Machine learning has come a long way since the 1950s when a computer was taught how to play checkers. Breaking out of the playroom and into the boardroom, machine learning is helping businesses in a number of ways to optimize business and create efficiencies.
The deluge of data has created the foundation machines needed to effectively assess the world and make decisions from what the data tells them. In business, machine learning helps inform many activities from making swift decisions to creating new products.
What is machine learning?
While computers have had a dramatic impact on our world and can already do many things faster than humans, up to this point, we wouldn’t call them intelligent because humans have to program them before they are capable of accomplishing tasks.
Machine learning takes the next step to teach computers to learn about the world around them by consuming data, interpreting and classifying it, and learning from successes and failures. Machine learning is the leading edge of artificial intelligence (AI) and requires that we build algorithms that help computers learn.
Early attempts at machine learning go as far back as the 1950s when Arthur Samuel of IBM taught a computer how to play checkers.
Since gaining knowledge is integral to learning, these early attempts at machine learning were held back because of the limited data at their disposal. That impediment has largely been removed due to the explosion of data thanks to the internet, mobile phones, social media, internet of things, connected devices and more.
Now that machine learning has accelerated as a result of our large data sets, companies in a variety of industries are investing and researching ways to create new products, services and business opportunities using machine learning. No longer just a buzzword or hype, here are a few examples of how machine learning is used in practice.
With many car manufacturers actively developing self-driving vehicles, it seems very likely we will soon share the road with them. Machine learning is used to teach the “brains” of self-driving cars to learn how to react to hazards in the road, respond to signage and other vehicles.
Drive.ai is a start-up that uses deep learning to create AI software for autonomous vehicles, but Tesla, Ford, Mercedes, Alphabet, and others are testing their own solutions. Powered by a tremendous amount of data, autonomous vehicles are learning how to drive and will hopefully learn to be better at driving than humans since 94 percent of car accidents are due to human error.
When machines fail on the production floor or break down in the field, it’s costly for manufacturers.
Built-in sensors on equipment measure performance and other critical components that are then sent back to computers and machine learning algorithms to analyze and predict possible performance issues or failure.
This monitoring and identification of potential issues allows action to be taken before a breakdown occurs. Caterpillar, Volvo, and John Deere are just a few of the manufacturers who use machine learning for predictive maintenance.
Enhance the customer experience
Companies know the better customer experience they provide those that use their services, the more likely they will become repeat customers or advocates.
Therefore, companies such as Uber use machine learning to help provide a better customer experience from search and recommendation engines to optimization of routes and its marketplace. Netflix enhances its ability to know what people want to watch thanks to the insights machine learning provides.
When people go into a Burberry store, they receive a very personalized experience courtesy of the machine learning insights from the data collected from its loyalty and rewards program.
From the chatbots that answer customer questions on Skype to Experian’s quick and effective decision-making on issues of credit, data powers many machine learning business insights.
Optimize operations and business efficiency
Companies such as Heineken use data and machine learning at every stage of the supply chain.
They aim to eliminate inefficiencies by using data analytics to optimize forecasting and replenishment processes. GE Power is committed to building an “internet of energy” with the help of machine learning so power can be optimized as can the business of energy.
Creativity isn’t just for humans
It’s easy to understand why you might imagine machines are only good at learning left-brained activities characterized by facts and logic, but it turns out machines are learning to exercise their creative sides as well.
IBM’s Chef Watson is learning how to combine ingredients and create recipes while music-generating algorithms are now composing songs.
Since nearly every industry and most companies are asking themselves how they can utilize machine learning to their business advantage, this list is just a snapshot of the many practical uses of machine learning.
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