| | MAY 20188CIOReviewBy Ashish Bansal, Senior Director, Enterprise Merchant Insights Lead, Capital OneMACHINE THINKINGMachine learning and Artificial Intelligence (AI) are the hottest terms today. Companies, big or small, are racing to figure out how to incorporate machine learning and AI in their way or working. While there is a lot of hype around this, it is important to understand what can machine learning do and how to apply it in the enterprise intelligently. Applying machine learning intelligently requires evolving the right mental model for it, and thinking the right way about its applicability. In short, it requires Machine Thinking.What is Machine LearningTom Mitchell in his book Machine Learning provides the following definition:"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."In other words, a program that improves its execution by performing more tasks can be considered in the category of machine learning. Let's consider a program that looks at credit card transactions and detects fraud. This could be built as a set of rules that depend on transaction amount, location of the transaction, time of day etc. Some combination of these will be classified as fraud. These rules were coded into software by humans analyzing previous patterns. This program classifies transactions into fraud or not fraud. However, more transactions it processes does not improve the accuracy of this algorithm. To improve, offline analysis needs to be performed and new rules need to be coded, or existing rules modified before the algorithm becomes better. In the machine learning regime, every transaction correctly or incorrectly identified as fraud results in a small improvement in the algorithm. Consequently, after processing lots and lots of transactions, the algorithm becomes very good at detecting fraud. It also adapts to techniques being used to conduct fraud. This methodology may be appropriate in many classes of problems such as translating between languages, detecting objects in images, diagnosing diseases from x-rays, validating profiles of people, estimating housing prices etc. It is always important to understand, given a problem, if experience at doing the task should result in improvement in performance. Not every problem fits this descriptionHammer, Nail...The corollary of above is that not every problem is a machine learning problem. Many problems can be solved using rule based systems. For example, validating form fields and correctness of data being filled in could be simply coded using rules.Machine thinking requires building a traditional software engineering solution to the problem using rules or other techniques, irrespective of whether a machine learning solution exists. At the very least, this provides a baseline that the machine learning solution should beat. Further, iterate on the machine learning solution by using a simple algorithm (like linear or logistic regression) before going for the more complicated methods like Random Forests or Neural Networks/Deep Learning. Key to remember is that a complex machine learning algorithm should provide incremental benefits to accuracy at accomplishing the task.Understand Your DataA lot of progress in recent times has been attributed to Deep Learning. State of the art today depends on having large amounts of data available for deep learning to work effectively. Not all problems and organizations have such large data sets available for use. Current state of data science is that 60-70 percent of the time is spent on wrangling data, and 30-40 Ashish Bansal
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