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Are machines really intelligent? Learn the answer and how it can affect your business. It's machine-learning 101 for curious business leaders out there.
Autonomous cars taking us on our favorite and most efficient routes, virtual assistants serving up the exact data a doctor needs to diagnose an illness or that an engineer needs to identify a faulty part, customer support bots that are always available to answer your questions and book your appointments accurately and quickly. All of these use-cases are either here or will be soon, and they all rely on Machine Learning to be successful. But how do the machines learn? There’s a lot of market confusion out there, and it’s important to take a step back and understand what we’re talking about and why.
There’s a lot of discussion about the difference between Artificial Intelligence (AI) and Machine Learning (ML), and even the term analytics has become very broad. What’s important from a business perspective is not the technical definitions but the value that it brings you. All of these technologies are powered by data, and the goal for a business is simply to do what it takes to move from data to insight to outcomes as efficiently as possible.
One area where there is a clear connection between data, insight and outcomes is in the field of maintenance. Routine preventative maintenance has long used data to guide implementation, such as changing the oil in your car and rotating your tires at regular intervals. Done correctly, it will be much more efficient than simply driving your car until it breaks down and then fixing what’s broken. While there is some basic data that goes into the maintenance cycles, with the proliferation of IoT sensors it’s become possible to get a far more sophisticated view of the condition of your machinery and equipment.
The large volume of data presents a tremendous opportunity, but it can be hard to take full advantage. After all, you don’t simply need more data to guide your decisions—you’re likely drowning in that already. In fact, one of the biggest questions that you may be facing as a business leader is how to utilize the data you’re already generating on a daily basis.
Perhaps you’re already employing data scientists to build the models you need to make sense of it all, but they can only process a fraction of the data that is flooding in. Hiring more data scientists is not only expensive, but there is such a shortage that hiring enough may be impossible. A better solution, then, is to utilize a Machine Learning platform to remove this burden from the team processing your data, allowing them to focus on making decisions while the platform focuses on analysis at scale.
For example, Cognitive Predictive Maintenance (CPdM) is a powerful way to apply Machine Learning techniques to preventative maintenance. With a CPdM platform, all of the data coming in is reviewed, compared to historic results, analyzed for patterns and presented for viewing—automatically and in real time. The platform doesn’t just base its results on past events at a moment in time, but is constantly learning and improving based on the data being generated from countless sensors, personalizing the maintenance that is needed for every device you’re monitoring. Instead of changing your oil routinely at 3,000 miles, now you only change it when your engine needs you to. Imagine what that means for large industrial use cases where downtime—whether unplanned or routine—can cost hundreds of thousands or millions of dollars.
That’s what Machine Learning can do, and the efficiencies and savings it can generate for businesses is clear. Now that we know how they can be used, it’s time to look at how machines interact with data and demystify how they really “learn.”
As we covered above, more data is readily available than ever before. Beyond the IoT sensors that constantly generate machine data, there’s also behavior data from customers and users and business transaction data in an ERP or CRM calling out to be analyzed. Data is in such abundance that the level of contextual insight that can be derived is phenomenal.
Humans, however, have a hard time processing data at this level. The average human is capable of processing several dimensions at once—for example, the temperature and humidity—but when too many variables are introduced, even the most intelligent among us can’t process it all. We start eliminating variables, or we generalize, which reduces the accuracy of our analysis.
Machines, on the other hand, do not have this limitation. Computers boil everything down to binary decisions, yes or no states, 0s and 1s, and eliminate nothing. Mathematical models can then be designed to simulate real world behaviors, and the computer can run the entirety of the data input against the model to yield a result that is as accurate as possible.
The key, then, to producing accurate results lies in the model. Ordinarily the creation—and critically, the frequent tuning—of a model is a complex task, requiring the painstaking labor of a team of trained data scientists. To keep the model up to date, it must take in the very latest and most relevant data, and the new analysis must be compared with past analysis and results, leading the data scientist to a series of conclusions that allows them to increase the accuracy further. This can be a difficult and time consuming process.
Fortunately, it’s all math, and we can use Machine Learning to automate much of this process. A computer can tweak a mathematical model in a multitude of ways and test its accuracy, constantly adjusting the input or the algorithm. These adjustments make it appear as if the machine is learning, but it is merely improving the accuracy of a model.
At a certain level of complexity, the mathematical algorithms created in this way are beyond the understanding of the average human being (myself included). But it’s all still just a model—there’s no actual “learning.” The magic is in the math.
In our next post, we’ll discuss the advances in technology that have made all this possible, and dive into the practical steps you can take to use this in your own business.
You can find part two of this series here, or jump right in to learn more about building cognitive-first business applications.
Mark Troester is the Vice President of Strategy at Progress. He guides the strategic go-to-market efforts for the Progress cognitive-first strategy. Mark has extensive experience in bringing application development and big data products to market. Previously, he led product marketing efforts at Sonatype, SAS and Progress DataDirect. Before these positions, Mark worked as a developer and developer manager for start-ups and enterprises alike. You can find him on LinkedIn or @mtroester on Twitter.
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