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Artificial Intelligence and Analytics February 2015 What is artificial intelligence? Artificial intelligence concepts became popular in the late ‘90s, but the appeal waned due to limited applicability. At the time, it was an idealistic notion and more than a little infeasible to implement. But with the birth of the data-driven economy at the turn of the century, new age computational tools and digital transformations have been embraced by a wide variety of industries. And artificial intelligence concepts have again found prominence and are being widely adopted as an alternative to traditional methodologies. 1 Artificial Intelligence and Analytics | Dell Inc., 2015 Table of contents About the author Shatanjoy Ray is a senior advisor in the Advanced Analytics practice at Dell Digital Business Services. He has nine years of experience in the analytics domain and has extensively worked in the retail and financial services industries, 2 Introduction......................................................................................................................................1 Analytics is artificial intelligence in practice.............................................................................. 3 Functional architecture of artificial intelligence-enabled analytics..................................... 5 Potential applications of machine learning techniques in industries.................................. 6 Dell and artificial intelligence-driven analytics..........................................................................7 Artificial Intelligence and Analytics | Dell Inc., 2015 Analytics is artificial intelligence in practice Artificial intelligence is the science and engineering of making intelligent computer programs or machines. Artificial intelligence and analytics Analytics over the years has moved away from traditional algorithms to machine learning — embodying the true essence of artificial intelligence. Practices and algorithms based on machine learning techniques are increasingly used to aid scientific and critical business decisions. With the advancement of technology and statistical tools, there is an emergence of several advanced mathematical methods for optimization, regression and classification using machine learning-based logic. Machine learning intelligence can be used in a wide variety of fields for tasks such as: Pattern recognition Predictive modeling Text mining and search Genetic programming Heuristics Gaming Speech recognition Medical diagnostics Credit card fraud detection Several industries, such as healthcare and banking and financial services, have already started using machine learning- 3 Artificial Intelligence and Analytics | Dell Inc., 2015 based analytics techniques in their everyday practices. A closer look reveals that many traditional data mining algorithms and statistical functions that employ iterative analyses are being classified as machine learning. However, the potential of machine learning goes beyond simple iterative algorithms. Emerging machine learning algorithms like automated neural networks, random forests and adaptive boosters continuously improve predictions and forecasts by dynamically learning after each iteration. This particular functionality brings this group of analyses closer to artificial intelligence. Some of the most widely used artificial intelligence-enabled analytics functions/ techniques include: Decision trees: This method of learning uses a decision tree as a predictive model, which maps observations about an item to conclusions about the item’s target value. Association rules: This method helps discover interesting relations between variables in large databases. Artificial neural networks: This learning algorithm, often referred to as a neural network, is inspired by the structural and functional aspects of biological neural networks. Computations are structured in terms of an interconnected group of Several industries, such as healthcare and banking and financial services, have already started using machine learning-based analytics techniques in their everyday practices. artificial neurons, processing information using a connectionist approach to computation. Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs, find patterns in data or capture the statistical structure in an unknown joint probability distribution between observed variables. Support vector machines (SVMs): This training algorithm is a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM builds a model that predicts which category a new example falls into. Clustering: Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to pre-designated criteria; and observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions about the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness (similarity 4 Artificial Intelligence and Analytics | Dell Inc., 2015 between members of the same cluster) and separation between different clusters. Clustering is a method of unsupervised learning and a common application of machine learning-based statistical data analysis. Bayesian networks: Also referred to as a belief network or directed acyclic graphical model, a Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that can perform inference and learning in Bayesian networks. Random forests: This popular machine learning technique is used for decision trees and regressions that build a large collection of de-correlated trees and then averages them. On many problems, the performance of random forests is very similar to boosting and can be simpler to train and tune. Functional architecture of artificial intelligence-enabled analytics Presentations Business decisions Prediction layer Prediction/rules Feedback layer Feedback Recommendation layer Recomendation Prediction module These algorithms and rules are implemented and stored in the recommendation layer. Whenever new sets of data that needs to be analyzed passes through the recommendation layer, recommendations are auto-generated for business decision making. Simultaneously, the model performance is assessed through its linkage with the feedback module. It also suggests appropriate updates to the model development module, auto-learning with every run of the recommendation module. This automated, iterative and autolearning functionality of the AIE brings artificial intelligence to life in motion and practice. 5 Artificial Intelligence and Analytics | Dell Inc., 2015 Model build module Training set Feedback module Recomendation module Test set Decision set Relevant business objects Artificial Intelligence engine The artificial intelligence engine (AIE) is the brain behind any analytics program. This is where models are developed using historical and relevant datasets, rules and prediction algorithms are finalized, recommendations are generated and decisions are made on a continuous basis. The AIE has three layers, as shown in the figure above: the prediction layer, feedback layer and recommendation layer. Historical data enters the prediction layer, where the model-building module develops appropriate statistical models, validates the robustness of the models and creates the prediction algorithm or rules. Potential applications of machine learning techniques in industries The emergence of big data and the Internet of Things (IoT) has led to a sudden explosion in the volume, variety, veracity and velocity of data. This not only demands a transformation of databases, but also the methodologies needed to analyze this data. Machine learning-based techniques (such as clustering, associations, predictive and prescriptive modeling and decision trees) are increasingly being adopted to solve these problems in the insurance, healthcare, banking and manufacturing industries. For example, machine learning techniques can be used to make it easier for doctors to sort through medical information in the form of images and unstructured data (like notes on a patient’s history and structured laboratory test results). This technique involves in-depth learning and helps rapidly turn large amounts of data into deep insights, as well as find subtle patterns in the data invisible to the naked eye. Images of importance, needing further examination by the practitioner, can be auto-flagged. This saves a lot of time for the medical practitioner, who otherwise would have to review hundreds of images/data to find the relevant ones. Iterative and dynamic analyses of symptoms and lab results can also help group ailments into appropriate cluster of diseases, leading to faster and more accurate treatment — and transforming diagnostic healthcare. 6 For banks and financial institutions, machine learning can be used to detect fraud, optimize credit limits and improve risk management by performing multiple iterations, dynamically improving model predictions with every data iteration. These optimized predictors can then be used for superior forecasts and predictions. This will help transform the way risk assessments currently performed, improving compliance and governance. In the field of insurance, telematics will play a crucial role in determining how insurance premiums can be calculated in the future. Machine learning can help business managers sift through tons of data being generated by the sensors, analyze associations and correlations between the variables and appropriately group drivers into various risk categories by analyzing their driving performance. This iterative and intelligent way of partitioning and grouping at nearreal time will help enable a scientific, accurate and dynamic premium calculation method. Similarly, property and casualty insurance premium calculations could be transformed using machine learning of sensor logs installed in modern appliances. The modern manufacturing sector is at the threshold of embracing digital transformation in a much bigger way — the potential to store, analyze and draw Artificial Intelligence and Analytics | Dell Inc., 2015 inferences from the multitude of data is immense. Machine learning can act as the brain behind any analytics-based decision making, purely because of the ability to analyze large datasets and dynamically auto-improve predictions and forecasts with every round of iteration. And machine learning-based techniques, such as random forests, can help enable this shift toward digital transformation. In short, the potential for applications of machine learning techniques in analytics projects spans industries and countless opportunities. Dell and artificial intelligence-driven analytics Dell has a good appreciation for machine learning-based techniques — not to mention in-depth experience and expertise. Apart from having trained data scientists in the field of machine learning, Dell recently acquired StatSoft, an analytics product company. Their analytics tool STATISTICA has one of the most advanced and best performing neural networks applications in the market, a module called STATISTICA Automated Neural Networks. It offers numerous unique advantages and has a broad appeal, benefitting beginners and neural network experts. Experts have a wide selection of network types and training algorithms to choose from. And new users can be guided, via the Automated Network Search tool, through the necessary procedures for creating neural networks. STATISTICA automated neural networks is a comprehensive, state-of-the-art, powerful and extremely fast neural network data analysis package. Artificial intelligence-based analytics is at the inflexion point of adoptability. Let the in-house expertise of Dell Services help your organization on this new and exciting journey. About Dell Digital Business Services Dell Digital Business Services enables digital transformation for customers by taking a business-first approach. Digital Business Services uses a robust consulting methodology to create digital strategy roadmaps for organizations, enabling new revenue models, exceptional customer engagement and superior operational excellence. Our services utilize digital technologies, such as analytics, mobile, social media, cloud and IoT, to deliver end-to-end customer solutions. For more information about any of our service offerings, please visit Dell.com/services or contact your Dell representative. Further reading Hastie, Trevor, Robert Tibshirani and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second ed. http://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf. “Introduction to Neural Networks.” Wolfram Mathematica Documentation Center. http://reference.wolfram.com/applications/neuralnetworks/NeuralNetworkTheory/2.1.0.html. Emanet, Nahit, Halil R Öz, Nazan Bayram and Dursun Delen. “A Comparative Analysis of Machine Learning Methods for Classification Type Decision Problems in Healthcare.” Decision Analytics Journal, 2014. http://www.decisionanalyticsjournal.com/content/1/1/6. Franks, Bill. “When Machine Learning Isn’t Learning.” International Institute for Analytics. April 10, 2014. http://www.iianalytics.com/research/when-machine-learning-isnt-learning. “STATISTICA Automated Neural Networks.” STATISTICA. http://www.statsoft.com/Products/STATISTICA/Automated-Neural-Networks. Scan or click this code to learn how Dell Services can help your organization. This white paper is for information purposes only, and may contain typographical errors and technical inaccuracies. The content is provided as is, without express or implied warranties of any kind. Product and service availability varies by country. To learn more, customers and Dell Channel Partners should contact their sales representative for more information. Specifications are correct at date of publication but are subject to availability or change without notice at any time. Dell and its affiliates cannot be responsible for errors or omissions in typography or photography. Dell’s Terms and Conditions of Sales and Service apply and are available on request. Dell and the Dell logo are trademarks of Dell Inc. Other trademarks and trade names may be used in this document to refer to either the entities claiming the marks and names or their products. Dell disclaims proprietary interest in the marks and names of others. © 2015 Dell Inc. All rights reserved. Feb 2015 | D565-Artificial Intelligence and Analytics-whitepaper.indd | Rev. 1.0