An Introduction To Web 3.0

Web 3.0 is the new generation of the World Wide Web, through which Web 2.0 technology joins hands with the Semantic Web, making it possible for humans as well as machines to access and use the information stored in the Web. With Web 3.0, machines will be able to perform tasks requiring human intelligence, reducing our time and effort on the Internet dramatically.

Web 3.0, aiming at making the Internet a better, smarter network, is a precursor to the fully semantic Web, and successor to the Web 2.0.

Web 2.0 specialized in making the net usage collaborative by allowing the people to interact with the data and contribute their views through such things as wiki, blogs, social networking sites, etc. Examples: Wikipedia, Blogger, Digg, Technorati, http://Del.icio.us, StumbleUpon, Myspace, Facebook, Flickr, and many more.

But Web 3.0 will give Internet itself intelligence by making the machines-programs that access data (search engine bots, etc.,) -understand what the data itself is. This will make them dig up the best information from the Web for our needs and be able to contribute a lot better than they do now.

Need for Web 3.0

When we search in Google for particular information, most of what we get on the first page are the links to websites without any information useful to us. To obtain the Website that we need, we might have to use different keywords or go to the second or third SERP. Without using our intelligence, we can't get the required result. Programs cannot see what people can.

Google is a dumb machine discharging its bots throughout the Web, scanning for keywords. When it finds a keyword in any site already indexed by it, it will present the link to you. It is up to you to decide if the site is actually useful or not. Hence, most of the time, the first search results of Google are not what you want; they either contain technical jargon allover or advertisements, not the specific thing you want.

With the advent of Web 3.0, this is all going to change. Web 3.0 aims to make the Internet itself a huge database of information, accessible to machines as well as humans. When Web 3.0 becomes popular, we will have a data-driven web, enabling us unearth information faster from the net.

You can help the machine to suit your needs, by searching for, organizing and presenting information from the Web. This means that with Web 3.0, you can be fully automated on the Internet. In addition to this, with intelligent machines, you can achieve tasks like the following very simple: the automation of stock transactions, review and delete unwanted e-mails, creating and updating websites, and booking your tickets, airline tickets, etc.

Web 3.0 is going to be actually the era of artificial intelligence enabled programs sprawling the Web.

Semantic Web Enabling Technologies

Web 3.0 technologies contribute to the Semantic Web by creating a worldwide database of the data that is currently scattered across the Internet. We have millions of data formats for even one simple task. This is because there are too many applications on every genre, and each of them creates its own data format, which is hidden by other applications. The main task of Web 3.0 technologies is to unify all of these formats and a common, extensible format that any application can understand data. Only if the data is not hidden from the machines, the machines can not productive.

The web technologies that will realize Web 3.0 are these.

1. The RDF: resource description framework and RDF are, W3C created by a consortium of HTML, DHTML, SGML is, in general, Web markup languages such as creators of the scheme can be used to describe the resources on . Is, XML syntax is based on the model, primarily, Creative Commons license widget, RDF using the / XML scheme, title, author, Web page changes, etc. For example, such as date, the metadata on the Internet to describe the details of the license is used to describe the information.

2. XML: The Extensible Markup Language is a general-purpose markup scheme that can be used to generate custom markups. XML is such a highly versatile markup scheme that it lets the users define their own elements, enabling seamless compatibility.

3. OWL (Web Ontology Language): OWL is another creation of W3C. It's a knowledge representation scheme, used to script ontologies (the interrelationships between terms in any application document).

Mainly these three technologies, which enable the markup of custom data, are used to author information in machine-accessible form in the Web 3.0. In addition, the derivatives of these technologies and some other extensible markup schemes like XHTML, contribute to it.

Uses of Web 3.0

Web 3.0 contributes extremely to the development of the current Internet. Companies like ZCubes, ZOHO, Google, etc., which specialize in Web 3.0, have built applications to incorporate the semantic revolution of the Web.

Implementation technology, including the Web 3.0 (or Web services, online applications), it can do almost anything. For example, if you go to ZCubes site, you can create a custom Web page can contain text, tables, on-site calculation script, music, pictures, video broadcast, live websites, and so on. You can even hand-written page, and create your own high-quality vector graphics. All these features can be embedded in a single page, drag and drop, the product (a plain HTML file), you can save on your computer or network announced.

Conclusion

Web 3.0 is all about the backend of the Web, about creating extreme machine interfacing. When the Web 3.0 interface becomes more popular, it will entirely change the way we access the Internet. We humans will no longer have to do the difficult tasks of researching on the Internet and finding the exact information. Machines will better do all these tasks. We only will need to view the data, modify it in the way we want, and create whatever new thing we wish to create.

Scope of Artificial Intelligence in Business

Scope of artificial Intelligence in Business

Introduction

Business applications utilize the specific technologies mentioned earlier to try and make better sense of potentially enormous variability (for example, unknown patterns/relationships in sales data, customer buying habits, and so on). However, within the corporate world, AI is widely used for complex problem-solving and decision-support techniques in real-time business applications. The business applicability of AI techniques is spread across functions ranging from finance management to forecasting and production.
In the fiercely competitive and dynamic market scenario, decision-making has become fairly complex and latency is inherent in many processes. In addition, the amount of data to be analyzed has increased substantially. AI technologies help enterprises reduce latency in making business decisions, minimize fraud and enhance revenue opportunities.

Definition of AI

AI is a broad discipline that promises to simulate numerous innate human skills such as automatic programming, case-based reasoning, neural networks, decision-making, expert systems, natural language processing, pattern recognition and speech recognition etc. AI technologies bring more complex data-analysis features to existing applications.
There are many definitions that attempt to explain what Artificial Intelligence (AI) is. I like to think of AI as a science that investigates knowledge and intelligence, possibly the intelligent application of knowledge. Knowledge and Intelligence are as fundamental as the universe within which they exist, it may turn out that they are more fundamental.
One of the aims of AI is said to be the investigation of human cognition and AI is part of Cognitive Science. AI is really an investigation into the creation of intelligence and that there is no reason for the intelligence that is created to be exactly the same as human intelligence.

Importance of AI

Enterprises that utilize AI-enhanced applications are expected to become more diverse, as the needs for the ability to analyze data across multiple variables, fraud detection and customer relationship management emerge as key business drivers to gain competitive advantage.
Artificial Intelligence is a branch of Science which deals with helping machines, finds solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way. A more or less flexible or efficient approach can be taken depending on the requirements established, which influences how artificial the intelligent behavior appears.
AI is generally associated with Computer Science, but it has many important links with other fields such as Maths, Psychology, Cognition, Biology and Philosophy, among many others. Our ability to combine knowledge from all these fields will ultimately benefit our progress in the quest of creating an intelligent artificial being.

Emergence of AI in business

Artificial Intelligence (AI) has been used in business applications since the early eighties. As with all technologies, AI initially generated much interest, but failed to live up to the hype. However, with the advent of web-enabled infrastructure and rapid strides made by the AI development community, the application of AI techniques in real-time business applications has picked up substantially in the recent past.
Mechanical calculations to perform basic computer, using a suitable fix rules. To ensure efficient and artificial machine, which allows you to perform monotonous tasks tend to be the best human disease. For more complex problems, things become more difficult ... Unlike humans, computers, to understand the specific situations of failure to adapt to new situations. We aim to improve the operation of the machine in tackling complex tasks such as artificial intelligence.
Together with this, much of AI research is allowing us to understand our intelligent behavior. Humans have an interesting approach to problem-solving, based on abstract thought, high-level deliberative reasoning and pattern recognition. Artificial Intelligence can help us understand this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities.

Applications of AI

The potential applications of Artificial Intelligence are abundant. They stretch from the military for autonomous control and target identification, to the entertainment industry for computer games and robotic pets, to the big establishments dealing with huge amounts of information such as hospitals, banks and insurances, we can also use AI to predict customer behavior and detect trends.
AI is a broad discipline that promises to simulate numerous innate human skills such as automatic programming, case-based reasoning, decision-making, expert systems, natural language processing, pattern recognition and speech recognition etc. AI technologies bring more complex data-analysis features to existing applications.
Business applications using specific technologies just trying to make better use of potentially enormous change awareness (for example, sales data / model is unknown, customer buying habits, etc.). However, in the corporate world, AI is widely used in complex problem-solving and decision support technology, real-time business applications. Business all over the applicability of artificial intelligence from the financial management functions such as forecasting and product

Artificial Neural Networks

An artificial neural network (ANN), often just called a "neural network" (NN), is a mathematical model or computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. In more practical terms neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.


Real life applications of ANN

The tasks to which artificial neural networks are applied tend to fall within the following broad categories:
? Function approximation, or regression analysis, including time series prediction and modeling.
? Classification, including pattern and sequence recognition, novelty detection and sequential decision making.
? Data processing, including filtering, clustering, blind source separation and compression.
Application areas include system identification and control (vehicle control, process control), game-playing and decision making (backgammon, chess, racing), pattern recognition (radar systems, face identification, object recognition and more), sequence recognition (gesture, speech, handwritten text recognition), medical diagnosis, financial applications (automated trading systems), data mining (or knowledge discovery in databases, "KDD"), visualization and e-mail spam filtering.
The proven success of Artificial Neural Networks (ANN) and expert systems has helped AI gain widespread adoption in enterprise business applications. In some instances, such as fraud detection, the use of AI has already become the most preferred method. In addition, neural networks have become a well-established technique for pattern recognition, particularly of images, data streams and complex data sources and, in turn, have emerged as a modeling backbone for a majority of data-mining tools available in the market. Some of the key business applications of AI/ANN include fraud detection, cross-selling, customer relationship management analytics, demand prediction, failure prediction, and non-linear control.
A majority of the enterprises adopt horizontal or vertical solutions that embed neural networks such as insurance risk assessment or fraud-detection tools, or data-mining tools that include neural networks (for instance, from SAS, IBM and SPSS) as one of the modeling options.
Artificial Intelligence in Manufacturing
As the manufacturing industry becomes increasingly competitive, sophisticated technology has emerged to improve productivity. Artificial Intelligence in manufacturing can be applied to a variety of systems. It can recognize patterns, plus perform time consuming and mentally challenging tasks. Artificial Intelligence can optimize your production schedule and production runs. In order for organizations to meet ever increasing customer demands, and to be able to survive in an environment where change is inevitable, it is crucial that they offer more reliable delivery dates and control their costs by analyzing them on a continual basis. For businesses, being capable of delivering high quality goods at low costs and short delivery times is akin to operating in a whirlpool environment like the Devil's Triangle, and this is no easy task for any organization. Managing so that production takes place at the right time, on the right equipment, and using the right tools will minimize any deviations in delivery dates promised to the customer. Utilizing equipment, personnel and tools to their maximal efficiency will no doubt improve any organization's competitive strength. In return, proper utilization of these capabilities will result in lower costs for the organization
Optimal scheduling of work, the equipment did not use computer software, is a truly daunting task. Implementation of the plan to use the "deterministic simulation approach" will provide a timetable for the work load of the display device for you. Even in the limited facilities of a single piece, as the number of job placements for additional equipment found in the "set of possible solutions" has become almost impossible for the correct solution. In the real world, difficult solution set by large-scale due to the work, equipment and products form a recipe, and formed a technical limitations, as well as in finding the ideal solution to close the complex nature is obvious.
Research and studies are being conducted worldwide on the subject of scheduling. Software vendors working in this area follow developments closely, and they are coming out with new products to better meet demands. "Genetic Algorithms", "Artificial Intelligence", and "Neural Networks" are some of the technologies being used for scheduling

Advantages

? View your best product runs and the corresponding settings.
? Increase efficiency and quality by using optimal settings from past production.
? Artificial Intelligence can optimize your schedule beyond normal human capabilities.
? Increase productivity by eliminating downtime due to unpredictable changes in the schedule.


Artificial Intelligence in Financial services

AI has found a home in the financial services industry and is considered as a valuable addition to numerous business applications covered. Sophisticated technologies including neural networks, and business rules with AI techniques, the positive results in the transaction-oriented scenarios for financial services. AI has been adopted extensively in these areas of risk management, compliance, and securities trading and surveillance, with an expansion in Customer Relationship Management (CRM). Tangible benefits include reduced risk of AI adoption fraud, which increased sales from existing customers with newer ways of avoiding fines resulting from non-compliance and averted securities business exceptions that could result in delayed settlement, if not proved to be .
Warren Buffet is known as end-investors in this age group. So good he is, in fact, that artificial intelligence software developed at Carnegie Mellon that the stock was forecast named after him. But the machines can really take the place of human traders, much less surpass it them? When Deep Blue defeated Kasparov, chess grandmaster in 1997, AI was catapulted into the limelight. In fact, if a machine whiz through the intricacies of the last game of strategy, why do not they propose in other areas as well - and thus to facilitate the work, decreasing costs and errors and improve productivity and quality. This study focuses on the application of AI in the financial sector, particularly in stock trading. In the area of finance, has used artificial intelligence for some time. Some applications of artificial intelligence are
? Credit authorization screening
? Mortgage risk assessment
? Project management and bidding strategy
? Financial and economic forecasting
? Risk rating of exchange-traded, fixed income investments
? Detection of regularities in security price movements
? Prediction of default and bankruptcy
? Security/and or Asset Portfolio Management
Artificial intelligence types used in finance include neural networks, fuzzy logic, genetic algorithms, expert systems and intelligent agents. They are often used in combination with each other. When AI first appeared a decade ago, it generated mass media hype but delivered inconsistent results. A number of those who praised its ability were paralyzed in the end. One such case is Fidelity Investments. In this paper, we set the stage by describing how traditional stock trading differs from AI-powered stock trading. We define the various AI systems available and also explore the various solutions available in the market, their IT foundations and how salient they are. Then, we move into how AI systems for stock trading will affect traders, companies and individuals. Benefits, risks and competitive strategy will be defined and real-world examples cited, as grounding for our recommendations in the end. Recommendations include getting management buy-in, implementing the system and managing the whole structure to succeed.


Artificial Intelligence in Marketing

Advances in artificial intelligence (AI) eventually could turbo-boost customer analytics to give companies speedier insights into individual buying patterns and a host of other consumer habits.
Artificial intelligence functions are made possible by computerized neural networks that simulate the same types of connections that are made in the human brain to generate thought. Currently, the technology is used mostly to analyze data for genetics, pharmaceutical and other scientific research. It's seeing little use in CRM right now, though it has tremendous potential in the future
AI-enhanced analytics programs also provide survival modeling capabilities -- suggesting changes to products based on use. For example, customer patterns are analyzed to learn ways to extend the life of light bulbs or to help decide the correct dosage for medications.
High-tech data mining can give companies a precise view of how particular segments of the customer base react to a product or service and propose changes consistent with those findings. In addition to further exploring customers" buying patterns, analytics could help companies react much more quickly to the marketplace.
According to Meta Group vice president Liz Shahnam, intelligent agents could let companies make real-time changes to marketing campaigns. "New technologies would have the model refreshed on the fly based on each new incoming piece of customer information -- reaction to the campaign -- for a more targeted offer,"

Artificial Intelligence in HR

It is widely believed that the role of managers is becoming a key determinant for enterprises' competitiveness in today's knowledge economy era. Owing to fast development of information technologies (ITs), corporations are employed to enhance the capability of human resource management, which is called human resource information system (HRIS). Recently, due to promising results of artificial neural networks (ANNs) and fuzzy theory in engineering, they have also become candidates for HRIS. The artificial intelligence (AT) field can play a role in this, especially; in assuring that the fuzzy neural network has the characteristics and functions of training, learning, and simulation to make an optimal and accurate judgment according to the human thinking model. The main purposes of the study are to discuss the appointment of managers in enterprises through fuzzy neural network, to construct a new model for evaluation of managerial talent, and accordingly to develop a decision support system in human resource selection. Therefore, the research methods of reviewing literature, in-depth interview, questionnaire survey, and fuzzy neural network are used in the study. The fuzzy neural network is used to train the concrete database, based on 191 questionnaires from experts, for getting the best network model in different training conditions. In order to let decision-makers adjust weighted values and obtain decisive results of each phase's scores, we adopted the simple additive weighting (SAW) and fuzzy analytic hierarchy process (FAHP) methods in the study. Finally, the human resource selection system of Java user interface has been constructed by FNN in the study.


Conclusion

It is difficult for business to see general relevance from AI. This is probably one of the reasons for the compartmentalization of AI into things like Knowledge Based Systems, Neural Networks, and Genetic Algorithms etc. Some of these separate sub topics have been shown to be very useful in solving certain difficult business and industrial problems and consequently funding bodies influence research directions by encouraging work on these more application based areas. This can have a positive effect for business benefit and has lead to some very useful systems that have found their way into the heart of business activity. Business should not lose sight of where AI could go because there are many potential benefits to current and new businesses of future research. The idea of robotic domestic workers is still far fetched but companies are making progress even here. There is already a Robot Vacuum Cleaner marketed by Electrolux and doubtless improved systems with better functionality will follow. .
I would like to close by quoting from Tom Peters, a leading management guru: "When you think you've reached the top, tear down everything and do it all over again. If you don't, your competitor will." To this, I would like to add my own: "If your competitor won't, new investors will enter the market segment who will do the same job better."