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.

Expert systemsExpert systems occupy a type of microworldfor example, a model of a ships hold and its cargothat is self-contained and relatively uncomplicated. For such AI systems every effort is made to incorporate all the information about some narrow field that an expert (or group of experts) would know, so that a good expert system can often outperform any single human expert. There are many commercial expert systems, including programs for medical diagnosis, chemical analysis, credit authorization, financial management, corporate planning, financial document routing, oil and mineral prospecting, genetic engineering, automobile design and manufacture, camera lens design, computer installation design, airline scheduling, cargo placement, and automatic help services for home computer owners.

TRANSACTION PROCESSING SYSTEMSTransaction processing systems support the operations through which products are designed, marketed, produced, and delivered. In larger organizations, transaction processing is frequently accomplished with large integrated systems known as enterprise systems. In this case the information systems that support various functional unitssales and marketing, production, finance,... Cross-functional information system?A cross-functional information system is the third era of infromation systems, after calculations systems and functional systems.

Cross-functional systems were designed to intergreate the activities of the entire business process, and are called so because they 'cross' departmental boundaries.

Chaning over to a cross-functional system from a functional one can be problematic at times, as it involves the coordinationg of activities across multiple deparments, with the users changing the way that they work. There is no clear line of authority, and fierce peer competition can often lead to interderparmental rivalries that hinders the development of the new system.

DEFINITION OF CROSS FUNCTIONAL INFORMATION SYSTEMA cross-functional information system is the third era of information systems, aftercalculations systems and functional systems.Cross-functional systems were designed to integrate the activities of the entire businessprocess, and are called so because they 'cross' departmental boundaries.Changing over to a cross-functional system from a functional one can be problematic attimes, as it involves the coordination of activities across multiple departments, with theusers changing the way that they work. There is no clear line of authority, and fiercepeer competition can often lead to interdepartmental rivalries that hinders thedevelopment of the new system.

EXAMPLES OF CROSS FUNCTIONAL SYSTEM1. Enterprise Resource Planning (ERP) is an integrated computer-based system usedto manage internal and external resources, including tangible assets, financialresources, materials, and human resources. Its purpose is to facilitate the flow ofinformation between all business functions inside the boundaries of the organizationand manage the connections to outside stakeholders. Built on a centralized databaseand normally utilizing a common computing platform, ERP systems consolidate allbusiness operations into a uniform and enterprise-wide system environment.An ERP system can either reside on a centralized server or be distributed acrossmodular hardware and software units that provide "services" and communicate on alocal area network. The distributed design allows a business to assemble modules fromdifferent vendors without the need for the placement of multiple copies of complex andexpensive computer systems in areas which will not use their full capacity.

COMPANIES THAT USES CROSS FUNCTIONAL INFORMATION SYSTEMINTRODUCTIONCross-Functional Enterprise Applications Many companies today are using information technology to developintegrated cross-functional enterprise systems that cross theboundaries of traditional business functions in order to reengineer andimprove vital business processes all across the enterprise. Many companies first moved from functional mainframe legacy

Applications of AI Q. What are the applications of AI? A. Here are some. game playingYou can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second. speech recognitionIn the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient. understanding natural languageJust getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains. computer visionThe world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use. expert systemsA ``knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. The usefulness of current expert systems depends on their users having common sense. heuristic classificationOne of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).

Artificial Neural Networks: Introduction and ApplicationComputer scientists have long been inspired by the human brain. In 1943, Warren S. McCulloch, a neuroscientist, and Walter Pitts, a logician, developed the first conceptual model of an artificial neural network. In their paper, "A logical calculus of the ideas imminent in nervous activity, they describe the concept of a neuron, a single cell living in a network of cells that receives inputs, processes those inputs, and generates an output.Their work, and the work of many scientists and researchers that followed, was not meant to accurately describe how the biological brain works. Rather, an artificial neural network (which we will now simply refer to as a neural network) was designed as a computational model based on the brain to solve certain kinds of problems.Its probably pretty obvious to you that there are problems that are incredibly simple for a computer to solve, but difficult for you. Take the square root of 964,324, for example. A quick line of code produces the value 982, a number Processing computed in less than a millisecond. There are, on the other hand, problems that are incredibly simple for you or me to solve, but not so easy for a computer. Show any toddler a picture of a kitten or puppy and theyll be able to tell you very quickly which one is which. Say hello and shake my hand one morning and you should be able to pick me out of a crowd of people the next day. But need a machine to perform one of these tasks? Scientists have already spent entire careers researching and implementing complex solutions.The most common application of neural networks in computing today is to perform one of these easy-for-a-human, difficult-for-a-machine tasks, often referred to as pattern recognition. Applications range from optical character recognition (turning printed or handwritten scans into digital text) to facial recognition. We dont have the time or need to use some of these more elaborate artificial intelligence algorithms here, but if you are interested in researching neural networks, Id recommend the books Artificial Intelligence: A Modern Approach by Stuart J. Russell and Peter Norvig and AI for Game Developers by David M. Bourg and Glenn Seemann.

Figure 10.2A neural network is a connectionist computational system. The computational systems we write are procedural; a program starts at the first line of code, executes it, and goes on to the next, following instructions in a linear fashion. A true neural network does not follow a linear path. Rather, information is processed collectively, in parallel throughout a network of nodes (the nodes, in this case, being neurons).Here we have yet another example of a complex system, much like the ones we examined in Chapters 6, 7, and 8. The individual elements of the network, the neurons, are simple. They read an input, process it, and generate an output. A network of many neurons, however, can exhibit incredibly rich and intelligent behaviors.One of the key elements of a neural network is its ability to learn. A neural network is not just a complex system, but a complex adaptive system, meaning it can change its internal structure based on the information flowing through it. Typically, this is achieved through the adjusting of weights. In the diagram above, each line represents a connection between two neurons and indicates the pathway for the flow of information. Each connection has a weight, a number that controls the signal between the two neurons. If the network generates a good output (which well define later), there is no need to adjust the weights. However, if the network generates a poor outputan error, so to speakthen the system adapts, altering the weights in order to improve subsequent results.There are several strategies for learning, and well examine two of them in this chapter. Supervised Learning Essentially, a strategy that involves a teacher that is smarter than the network itself. For example, lets take the facial recognition example. The teacher shows the network a bunch of faces, and the teacher already knows the name associated with each face. The network makes its guesses, then the teacher provides the network with the answers. The network can then compare its answers to the known correct ones and make adjustments according to its errors. Our first neural network in the next section will follow this model. Unsupervised Learning Required when there isnt an example data set with known answers. Imagine searching for a hidden pattern in a data set. An application of this is clustering, i.e. dividing a set of elements into groups according to some unknown pattern. We wont be looking at any examples of unsupervised learning in this chapter, as this strategy is less relevant for our examples. Reinforcement Learning A strategy built on observation. Think of a little mouse running through a maze. If it turns left, it gets a piece of cheese; if it turns right, it receives a little shock. (Dont worry, this is just a pretend mouse.) Presumably, the mouse will learn over time to turn left. Its neural network makes a decision with an outcome (turn left or right) and observes its environment (yum or ouch). If the observation is negative, the network can adjust its weights in order to make a different decision the next time. Reinforcement learning is common in robotics. At time t, the robot performs a task and observes the results. Did it crash into a wall or fall off a table? Or is it unharmed? Well look at reinforcement learning in the context of our simulated steering vehicles. This ability of a neural network to learn, to make adjustments to its structure over time, is what makes it so useful in the field of artificial intelligence. Here are some standard uses of neural networks in software today. Pattern Recognition Weve mentioned this several times already and its probably the most common application. Examples are facial recognition, optical character recognition, etc. Time Series Prediction Neural networks can be used to make predictions. Will the stock rise or fall tomorrow? Will it rain or be sunny? Signal Processing Cochlear implants and hearing aids need to filter out unnecessary noise and amplify the important sounds. Neural networks can be trained to process an audio signal and filter it appropriately. Control You may have read about recent research advances in self-driving cars. Neural networks are often used to manage steering decisions of physical vehicles (or simulated ones). Soft Sensors A soft sensor refers to the process of analyzing a collection of many measurements. A thermometer can tell you the temperature of the air, but what if you also knew the humidity, barometric pressure, dewpoint, air quality, air density, etc.? Neural networks can be employed to process the input data from many individual sensors and evaluate them as a whole. Anomaly Detection Because neural networks are so good at recognizing patterns, they can also be trained to generate an output when something occurs that doesnt fit the pattern. Think of a neural network monitoring your daily routine over a long period of time. After learning the patterns of your behavior, it could alert you when something is amiss. This is by no means a comprehensive list of applications of neural networks. But hopefully it gives you an overall sense of the features and possibilities. The thing is, neural networks are complicated and difficult. They involve all sorts of fancy mathematics. While this is all fascinating (and incredibly important to scientific research), a lot of the techniques are not very practical in the world of building interactive, animated Processing sketches. Not to mention that in order to cover all this material, we would need another bookor more likely, a series of books.So instead, well begin our last hurrah in the nature of code with the simplest of all neural networks, in an effort to understand how the overall concepts are applied in code. Then well look at some Processing sketches that generate visual results inspired by these concepts.Virtual RealityThis is the definitive guide to virtual reality. It contains a wealth of information about virtual reality which is designed for the newcomer and experienced technologist alike. It discusses all aspects of virtual reality which includes concepts of virtual reality, technologies used, applications and ethical issues.This is a complex and at times, esoteric subject which continues to fascinate a great many people. Yet there is a certain amount of cynicism towards virtual reality or VR for short which in the early days, promised so much but did not always deliver. We have also included a section about augmented reality: this is a similar form of technology in which the lines are blurred between the real world and computer generated imagery, e.g. video. Sound, video or images are overlaid onto a real world environment in order to enhance the user experience. Virtual Reality (VR), sometimes referred to as immersive multimedia, is a computer-simulated environment that can simulate physical presence in places in the real world or imagined worlds. Virtual reality can recreate sensory experiences, which include virtual taste, sight, smell, sound, and touch.Most current virtual reality environments are displayed either on a computer screen or with special stereoscopic displays, and some simulations include additional sensory information and emphasise real sound through speakers or headphones targeted towards VR users. Some advanced, haptic, systems now include tactile information, generally known as force feedback in medical, gaming and military applications. Furthermore, virtual reality covers remote communication environments which provide virtual presence of users with the concepts of telepresence and telexistence or a virtual artifact (VA) either through the use of standard input devices such as a keyboard and mouse, or through multimodal devices such as a wired glove or omnidirectional treadmills. The simulated environment can be similar to the real world in order to create a lifelike experiencefor example, in simulations for pilot or combat trainingor it differs significantly from reality, such as in VR games. In practice, it is currently very difficult to create a high-fidelity virtual reality experience, because of technical limitations on processing power, image resolution, and communication bandwidth. However, VR's proponents hope that virtual reality's enabling technologies become more powerful and cost effective over time.Virtual reality is often used to describe a wide variety of applications commonly associated with immersive, highly visual, 3D environments. The development of CAD software, graphics hardware acceleration, head-mounted displays, datagloves, and miniaturization have helped popularize the notion. In the book The Metaphysics of Virtual Reality by Michael R. Heim, seven different concepts of virtual reality are identified: simulation, interaction, artificiality, immersion, telepresence, full-body immersion, and network communication. People often identify VR with head mounted displays and data suits

A FUZZY CONTROL SYSTEMA fuzzy control system is a control system based on fuzzy logica mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).

Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based. The idea of fuzzy logic was first advanced by Dr. Lotfi Zadeh of the University of California at Berkeley in the 1960s. Dr. Zadeh was working on the problem of computer understanding of natural language. Natural language (like most other activities in life and indeed the universe) is not easily translated into the absolute terms of 0 and 1. (Whether everything is ultimately describable in binary terms is a philosophical question worth pursuing, but in practice much data we might want to feed a computer is in some state in between and so, frequently, are the results of computing.)

Fuzzy logic includes 0 and 1 as extreme cases of truth (or "the state of matters" or "fact") but also includes the various states of truth in between so that, for example, the result of a comparison between two things could be not "tall" or "short" but ".38 of tallness."Fuzzy logic seems closer to the way our brains work. We aggregate data and form a number of partial truths which we aggregate further into higher truths which in turn, when certain thresholds are exceeded, cause certain further results such as motor reaction. A similar kind of process is used in artificial computer neural network and expert systems..It may help to see fuzzy logic as the way reasoning really works and binary or Boolean logic is simply a special case of it