Module : NCG189: Intelligent Systems Name : Tan Yee Mei (Jazz) Student ID : 10034482 KDU Student ID : C15425 Assignment : Activity 1 Describe an example of an artificial intelligent system. Identify as precisely as possible that aspect of the behaviour that makes it ‘intelligent’. Explain how that intelligence is achieved. Include references to the original research where possible. Write a short essay on the topic approximately 1500 words. [20]. STATISTICA is a software created by Statsoft. It is a software that does data mining and analysis on its data (StatSoft Inc, 2012). In this case study, the STATISCA software that will be focusing on would be STATISTICA Solutions for marketing purposes. Companies uses the system for their marketing departments to use the STATISCA Data Miner to help in the customer’s profiles, conduct effective marketing strategies, create more opportunities to do cross-selling and up-selling, forecast results with consumer behaviour and dealing with optimal inventory and packaging (STATISTICA Solution for Marketing, 2011). As artificial intelligence is being described by John McCarthy(2007), he stated that, “Artificial Intelligence is the science and engineering of making the machines intelligent, especially in computer programs”. Therefore this can be defined with STATISTICA as a program that can help the marketing department in their business to do predictive consumer behaviours and strengthening decision making on their marketing strategies. This has been difficult to implement as the companies have to provide previous results in order to input the program with data before it could actually have the data to be analyst. Other than that, the system is capable of predicting future customers purchases based on the given data with its programs running in the backend. Based on STATISTICA themselves, they said that the STATISTICA Data Miner’s Interactive Drill-Down Explorer allows it to review many types of purchases that a customer would normally make. It is done with different characteristics, studying the group, ages and extract info such as the likeness for the consumer for a new product from the previous customers with the study of the current market by the drill-down analysis (STATISTICA Solution for Marketing, 2011). The system is also known to be intelligent because it is programmed in a way that it runs on data mining technique (Statsoft, 2011). The STATISTICA Data Miner’s Regression Modelling and Classification tools which includes an intelligence of constructing decision trees such as the C&RT, neural networks, boosting and bagging for the predictive data mining. It is also intelligent in a way of clustering and mapping down multi-dimensional scaling and correspondence analysis. It also plays around the value which enables it to process rapidly huge data sets of the customer’s transactions. Referring to Data Mining Techniques (2011), it is defined as an analytic process in STATISTICA to explore huge amount of data and especially in business in order to search for patterns or systematic relationships between the data. The part where the system is identified as intelligent would be the predictive data mining which consists of 3 stages. The stages are initial exploration, model building and identification of patterns and deployment. Stage 1(Exploration): According to the Data Mining Techniques (2011), this stage usually starts with the preparation of the data which needs to be selected such as the subsets of records. It is then send to perform some preliminary feature selection operations to bring the large variables into a manageable range. Depending on the analytical problem, this may involve straightforward predictors for a regression mode using a wide variety of graphical and statistical methods in order to identify the complexity of the nature models that can be taken into the next stage. Stage 2(Model Building and identification of patterns): This is the stage that uses different models and choosing the best model which based on their predictive performances such as producing results among samples. It may look like a simple operation but it is in fact that it sometimes involves a lot of elaboration process. There are variety of techniques to achieve the goals which applies different models to the same data sets and comparing it to choose the best performances. These techniques include the predictive data mining such as bagging, boosting, stacking and Meta-Learning. Bagging is a concept of voting for classification, averaging the problems with continuous dependent variables of interest applies to the area of predictive data mining and to combine multiple models or from the same type for different learning data. In practice, different trees can be grown to a full fledge data. A sophisticated algorithm in the machine learning will generate weights for the prediction or voting in boosting procedures. Boosting is a concept that applies to the predictive data mining in order to generate the multiple models or classifiers such as the C&T or CHAID to the learning data. It assign greater weight to the observations that is difficult to classified compare to the easy ones and continue its iteration. Stacking on the other hand, is a concept that applies to the predictive data mining by combining predictions from different models. It is really useful provided it included the projects in many different ways. Meta-Learning applies to the area of predictive data mining which combines the predictions from multiple models. It is also refered to Stacking as mention previously(Stack Generalization). Stage 3(Deployment): This is the final stage which involves the best model as stated in the previous stage to new data and allowing it to generate predictions and estimates the expected outcomes. This eventually leads to the Neural Network used in STATISTICA as well to perform analytics on the data. Neural Networks is the analytic technique used to process the hypothesised and modelled to handle the learning processes in the system and the neurological functions of the brains which is used to observe. In another words, it learns from existing data. Referring back to John McCarthy(2007), artificial intelligence is achieved through learning experience and how the machine applies its knowledge onto a program. This explains why STATISTICA is intelligent because it ability to learn from the models. The first step in order to design the specific network architecture will include the layers and neurons. The structure and size of the network needs to match the nature for example the formal complexity of the phenomenon. This task is not easy as it involves trails and errors in the system. However, the neural network applies the artificial intelligence to aid the task to find the best network architectures. After that, it is subjected to training and in that phase itself, the neurons apply its iterative process to the inputs or variables to adjust its weight of the network. This is because it would want to optimally predict the sample with its training that is performed on the data. After the phase of it being trained, it is now fit to perform predictive analyst to output predicted data. The result of the network developed in the process of learning will represents a pattern in the data. Therefore, this method of approach is the functional equivalent to the model of relations between variables of the traditional building model approach. Some neural networks can sometimes produce really high accurate predictions that they represent. However in other words of the theory would be the ‘black box’ research approach. Neural Network techniques can also be used as a component designed to build explanatory models because it explore data sets in search the variables and groups of variables. It will eventually facilitates the process of model building process. The system is also intelligent because of its advantages which they are capable of approximating any continuous function and thus the researcher does not have any hypotheses about the underlying model. Haykin(1994) defines neural network as a “massively parallel distributed processor which has natural propensity for storing knowledge and making it available for use. It resembles the brain in 2 ways. 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