Application of artificial intelligence in business

Application of artificial intelligence in companies

Introduction

Business applications using specific technologies mentioned earlier to try to better understand the potentially enormous variability (for example, unknown patterns / relationships in sales data, the buying habits of customers, and so on). However, in the corporate world, AI is widely used to solve complex problems and techniques of decision making in enterprise applications in real time. The applicability of business intelligence techniques artificial is shared between functions ranging from finance management to forecasting and production.
In the highly competitive market scenario, dynamic decision making has become quite complex and the latency is inherent in many processes. In addition, the amount of data to be analyzed has increased considerably. AI technologies help businesses reduce the latency in making business decisions, reduce fraud and improve revenue opportunities.

Definition of AI

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

Importance of AI

Companies using improved AI applications should become more diverse, the needs of the ability to analyze data through multiple variables, fraud detection and management of customer relations are seen as key business drivers to gain a competitive advantage.
Artificial Intelligence is a branch of science that deals with support machines, finding solutions to complex problems in a manner more like a human. This usually borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way. Approach more or less flexible or efficient may be made based on the requirements established, which influences how artificial the intelligent behavior appears.
AI is generally combined with computing, but it has many important links with other areas such as mathematics, psychology, cognition, Biology and Philosophy, among many others. Our ability to combine knowledge from all these areas will ultimately benefit our progress in the quest to create an intelligence be artificial.

Emergence of AI in business

Artificial Intelligence (AI) has been used in business applications since the early eighties. Like all technologies, AI initially generated much interest, but not up to the hype. However, with the advent of the Web-enabled infrastructure and rapid progress made by the community AI development, the application of artificial intelligence techniques in business applications in real time has accelerated significantly in the recent past.
Computers are fundamentally well suited to perform mechanical calculations, using fixed programmed rules. This allows artificial machines to perform simple monotonous tasks efficiently and reliably, which humans are poorly adapted. For more complex problems, things become more difficult … Unlike humans, computers have trouble understanding specific situations, and adapt to new situations. Artificial Intelligence aims to improve the behavior of machines in the fight against these complex tasks.
Thus a large part of AI research is that allows us to understand our intelligent behavior. Humans have an interesting approach to solving problems, based on abstract thought, high level of reasoning and deliberation pattern recognition. Artificial intelligence can help us understand this process by recreating it, then eventually we can improve it beyond our current capabilities.

Applications of AI

Applications potential of artificial intelligence are abundant. They range from the army for autonomous control and target identification, the entertainment industry for computer games and robotic pets, large establishments handling huge amounts of information such as hospitals, banks and insurance, we can also use AI to predict customer behavior and detect trends.
IA a broad discipline that promises to simulate numerous innate human such as automatic programming, case-based reasoning, decision making, systems experts, the natural language processing, pattern recognition and speech recognition etc. AI technologies bring more complex features of data analysis existing applications.
Business applications using specific technologies mentioned earlier to try to better understand the variability potentially huge (eg, unknown models / relationships sales data, purchasing habits of customers, and so on). However, in the corporate world, AI is widely used to solve complex problems and techniques of decision making in business applications in real time. The applicability of business techniques of artificial intelligence is shared between the functions ranging from financial management of forecasting and product

Artificial Neural Networks

An artificial neural network (ANN), often simply called a "neural network" (NN) is a mathematical model or computational model based on biological neural networks. It consists of a 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 structure based on internal or external information that passes through the network during the learning phase. In more practical terms neural networks are non-linear statistical tools for data modeling. They can be used to model complex relationships between inputs and outputs or to find trends in the data.

real applications of ANN

The tasks to which artificial neural networks are applied tend to fall into the following categories:
• Analysis of function approximation, or regression, including series time forecasting and modeling.
• Classification, including the reasons and sequence recognition, novelty detection and decision- decisions sequentially.
• Data processing, including filtering, clustering, blind source separation and compression.
Areas applications include system identification and control (vehicle control, process control), video game and making making (backgammon, chess, racing), pattern recognition (radar systems, face recognition, object recognition and more), recognition sequences (gesture, speech, recognition of handwritten text), medical diagnosis, financial applications (trading systems automated), data mining (or knowledge discovery in databases, "KDD"), visualization and spam filtering e-mail.
The proven success of artificial neural networks (ANN) and expert systems has resulted in AI in the widespread adoption of business applications. In some cases, such as fraud detection, the use of AI is already become the preferred method. In addition, neural networks have become an established technique for model recognition, especially images, data streams and complex data sources and, in turn, have emerged as a backbone for the majority of modeling tools for data mining available on the market. Some of the key business applications of AI / ANN include fraud detection, cross-selling, the analysis customer relationship management, demand forecasting, bankruptcy prediction, and nonlinear control.
Most companies adopt horizontal or vertical solutions that integrate neural networks such as risk assessment, or insurance fraud detection tools, or data mining tools that include neural networks (eg, SAS, IBM and SPSS) as one of 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 trends, do a lot more time and mental tasks. Artificial Intelligence can optimize your production schedule and production cycles. So that organizations to meet the demands of the growing clientele, and to be able to survive in an environment where change is inevitable, It is crucial that they provide more reliable delivery dates and control their costs in their analysis on a continual basis. For businesses, being able to provide high quality products at low cost and short delivery times to operate in a similar environment as the Devil's Triangle vortex, and this is not an 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 gaps in delivery dates promised to customers. Using the equipment, personnel and tools to their maximum efficiency will undoubtedly improve the competitiveness of any organization. In return, the appropriate use of these capabilities will result in reduced costs the organization
optimal scheduling of jobs on the hardware, without the use of software is a very difficult undertaking. planning the scene using the "deterministic simulation method will give you hours which will show the expenses by job equipment. Even if limited to one piece of equipment, such as the number of jobs in the Annex to this increase in equipment, find the correct solution in the game "Possible Solutions" becomes almost impossible. And in the real world, the difficulties arising from the enormous size of the set solutions because of revenue brought by jobs, equipment and products, and shaped by the technological limitations and the complexity to find close to the ideal solution is obvious.
Research and studies are conducted worldwide on the topic of planning. Publishers software working in this field to follow developments closely, and they come out with new products to better meet demand. "Algorithms genetic, "" Artificial Intelligence "and" neural networks "are some of the technologies used for planning

Benefits

• View your best product and shopping settings.
• Increase efficiency and quality using the optimal parameters of past production.
• Artificial Intelligence can optimize your time beyond the capabilities humanities.
• Increase productivity by eliminating downtime due to unpredictable changes in the calendar.

Artificial Intelligence in Financial Services

Amnesty International has found a home in the financial services and is recognized as a valuable asset for many business applications. Sophisticated technologies including neural networks and business rules and techniques AI-based yield positive results in the scenarios based on financial services transactions. AI has been widely adopted in areas such as management risk, compliance, and securities trading and surveillance, with an extension in the customer relationship management (CRM). The tangible benefits include the adoption AI reducing the risk of fraud, increase revenue from existing clients due to new possibilities, exceptions to avoid fines resulting non-compliance and avoid trade securities which could result in delayed payment, if not detected.
Warren Buffett is known as the ultimate investor in this age. So good, it is, in fact, that artificial intelligence software developed at Carnegie Mellon that predicts stock movements has been named after him. But can machines really take the place of human operators, and even less than? When Deep Blue defeated Kasparov Chess Grandmaster in 1997, AI was propelled into the spotlight. Indeed, if a machine can whistle through the intricacies of the ultimate game of strategy, why not beat man in other fields as well – thus facilitating the work, reduce costs and errors and increase productivity and quality. This study focuses on the application of AI in Finance, particularly in stock trading. In the field of finance, artificial intelligence has long been used. Some applications of artificial intelligence
• screening Credit Authorization
• Mortgage Risk Assessment
• Project management and bidding strategy
• financial forecasts and economic
• Risk rating of exchange on an exchange, fixed income investments
• Detection of regularities movements in the price of security
• Prediction of default and bankruptcy
• Security and / or management of the portfolio of assets
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 the AI appeared a decade ago, it generated mass media hype, but yielded conflicting results. A number of those who praised its capacity has been paralyzed at the end. This is the case of Fidelity Investments. In this article, we set the scene by describing how the stock trading differs from traditional equity trading AI-powered. We define the different systems available AI and explore different solutions available on the market, their IT foundations and how they are Highlights. Then we go into how AI systems for stock trading will affect traders, businesses and individuals. Benefits, risks and competitive strategy will be defined and examples of real world cities, such as the land of our recommendations at the end. Recommendations include obtaining management buy-in, the implementation of the system and the management of the entire structure to succeed.

Artificial intelligence Marketing

Advances in artificial intelligence (AI) could possibly turbo-boost customer analytics to give companies a quick overview buying habits of individuals and many other consumer habits.
Artificial intelligence functions are made possible by neural networks computer to simulate the same types of connections that are made in the human brain to generate thought. Currently, the technology is primarily used to analyze genetic data, pharmaceutical and other research scientists. It is to see little use CRM in the law now, even if it has a huge potential in the future
AI-building programs also discusses modeling capabilities Survival – by proposing changes to products based on use. For example, the habits of Internet users are analyzed to find ways to extend the bulb life or to help decide the correct dosage of medicines.
High-tech data mining can give companies a clear vision of how certain segments of the customer against a product or service and suggest changes consistent with these conclusions. In addition to continuing to explore customers' purchasing behavior, the analysis could help companies respond more quickly to market.
According to Meta Group Vice President Liz Shahnama, intelligent agents could enable companies to make changes in real-time marketing campaigns. "New technologies have model refreshed on the fly for each new incoming piece of client information – response to the campaign – to bring more targeted, "

Artificial Intelligence in HR

It is widely accepted that the role of managers is becoming a factor for businesses "Competitiveness in the current context of the knowledge economy. With the rapid development of information technology (STI), companies are used to strengthen the capacity of human resources management, which is called information system (HRIS). Recently, Because of promising results of artificial neural networks (ANN) and fuzzy theory in engineering, they have also become candidates for HRIS. The artificial intelligence (AT) on the ground may play a role in this area, in particular, to ensure that the fuzzy neural network has the characteristics and functions of training, learning, and simulation to accurately judge and as the optimal model of human thought. The main objectives of the study are discussing the appointment of heads of enterprises through fuzzy neural network to build a new model for evaluating management talent and therefore to develop a decision support system in selecting human resources. Therefore, the research methods to examine literature, in-depth interview, questionnaire survey, and fuzzy neural networks are used in the study. The neural network Blur is used to form the basis of concrete data on the basis of 191 questionnaires of experts to get the best network model in conditions different training. To enable decision makers to adjust weighted values and obtain decisive results of the scores of each phase, we adopted the simple additive weighting (SAW) and fuzzy analytic hierarchy process (FAHP) methods in the study. Finally, the system human resource selection of Java user interface has been constructed by FNN in the study.

Conclusion

It is difficult for companies to see the general interest of AI. This is probably one reason for the partitioning of AI in things as knowledge systems, neural networks, genetic algorithms and etc. Some of these sub-themes have been demonstrated distinct be very useful in solving some difficult cases and industrial problems and therefore funding agencies influence the direction of research by encouraging more work on these areas of application based. This can have a positive effect on business profits and led to some systems very useful to have found their place in the heart of economic activity. Companies should not lose sight of where AI could go because there are many potential benefits for existing and new businesses for future research. The idea of robotic domestic workers is still far-fetched but companies make progress, even here. There is already a robot vacuum cleaner marketed by Electrolux and probably improved systems with better features follow. .
Let me conclude by quoting Tom Peters, management guru leading: "When you think you've reached the top, destroy everything and start over. If you do not, your competitor. "For this I would add my own:" If your competitor will not, new investors will enter the segment market that will do the same job better. "

About the Author

A.Sabari rajan. ,
Faculty of Management studies,
PSNA College of Engineering and Technology,
Dindigul.
India.

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