Nueral Networks V. Expert Systems

A neural network is a system of programs and data structures that approximates the operation of the human brain. A neural network usually involves a large number of processors operating in parallel. Each part has its own small specialty of knowledge and access to data in its local memory. It works the same way as the biological nervous system works. This type of system learns by example. It is configured for a specific application to do things like recognize patterns or classify data. It can be thought of as an expert in the category of information and its much easier answer what if questions. A neural network is trained or fed large amounts of data and rules about data relationships. Neural Networks can extract patterns and detect trends that are too complex for humans to take in. Neural networks are sometimes described in terms of knowledge layers. The more complex networks have deeper layers. In feed forward systems the learned relationships about data can "feed forward" to higher layers of knowledge. Examples of where you might use a neural networking is in sales forecasting, risk management, or target marketing. In the business world neural networks can be used in a variety of ways as well.


An Expert System is a system that attempts to mimic the knowledge and decisions of a human. This type of system needs to be created from the knowledge of a human expert. This is an artificial intelligence based system that converts the knowledge of an expert in a specific subject into a software code. This code can be merged with other such codes based on the knowledge of other experts and used for answering questions or queries submitted through a computer. In today's society expert systems attempt to solve problems. There are three different parts that an expert systems typically consists of. First, a knowledge base which contains the information acquired by interviewing experts, and logic rules that govern how that information is applied. Second, an interference engine that interprets the submitted problem against the rules and logic of information stored in the knowledge base. Third, interface that allows the user to express the problem in a human language such as English. The system must be a specialist, justify its conclusions, be able to learn, and estimate the reliability of its answer.   

The difference between the two is quite simple. Expert systems technology is generally regarded as simpler and more widely used than neural network technology. An expert system uses sets of rules and data to produce a decision or recommendation. Neural networks, on the other hand, attempt to simulate the human brain by collecting and processing data for the purpose of “remembering” or “learning”. The primary difference between an expert system and a neural network is that a neural network can adapt its criteria to better match the data it analyzes, while an expert system produces results without adjusting for changes in the analyzed data. Many resources for both technologies can be found on the Internet, from simple explanations to elaborate demonstrations.

Neural Network

When it comes to information technology, a neural network is a system of programs and data structures that approximates the operation of the human brain. A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory. It works the same way as the biological nervous system works. This type of system learns by example. It is configured for a specific application to do things like recognize patterns of classify data. It can be thought of as an expert in the category of information that it is given, and much easier answer what if questions.

Typically, a neural network is initially trained or fed large amounts of data and rules about data relationships. Neural Networks can extract patterns and detect trends that are too complex for humans to notice. For example, "A grandfather is older than a person's father". A program can then tell the network how to behave in response to an external stimulus's. For example, to input from a computer user who is interacting with the network, or can initiate activity on its own within the limits of its access to the external world. 

In making determinations, neural networks use several principles including gradient-based training, fuzzy logic, genetic algorithms, and Bayesian methods. Neural networks are sometimes described in terms of knowledge layers. With more complex networks having deeper layers. In feed forward systems, learned relationships about data can "feed forward" to higher layers of knowledge. Neural networks can also learn temporal concepts and have been widely used in signal processing and time series analysis.

There are advantages and disadvantages to using neural networks. There is no "perfect" machine learning method. For every problem, for which a certain method is good, there is another problem for which the same method will fail. The methods at which it fails may be solved by other methods quite easily however. Neural networks are quite simple to implement. Although, neural networks cannot be retrained. If you add data later, this is almost impossible to add to an existing network. Also, neural networks often exhibit patterns similar to those exhibited by humans. However this is more of interest in cognitive sciences than for practical examples.

Current applications of neural networks include: oil exploration data analysis, weather prediction, the interpretation of nucleotide sequences in biology labs, and the exploration of models of thinking and consciousness. What makes neural networks more valuable than traditional computing process is their ability to learn. Neural networks offer a different way to analyze data, and to recognize patterns within that data, than traditional computing methods. However, they are not a solution for all computing problems. Traditional computing methods work well for problems that can be well characterized. Balancing checkbooks, keeping ledgers, and keeping tabs of inventory are well defined and do not require the special characteristics of neural networks.

Minskys Lecture

Marvin Minsky is a professor who lectures and teaches about Media Arts and Sciences. He is a professor of Electrical Engineering and Computer Science Description. In the lecture i listened too, Marvin talked about common sense thinking, artificial intelligence, and the future of the human mind. There is a long history of artificial intelligence. Minsky discussed the history, the future of the growth of artificial intelligence as well as the current situation, and where it will find its place in the coming decades. Human beings have a logical way of thinking, and Minsky discussed why it is hard to create machines aka computers, databases, etc, using artificial intelligence to match the human mind.

Minsky asked the question, Does an expert system contain common sense? In his opinion, machines may have a little common sense. Machines store a certain knowledge base which in return allows the common sense. There is certain things that are able to assist the machines to have some answers, but not all. Just because they have a knowledge base, does not make them experts. Machines can be created for specific purposes or tasks but it is the creation that allows this, not just expert knowledge. This is different than huamns because we do have common sense even if we have no expertise. Minsky used a specific example to illustrate this idea. He used a 4 year old child being able to identify things in a room. This is common sense for the kid, and it does not require the kid to operate off of different codes like machines do. If you asked a machine to identify objects in a room, it might be impossible because they need to operate off of codes. They need specific instructions, but a child can learn from the environment and do so quite easily and fast. It takes a lot of time and intelligence to program these machines to be able to perform actions, because they can not think on their own with common sense. In the future we may be able to make a machine which inherits lots of common sense to be able to do something such as sweing up a torn shirt. At the moment, there is nothing like this in the world.

Expert System

An expert system is an artificial intelligence based system that converts the knowledge of an expert in a specific subject into a software code. This code can be merged with other such codes (based on the knowledge of other experts) and used for answering questions or queries submitted through a computer. In other words, it is a system that trys to copy the knowledge and decisions that human makes. In today's society there are many types of expert systems that attempt to solve problems.

There are three different parts that an expert systems typically consists of. First, a knowledge base which contains the information acquired by interviewing experts, and logic rules that govern how that information is applied. Second, an interference engine that interprets the submitted problem against the rules and logic of information stored in the knowledge base. Third, interface that allows the user to express the problem in a human language such as English.

The design of an expert system is built like human beings. The system must be a specialist and able to use heuristics, justify its conclusions, be able to learn, and estimate the reliability of its answer. Being a specialist means it must be able to know facts and know procedural rules. The system has to give an answer and be able to justify its answer with facts. A good expert system will be able to absorb tons of knowledge and the use the knowledge it absorbs. All of this knowledge to make this expert system be able to  function correctly is domain specific. In other words, it takes all of the knowledge of a human expert to create this advanced type of  expert system.

The expert system works off of a rule base or knowledge base. In expert system technology, the knowledge base is expressed with natural language rules IF ... THEN ... For examples 

"IF it is living THEN it is mortal"
"IF his age = known THEN his year of birth = date of today - his age in years"

IF is called the set of conditions and the set THEN is what happens as a result. Each result can be used as a new fact for the system or just as an action the system must take.

Expert systems can also use forward-chaining and backward-chaining to determine results. Forward-chaining is the questioning of an expert who has no idea of the solution and investigates progressively and is data driven. There is a supply of facts and it repeatedly applies rules to create new facts to get to a certain goal. Backward chaining is goal driven. The engine has an idea of the target. It starts from the goal in hopes of finding the solution as soon as possible by creating sub goals.

There are many benefits to having an expert system. Expert systems offer many advantages for users when compared to traditional programs because they operate like a human brain. Human expertise may not always be readily available when needed so an expert system would take over. Also, when traditional computer methods fail a company may want to implement an expert system. The expert system has a quick availability and it is able to program itself. As the rule base is in everyday language, expert systems can be written much faster than a conventional program, by users or experts, bypassing professional developers and avoiding the need to explain the subject. Expert systems have the ability to exploit a considerable amount of knowledge, the expert system uses a rule base unlike conventional programs which means that the volume of knowledge to program is not a major concern. Whether the rule base has 10 rules or 10 000, the engine operation is the same. The reliability of an expert system is the same as that of a database. For example, it is good or higher than that of a classical program. Also,  human experts can resign, retire, or even die which means valuable knowledge can disappear. But if it is recorded in an expert system, it becomes eternal.

There are also some challenges to designing an expert system.  The flaw is knowledge collection and its interpretation into rules. Most developers have no automated method to perform this task. Instead they work manually, increasing the likelihood of errors. Expert knowledge is generally not well understood either. For example, rules may not exist, be contradictory, or be poorly written and unusable. Also, most expert systems use an engine incapable of reasoning. As a result, an expert system will often work poorly, and the project abandoned.

An example of expert system application in business is in the financial field  which uses expert systems for mortgages. Loan departments are interested in expert systems for mortgages because of the growing cost of labor, which makes the handling and acceptance of relatively small loans less profitable. They also see a possibility for standardized efficient handling of mortgage loans by applying expert systems, appreciating that for the acceptance of mortgages there are hard and fast rules which do not always exist with other types of loans. 

The World is Flat

The world is flat idea comes from Thomas Friedman. The world meaning, the world as a level playing field in terms of commerce, where all competitors have an equal opportunity. Back in 1995 is when the flat world theory began and we didn't even realize it. People were able to collaborate and connect with people in the cheaper ways than ever before. Friedman wrote a book on the world is flat and explained all of his ideas. The idea he intended to make was very simplistic. It is the concept that since everyone is now able to be connected to the Internet in some way, we can all be tied together. In 1995, the first Netscape browser was created. This was the first ways in which a browser tied the world together globally. The browser was a took that could be used across the world! Before Netscape was created, everyone still relied on communication through phones and writing letters. But when Netscape came along, it created a whole new revolution that is now referred to as the Internet.

Friedman likes to use the term, "what can be done, will be done" in the video many times. What he means by this is that being connected to all of this IT and knowledge management, anything you want to be done, can be done! You have all the tools at your fingertips, it is how you use those tools to your advantage that will make you successful. If you are trying to manufacture or sell goods at cheaper prices, then all you have to do is jump on the Internet and search for new ways. Before the Internet, countries were not able to connect easily. When the Internet came along, these countries were able to connect instantly. People in different countries were able to manage, share, collect, and use data with each other. Anyone who wanted to take advantage of this, could jump on the Internet and have their way with this knowledge! Friedman suggested that everyone was neighbors with this new revolution, because there were no boundaries when it came to sharing knowledge and everyone had access!

In today's world, we no long rely on TVs or newspapers to get our information. Almost everyone you know is connected to the Internet and this is how they stay up to date with news and information. This is also how the majority of the world shares and acquires knowledge. Technology and advancements in communications have made it to where people on the other side of the world are more accessible and a flat world seems more accessible than a round one. Sustaining a competitive edge is becoming hard and harder in today's society because everyone is connected to the Internet. Any access that one business has to information via Internet, another business has the same opportunity.

Google and other search engines are the prime example of being able to get information instantly. Friedman quotes, "Never before in the history of the planet have so many people, on their own, had the ability to find so much information about so many things and about so many other people." The growth of search engines is tremendous and constantly growing.  Google is now processing roughly one billion searches per day which is up from 150 million just three years ago. Since everyone has access to all of this changing technology and evolving data and knowledge, Friedman states it is important for the United States to keep updating its work skills. Friedman argues if we continue to make the work force more adaptable, then it will become more employable. He also suggests that the government should make it easier to switch jobs by making retirement benefits and health insurance less dependent on one's employer. For example, provide insurance that would partly cover a possible drop in income when changing jobs. Friedman also believes there should be more inspiration for youth to be scientists, engineers, and mathematicians due to a decrease in the percentage of these professionals being American. If we as Americans take advantage of this, we do not have to look at the corporate world for the next big idea or accomplishment. At any time, present or future, any one individual could be leading the innovation of the world. This platform gives more equal opportunities for individuals to compete globally and be successful as a whole nation or individually.

Managing Knowledge Different

There are three different cases i am going to talk about where three different companies all have one thing in common which is the ability to share knowledge. Partners Health care, Nucor Steel, and Buckman labs all have something in common, but the way they share and manage knowledge is completely different. Below i will discuss the ways in which each company shares and manages knowledge. 

Partners health care has a unique way of sharing and managing knowledge. Although, they had one goal in mind when they created their different ways of sharing and managing knowledge and that was to eliminate wrong diagnoses and prescriptions. Partners developed a system that would be tied together that consisted of order prescription entry, drug database, patient database, and lab test database. This is online access to different medical publications. Being in the medical field, doctors need to be able to get information fast. It they are not able to get this information quick, it can be a matter of life and death for their clients. The reason they created this is because the doctors were having a hard time trying to remember lots of information as fast as they needed it. They want to be able to diagnose patients, prescribe medicine, and remember the history of the patient they are dealing with.  The first thing they developed was the Physician order entry.

They created the system so accurately that it will detect any prescriptions that may have a negative reaction to their patients. The doctor can enter the prescription information, and the system will either offer suggestive prescriptions or show that something may interfere with the patients medical history. With this information, the doctor can then use their knowledge to prescribe the right medication. When the doctor detects something is wrong, they can change or override a suggestion that the system provided. The only kick back to this system is that it only recommends information based on what has been previously entered. The idea Partners had was to make the knowledge so readily accessible that there was little room for error. The other thing they developed was an event-detection system for times when the doctor is not treating the patient directly but still needs to know something that has happened. The doctor is alerted when hospitalized patients monitored health changes and they need to be helped. When the doctor is notified, he can visit the patient directly or call in a new treatment.  



Nucor steel is a steel manufacturing company that has a highly developed knowledge management system that steers away from using IT to gain competitive advantage like other organizations. This company has developed a perfect social ecology for gathering and maintaining knowledge. In other words, Nucor Steel has created a very effective social system in which people operate. It has certain expectations of individuals, defines certain type of individuals who will fit nicely into the organization, lets individuals be free in perusing actions without prior approval, and affects how the workers interact with people inside and outside of their organization. There are two important factors that Nucor steel focuses on within its organization and knowledge. The first is creating knowledge. Unless they keep generating new knowledge, they will be behind everyone else because knowledge increases every day. The second factor is sharing and mobilizing the knowledge you have acquired through the whole corporate network. Nucor steels system is based on incentives that they offer and the value of the knowledge that each of their workers have to offer the company. Nucor steel encourages their workers to think outside of the box and create unique processes that will be more effective to the company. When an employee thinks outside of the box and the knowledge they share with the company is considered excellent and boosts productivity, they receive bonuses. If you do not pump all the knowledge you are gaining through the whole organization then they will become at risk to their competitors who are able to replicate ideas fast.  Nucor allowed their company to identify opportunities to share knowledge, encouraged their workers to share their knowledge, and built effective ways to encourage their workers to use the knowledge they receive. In order to share the knowledge, they made performance data visible to the whole company. In order to encourage workers to accept and use the knowledge they received, they gave incentives and rewarded the ones with strong performance and the ones with weak performances were exposed to peer pressure.

Buckman labs is a company that utilizes all technology in the way they share and manage knowledge. The way Buckman works is all the information in the databases is accessible to everyone, at any given time they need it. There are many advantages to their system because it is easy to navigate, multilingual, searchable, and also available offline. Also, it steadily streams out information so there is a constant flow for the employees. Buckman allows everyone to access the knowledge base and allows each individual to enter knowledge into the system which is a plus. The system functions 24/7 and it’s updated automatically. Buckman is always using the features of email, groupware, and video conferencing. The system is big on collaborating and teamwork. With the knowledge always being available to be communicated, it is easy for them to be successful. Another good thing about their system is the language is easy to understand. Some people may not be as technologically advances but they can still interpret the system. Buckman also created the new system CompuServe. This system allowed them to monitor discussion, track individual requests, and answer questions within 24 hours. If they could not answer the questions they would find someone who could.

 
These three companies have created systems which they are able to use to their advantage and sustain a competitive edge over their competitors.  Although, the various ways in which they share, manage,update, and store data are significantly different as you can see. Partners focuses on one person making the decision but their system has made lots of information easily accessible to them. Nucor steel focuses on their employees and puts a lot of trust in them to boost their productivity and knowledge stock. Buckman labs makes all of their data and knowledge available to everyone and allows everyone to access it and add to the bulk. Each of these companies have found their own unique way to benefit from managing knowledge different and have had tremendous success in doing so.