The term “machine learning” may conjure up images of old science fiction movies like the 1968 Stanley Kubrick classic 2001: A Space Odyssey. Thanks to decades of technological advancements and education, modern day artificial intelligence is a lot less scary now than it was back in the sixties. Today’s intelligent computers have the power and potential to transform all industries and improve practically every aspect of our lives.
More and more companies are investing in machine learning and artificial intelligence solutions as a way to reduce costs, increase profits, and learn more about their customers in order to make intelligent business decisions. In a recent study conducted by O’Reilly, half of the respondents were in the beginning stages of machine learning adoption while the other half reported moderate to extensive experience.
What is machine learning?
Stanford University defines machine learning as “the science of getting computers to act without being explicitly programmed.” This is done through the use of algorithms that process large amounts of information and then are able to determine the best action to take based on an overall analysis.
Machine learning programs act as intricate decision trees according to if/then logic and are dependent on the information received as well as the stated purpose of the program. While processing certain types of data over and over again, machines begin to recognize, or “learn,” certain patterns, predictions, and outcomes.
Current examples of machine learning in action
A simple example of machine learning in action is email spam filtering. The process is constantly learning which types of emails get reported as spam based on keywords, sender email addresses, and subject lines. A more advanced scenario is how the ride-sharing app Uber operates. Uber’s algorithm studies traffic patterns, rush-hour stats, and rider behavior in order to predict rider demand, determine pricing, and give up-to-the-minute ride status information.
The fastest growing job category from 2012 to 2017 according to LinkedIn was Machine Learning Engineer. Juniper Research reported that spending for AI and machine learning globally will grow to $7.3 billion annually by 2022. This is an increase from $2 billion in 2018.
Just why exactly has machine learning become so important in the last several years?
The most obvious reason is that we are living in a time where we have access to and gather more information than ever before in the history of technology. There is so much “big data” out there that it is impossible for humans to analyze all of it alone. Computers and smart machines are able to process millions of pieces of information in seconds. In order to keep up with a changing business world, companies have to adopt this new technology if they want to stay relevant.
Pinterest is currently using machine learning to analyze user habits, optimize advertising revenue, and improve search results across its platform. The company actually went so far as to acquire Kosei– a machine learning enhanced recommendation engine – back in 2015. The technology provides Pinterest users with a more personalized browsing experience and has led to an increase in user retention, both on the platform and in email communications.
Should you outsource the development of machine learning apps?
Two of the biggest challenges for companies looking to implement machine learning into their apps and business processes is overall costs and a lack of skilled developers within their own IT department. Both of these problems are easily addressed by outsourcing. But there are several other vitally important reasons that make hiring an outside software development firm for machine learning applications a wise decision.
The rush to develop machine learning applications is only going to grow stronger in the coming years. It will be rare to come into contact with an app or solution that doesn’t feature some type of AI. Businesses and consumers alike will become increasingly dependent on machine learning as it continues to provide them with more efficiencies and an improved quality of life.
Machine learning is no longer simply the subject of science fiction. It’s here. It’s now. And it’s having an impact through numerous applications across all industries.
Today’s CEOs and CTOs must have a plan in place to implement machine learning sooner rather than later. Otherwise, they risk being outpaced and replaced by newer, more agile startups that are already using this technology to help deliver a better product or service, and in turn, a better bottom line.
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