Artificial Intelligence

Artificial intelligence has many definitions, some define as "intelligent machines" such as a robot, others define as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. No matter what definition you agree with, AI is a reality. Though it is important to make a distinction between computing power and the ability of computers to think, it all comes down to how fast a machine can perform an operation. Everything a computer does breaks down into math. Your computer's processor interprets any command you execute as a series of math problems. Faster processors can handle more calculations per second than slower ones, and they're also better at handling really tough calculations. Computers cannot think (as of now), they process information based on a given set of instructions. So, it is the computing power that has allowed AI to become what it is today.

Analytical Software, Expert Systems, Artificial Neural Networks, these are just a few names that were nonexistent 20 years ago. Today, Artificial Intelligence is a multibillion dollar industry. The uses and applications are widely varied.

Companies are taking advantage of AI and changing the way traditional tasks/methods in many industries are done. Many of us use the internet without realizing the behind the scenes that goes on every time you visit a website. Advertising is the biggest one, pay attention to those ads that are customized for you, many of them even contain your name, so how do they do it? AI and software. Here are a few industries and application that will be implemented in the near future.

• Sales forecasting
  

Many companies dedicated to retailing depend on sales forecasts for their success as a company. For them producing accurate forecasts is a critical activity, typical forecasting methods can introduce errors that can result in inefficiently allocating resources or planning, for instance, preventing stock outs is very critical for retailers. With the introduction of Artificial Neural Networks in forecasting the accuracy of its results is significant. Part of this is because predictors use advanced neural network technology which does not suffer from the limitations of traditional methods. In the future companies will not have to worry about past data because with this new technology forecasting will be made in real time that is, these systems will be able to see any changes in the patterns as the sales occur. This will help reduce inventory costs, improve planning and therefore save time and money.

• Industrial process control
  

Expert Systems are already in place in certain industries. Expert Systems are widely applied, from sensor validation through production change control, plant-wide optimization, and dynamic scheduling. Combining an ES and ANN technology can be very beneficial. For example in the future an Expert System with learning capabilities could acquire all the knowledge of the best operators and process experts in any industry, so then if a substitute operator is on duty and encounters an abnormal situation that he or she is not familiar with, then this Expert System will be able to help with diagnosis and advice.

• Customer research
  

With the introduction ANNs in customer research, companies have benefited tremendously. The applications are varied and have helped businesses to solve many problems. ANNs can be used to learn complex patterns of information about customers and generalize the learned information. With this information companies in the future will be able to predict customer satisfaction for instance. These systems will be able to identify the drivers of loyal customers; that is to say, what makes a customer become loyal. Using this information a system can be trained to analyze satisfied consumer behavior's and create customized advertizing that will turn these consumers into loyal customers.

• Target marketing
  

The methods used for advertising can decide the future of many companies, because ANNs do not need a lot of information to identify patterns, it can be used to avoid un-target advertising. ANNs are currently being used to create market segmentation. Classification and recognition problems have been identified as the most commonly cited applications of artificial neural networks; other applications go as far as predicting bankruptcy and loan default and also modeling consumer choice and advertising responses. If the current recession in the US was caused in part by the housing market collapse, an ANN system would have been able to predict all those bad mortgages or potential customers that would default their loans, sounds unrealistic, but it might have been possible to prevent it from happening.

• Risk management
  

In risk management, budgeting takes place in the early phases of a project, accurate estimates are very essential to ensure that a project gets sufficient funding; therefore, many project managers are seeking better ways to increase the accuracy of their project cost estimates. With the help of ANNs, they are able to predict how much a project will cost in the early phases of project design and planning. For instance, using ANN models project managers are able to identify at these early stages potential cost variations that might occur by the end of the project. Given these predictions they can put aside sufficient funds so that the project gets completed on time and also prevent over-budgeting and therefore devoting any extra funds to new projects/investments.