What is Machine Learning? Everything You Need to Know

January 16, 2019 White Papers

Open the pod bay door, Hal.
What is machine learning
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.

While it is obvious that most organizations are anxious to jump on the machine learning bandwagon as a way to keep up with competitors, the majority of them are not really clear on exactly how this technology can improve their business.

Machine Learning Adoption

In an effort to better explain machine learning and how smart technology can affect your bottom line in the coming year, this white paper will cover the following topics:

1. What is machine learning?
2. What’s the difference between machine learning and artificial intelligence?
3. Why is machine learning so popular now?
4. What are some specific examples of machine learning in action?
5. Why should you consider outsourcing the development of machine learning apps?

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.

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.

Machine learning and artificial intelligence are certainly the “trends-du-jour,” with every CEO and CTO scrambling to incorporate these technologies into their current business processes and product offerings. But they are not the same. Let’s take a look at the differences between machine learning and AI.

What’s the difference between machine learning and AI? Read the rest of this white paper to find out.

I Want to Know More

Download White-Paper