Machine learning has an enormous range of applications, and many of them already show up in our daily lives. When a dating app matches you with a potential partner, when a search engine suggests alternate search terms, or when an online shopping site recommends products based on your past purchases, machine learning is at work. However, many people still don’t understand what machine learning is and how it differs from the more general concept of artificial intelligence (AI).
In this article, we’ll explain the relationship between AI and machine learning and discuss some compelling business benefits that machine learning systems can deliver.
What Is Artificial Intelligence?
Artificial intelligence is a broad concept related to machines performing tasks using methods we consider “smart.” Of course, defining the term “smart” is a contentious issue, and that’s true whether we’re talking about smart humans or smart machines. So it might not surprise you to learn that there has been plenty of confusion and controversy over how to define AI. In general, though, most people agree that AI involves computers emulating behaviors that we normally associate with human beings.
While the progress we’ve made in developing computers over the past 100 years or so is nothing short of astounding, our current computers still struggle with certain tasks even though they excel at others. For example, computers we would consider laughably outdated can still perform mathematical calculations or comb through sets of data at a rate no human could ever hope to achieve. On the other hand, even very powerful computers are relatively bad at things we do without a second thought, like recognizing patterns and learning from mistakes.
Much of the current excitement around AI stems from the fact that it could allow computers to combine some of those human strengths with the raw computational and processing power of computers, granting them the ability to solve problems and answer questions that once seemed impossible.
Machine Learning Is a Way of Achieving AI
Machine learning (ML) isn’t an alternative to AI. Rather, it’s an application of AI that gives systems the ability to learn and improve based on experience. The field of machine learning focuses on developing algorithms that allow computers to examine large sets of data and then use that data to draw new conclusions that aren’t part of the initial programming.
So, machine learning fits into the vision for AI that we outlined above: it lets computers combine a skill they naturally excel at (searching through and processing vast datasets) with one we usually associate with humans (recognizing patterns and drawing new conclusions from them). However, just like humans, machine learning systems develop their skills through training, which means they require initial guidance.
Machine learning algorithms fall into several main branches, which are:
- Supervised learning (predictive) models
The goal of supervised learning models is to predict a future outcome based on existing data. If you want to create a machine learning algorithm that can predict whether and when an event will happen, like a machine on your factory floor breaking down, then a supervised learning model is likely the answer.
- Unsupervised learning (descriptive) models
Unsupervised models can help when you have a question or goal that’s more open-ended than predicting a known outcome. For example, if you wanted to look at the set of customers who purchase a product or service and determine which other products or services they’d be most likely to buy, an unsupervised machine learning model would probably be the right choice.
- Reinforcement learning models
Reinforcement learning models enter the picture when we need to figure out the ideal behavior in a specific context without an example set of data. If we want to create a system that can teach itself how to play chess, for example, then a reinforcement learning model could be the way to go.
If you’re interested in the mathematical concepts behind these types of algorithms, we’ve published a series of articles on our blog that delve into the deeper math of machine learning.
RELATED BLOG ARTICLE: The Detailed Math Behind a Supervised Learning Algorithm
Machine Learning Can Deliver Real Business Value
Machine learning isn’t some pie-in-the-sky pursuit that might yield results in 10 or 20 years. Innovative and highly profitable companies like Google, Amazon, and Microsoft are already using machine learning-based tools to examine data about their customers and automate decisions. Machine learning lets these companies grow quickly and maintain enormous user bases while responding to their customers’ changing needs and shifting behaviors in an extraordinarily agile way.
Here are just a few examples of how AI and machine learning can help drive business value at growing companies:
- Make maintenance proactive
Machine learning and IoT technology can help manufacturing systems monitor the performance of individual devices, detect anomalies, and respond to them by scheduling maintenance proactively. With enough data on device operations, machine learning algorithms can predict when a device or tool will require maintenance based on the earliest fluctuations in the inner workings of the machine. Then, they can address issues before they affect product quality or operational efficiency.
- Monitor quality and reduce waste
With sufficient data, machine learning systems can predict the final quality level of a product with razor precision based on information from the earliest stages of production. At first, the algorithms may only be able to predict quality fluctuations based on known precursors, but with enough training, machine learning systems could identify important quality factors that you haven’t even considered yet.
- Make supply chains smarter and more responsive
Algorithms can use existing data from supply chains to forecast demand and sales, automatically adjust pricing and promotional offers for maximum benefit, predict inventory levels, and inform production planning, among other benefits.
- Improve customer satisfaction
Machine learning systems can mine records of customer actions and transactions to identify customers who are most loyal and those who are most likely to leave. Eventually, the algorithms can discover which actions are likely to improve customer satisfaction, minimize churn, and maximize your profitability.
- Automate and personalize customer service
Some companies receive lots of menial customer service requests, which can bog down their customer service teams and tire out their representatives. Machine learning systems can examine historical customer service data and learn from new interactions to field questions and deliver knowledgeable and relevant answers. If the inquiry moves beyond the scope of what the system can handle, a representative gets an alert and steps in.
Usually, this approach ends up improving the customer service experience for both customers and staff. In fact, research shows that 44% of U.S. consumers would rather deal with a chatbot than a human representative for most customer service inquiries.
These are just a few of the potential applications for machine learning in business. Much of machine learning’s allure comes from the fact that it will generate insights that surprise us and deliver business benefits we never expected — and the forward-thinking companies who implement machine learning algorithms now will reap the most benefits.
Stratus Innovations Group Can Help Your Business Realize the Benefits of Machine Learning
At Stratus Innovations Group, our machine learning experts have years of experience applying complex math and programming concepts to solve real business problems. Whether you’re trying to improve maintenance processes, make supply chains more efficient, or gain deep insights through better analytics and data, the team at Stratus Innovations Group can help.
To find out how we can help your business implement customized cloud-based IT solutions that deliver immediate business value, call us at 844-561-6721 or fill out our quick online contact form. Our sole purpose is helping your business become more profitable and efficient, and we look forward to helping you bring the power of machine learning and cloud computing to your organization!
44% of U.S. consumers want chatbots over humans for customer relations. (2016, December 8). Business Insider.
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