Put it to Work: Machine Learning in the Real World

Machine Learning is being embraced across various industries as a key method for unlocking hidden value and generating growth, innovation, and competitive advantage. Business leaders who have recognized this are educating themselves on machine learning and identifying ways to implement it, while realizing it is also a constant learning and iteration process (no pun intended).

Machine learning is a sub-field of the broader 'artificial intelligence' field. Machine learning is sometimes called, 'narrow AI' because machine learning focuses on a specific problem to solve instead of 'general intelligence.’ At its essence, machine learning is simply used to improve everyday life—whether that means offering up your next binge-worthy television recommendation or diagnosing breast cancer.

Machine learning is a method of data analysis that is based on the idea that computer systems can look at data, discover patterns, and make decisions without the need for much human intervention or being explicitly programmed. The machine learning algorithms detect the patterns in the data and can make predictions on future data based on the past patterns.

One caveat to machine learning is that it solves problems based on probabilities of successful outcomes and does not guarantee one hundred percent accuracy. In other words, the machine is going to be wrong sometimes and this must be acceptable. Despite this, machine learning has grown in importance and will only continue on that path. We now have more data on our hands than ever before, and with machine learning, we are able to produce models that analyze more complex data and deliver more accurate results faster. When businesses tap into this power, they are able to extract hidden insights from data, identify opportunities, and reveal problems before they happen, leading to improved operations, customer satisfaction, and more (more on that later).

The most widely used methods of machine learning are supervised learning, unsupervised learning, and reinforcement learning. In this case, “supervision” simply means that the answers are known during the training phase of machine learning model development. Supervised learning is commonly used in applications where historical data predicts future events. A good example of this is fraud detection. The system can detect and flag a fraudulent transaction, and it can be verified by a user who knows whether or not it was indeed fraud. Ever have your credit card company report possible fraud only to realize that it wasn’t (or vice versa)? This goes back to what was mentioned above—the computer is not always right and can produce false negatives and positives. Other examples of supervised learning include image classification, weather forecasting, life expectancy estimation, and diagnostics.

Unsupervised learning, as you might guess, is used against data with no target or label values, so the system does not have the “right” answers. The goal is to explore the data and glean meaningful insights within. Examples of unsupervised learning include targeted marketing, customer segmentation, structure discovery, and big data visualization.

Finally, reinforcement learning, is a type of machine learning that uses trial and error to discover what actions maximize rewards. It is often used for robotics, gaming, real-time decisions, and navigation. Self-driving cars? Yep, that’s reinforcement learning at work.

Machine learning can help to address many of the business problems we face today. And, certain methods of machine learning may be more appropriately suited to solve your specific needs, from creating operational efficiencies and narrowing down mass amounts of data to helping your workforce better engage with customers and establish new machine-driven sources of revenue. To learn more about whether machine learning is right for your business and what an investment might look like, contact us.