Behind the Buzzword: Machine Learning

Behind the Buzzword: Machine Learning

Part four of our five-part Behind the Buzzword series focuses on Machine Learning. We’ve peeked behind the curtain of Big Data and Predictive Analytics; let’s now take a closer look at the field of computer science that was coined over 58 years ago.

In our Artificial Intelligence blog, we learned that AI is an old branch of computer science software that simulates human decision-making by mimicking “learning” and “problem solving.” It does this with computer programming or with “Machine Learning,” by which the concept of machines carrying out tasks on their own without the need of direct programming for every single task. The idea is that people can program a computer that gives it access to data, and then runs various algorithms in order to make decisions based on the options presented. This way, the machine “learns” for itself.

Automated Self-Programming

The term “Machine Learning” was coined in 1959 by Arthur Samuel, an American pioneer in the field of Artificial Intelligence and computer gaming. The idea that a computer could teach itself everything it needed to know was far more efficient than the time it would take for a programmer to code everything line by line. If learning could be automated, if we could automate the self-programming of computers, the outcome could be revolutionary. With the birth and development of the internet in the 80s and 90s, this idea became a reality, and the science of machine learning took off. Machine Learning allows us to be guided to better decisions based on high-value predictions – many taken from insights hidden in places where we wouldn’t even know to look.

Machine Learning in the Real World

From automating mundane tasks to offering intelligent insights, industries in every sector are reaping the benefits of Machine Learning. You may already be using a device that utilizes it, like a wearable fitness tracker such as a Fitbit, or an intelligent home assistant such as Google Home. Here are some other examples of Machine Learning in use today.

  • Hospitality Industry
    Companies like Airbnb are disrupting the accommodation industry previously dominated by hotels using AI and Machine Learning.
  • Social Media Listening
    Knowing what your customers are saying about you on social media is important for customer service and branding. With literally trillions of social media posts currently out there today, it is impossible to manually sort though them all in order to glean insights. Machine Learning uses advanced algorithms that combines the scale and speed of automation with human understanding to categorize and respond just as a human would.
  • Medical Diagnosis
    Machine Learning is now being used by medical researchers to train algorithms to recognize cancerous tissue and other diseases at a level comparable to trained physicians.
  • Language Translation Software
    According to Google translate, “Macarena has a boyfriend who is called that is called Vitorino, that in the flag oath the boy he gave it to him with two friends.” Got that? If you’ve ever tried Googling a translation to your favorite foreign language song, you’ll realize that online translation services need some work. Machine Learning offers more advanced and accurate translation using models based on probability and statistics instead of grammar rules.
  • Financial Industry
    Machine Learning is already being used in consumer services, such as fraud investigation and credit checks, but is also expanding to loan approval and risk assessment applications.

Balance of Machines and Humans

Despite what popular sci-fi movies tell us, Machine Learning will never replace humans completely. Machines cannot foster creativity, emotions or accountability. They cannot manage the people that drive organizational culture, which is at the heart of any business.

After studying the interplay between people and computers around the game of chess, Russian world chess champion Garry Kasparov found that the best results came from a combination of the two: human planning, creativity, and intuition paired with technology that analyzed data to show insights used to make the best decisions.

Interested in what Machine Learning insights can offer your business? Contact us to learn more!

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