All About Neural Networks

1      Neural networks are a non-linear data statistical model. This is some light to how artificial neural networks are created. Just like any other network it is a system of functions that combine to make a network. First, the information is inputed for the network to analyze. Next, similarities are found from the information given. Once the similarities are made a decision is to be made from that information.

This pattern finding tool can be a huge benefit to companies. It can help companies to make decisions that they might not have been sure about before. Companies make decisions everyday and if there is a certain pattern it would be easier for companies to choose their next move.

Neural networks use decision making through previous patterns and inputs and outputs. The police tracking crimes in an area and looking to see where more police presence should be based on crime patterns, is an example of neural networks. This makes it easier for police to crack down on crime in areas. It would be a waste of time for police to put presence where crime is not heavy or non existent.

Differences between Neural Networks and Expert Systems

Both neural networks and expert systems have been a huge help to most businesses. The two have similarities and differences. You can make decisions from them both. Differences are how they work, what they are, and how you get decisions from them.

Neural networks are a non-linear data statistical model. This is some light to how artificial neural networks are created. Just like any other network it is a system of functions that combine to make a network. First, the information is inputted for the network to analyze. Next, similarities are found from the information given. Once the similarities are made a decision is to be made from that information.

An expert system works by the user putting the information into the user interface. This this causes the system to go and try to seek out the appropriate data. The inference engine and the knowledge acquisition tool work together to find the information needed for the decision. Once the information is found it is analyzed and the put into knowledge base. That information is then turned around and put back onto the user interface so that the user can view the results of the systems find.

Neural networks do differ from expert systems in a number of ways. Neural networks use decision making through previous patterns and inputs and outputs. As far as expert systems go they use knowledge as an expert of a field would do to come up with their decision making. Also neural networks are non-linear. An example of an expert system would be IBM domains compared to the police tracking crimes in an area and looking to see where more police presence should be based on crime patterns, which is an example of neural networks.

A Look Into Expert Systems

     Expert systems are systems that mimic human decision making skills. They make decisions such as an expert would do to make jobs and tasks easier. Expert Systems came around in the 1970's. They can also be considered a form of Artificial Intelligence. An expert system works by the user putting the information into the user interface. This this causes the system to go and try to seek out the appropriate data. The inference engine and the knowledge acquisition tool work together to find the information needed for the decision. Once the information is found it is analyzed and the put into knowledge base. That information is then turned around and put back onto the user interface so that the user can view the results of the systems find.

     The technological components of expert systems are knowledge base, inference engine, user interface, and knowledge acquisition tools. The knowledge base components function is to store all the information available for the expert system. The inference engine helps the system to make decision and assume the output. User interface is what makes it easy for the user to use the system and easily maneuver through the system. The knowledge acquisition tool is used to actually go out and get the knowledge that would need to be used for the decision making.

      An expert system can contribute to business world in many ways. The whole idea of an easier decision making tool is a plus. Expert systems are created to make decisions using knowledge. This is designed to be a replica of what the experts do. This could cause information to be shared fast, easier, and even over more Medias. Expert systems can also have a better type of control or filter over the information searched for, which would be an asset to knowledge Management. The major challenge would be that virtually they are not human.

A Flat World

Thomas Friedman is the author of the book The World is Flat. In this book he reveals why he believes that this world is flat. No, he is not talking about literally flat in it's geometric form, more so flat in the terms of economic competition. The competition explained in the book is between companies and countries. Friedman's book goes into globalization in a analytic perspective. A more common term for the purpose of The World is Flat is "a level playing field."

Each competitor of the world has an equal opportunity. Therefore, making it a flat world. I believe that competitors thinking about having a flat world makes them less available to competitive advantage. When companies think they are the same level as other companies then it makes them one of the same. In order to exceed in the industry you must be innovative. You have to set yourself aside from all the other competitors.

One can see that a flat world can viewed as positive or negative. This perspective all depends on where you are in the competition and the type of advantage you choose to have if any.

Differences of Knowledge Management

Knowledge Management can vary from company to companies. You can look at different companies and see this first hand. This can be for a number of reasons. You have different productions, different leadership, and different understandings. Partners Healthcare, Nucor Steel, and Buckman Labs are an example of varying knowledge managements.

Partners Healthcare had different problems than Nucor Steel and Buckman Labs. The problem that occurred was that Partners were getting medical errors. This is because they had to much information and often it would get lost, misplaced, and obsolete. So in order to control their medical errors they created integrated systems of information. These databases included order entry, drug, patient, and lab test; an online access to relevant medical publications.

Nucor Steel goes into a different direction for how they manage their knowledge. They use three different techniques to increase their knowledge stock. Those three techniques are knowledge creation, knowledge acquisition and knowledge retention. Each of these combine to provide superior human capital, high power-ed incentives, empowered employees, abilities, mind-set, behaviors, the lowest turnover rate in industry, successful ability to identify mutual interests and goals, operating policies cultivate loyalty and commitment.

Buckman Labs are one of the companies who have a different view of knowledge management. Employees have access to whatever information they need, easy user ability, and 24/7 access.  This causes more productivity and according to the text an employee who is given information can’t help but to take responsibility.

Some of these companies are in the health field, manufacturing, and etc. This is why they are have different ways to share knowledge. As one can see knowledge management can be handled different but is still created to serve one purpose.