These days we don’t give it a second thought when Netflix or Amazon recommends a video or product that would probably interest us. Or when Google seems to read our minds as we’re typing in a search phrase. These are examples of machine learning—a subset of artificial intelligence (AI)—in action in our everyday lives.
In our last post in this series, we explored virtual assistant technology powered by AI and machine learning. While AI can be tough to get your arms around, machine learning is complex territory in its own right. In this post, we’re going to break down machine learning into everyday terms and explore how it might play a bigger role in your life out in the field.
The A-B-Cs of Machine Learning
Before machine learning and the internet, computers performed tasks based on programs written in binary code contained in closed systems. Computers knew how to perform specific, repeatable tasks, based on direct input from humans.
As the internet advanced in the 1990s, the code landscape likewise evolved at a rapid pace. Fast-forward to today and the fact is we simply have too much data, and too many unique behavioral processes for any binary system to accommodate. Smartphones, social media, and real-time technologies now feed more data into computers than we could ever process through fixed systems.
Machine learning helps us deal with both changing behavior and data proliferation through algorithms that evolve without human input. To boil it down, we’re teaching computers to make assessments without human input.
Machine learning is teaching computers to learn as we humans do: by interpreting information from the world around us and bucketing it into manageable categories. Machine learning algorithms then apply this information to situations they run up against in real-time. Many machine learning algorithms continually learn as they apply information in real-time to create predictive scenarios for reacting to internet stimulus.
What are Machine Learning Libraries & Applications?
Think of your brain as having two major components or capacities: storing information (i.e., your memory) and thinking (through intelligence). As described above, we can now train computers to think based on the information they gather and store. In the world of machine learning, a computer’s memory is its library of information.
Instead of building up that library slowly by adding new “books” (i.e., information) over time, we can give the computer access to existing machine learning libraries and applications that call upon pre-existing algorithms, or sets of information.
The result is each machine learning algorithm doesn’t have to learn everything from scratch. It can use the problems solved by previous algorithms, which are accessed through machine learning libraries.
It’s just like this scene from the Matrix. Well, kind of:
Machine learning libraries help computers “think” through the situation at hand, leveraging data collected by algorithms that have solved similar problems in the past. Some common machine learning libraries and applications include:
- Route optimization software
- Product & content recommendations
- Predictive maintenance & quality control
Machine Learning Out in the Field
The ability to better process, interpret, and learn from data empowers field service teams to predict instead of just react. Plus, it allows them to automate tasks that don’t need human input. Here are some examples:
- Monitor and repair assets: Machine learning could process data gathered by sensors on equipment in the field to identify issues or even predict potential ones. Better yet, in some cases, machine learning could apply the necessary fix and eliminate the need for a visit by a field service technician. A similar but slightly different scenario is that machine learning could detect maintenance trends, order necessary parts, and schedule visits by field service engineers before a customer places a call.
- Optimize operations: To outright eliminate or minimize repairs and visits by technicians, machine learning could monitor assets and fine-tune settings to ensure optimal operations. As a result, field service engineers would only need to get involved for major issues or emergencies.
- Perform first-level diagnoses: Machine learning algorithms could process service tickets to determine the best course of action. That could include sending the customer a link to a PDF manual or triggering an email with step-by-step instructions on restarting the equipment.
- Help equip technicians: By evaluating like service calls and recognizing the best course of action across the aggregate, machine learning could recommend the most fitting parts and tools for technicians to take to a job.
These are just a few examples of how machine learning could impact—and is already changing—field service. More real-world applications are sure to come, and when they do, your organization can gain a competitive edge by preparing to harness these opportunities.
To stay ahead of the latest field service trends, subscribe to the Field Service Matters blog. And be sure to check out part one and part two of Adventures in Artificial Intelligence if you haven't already.