Use Cases of Machine Learning - (Part 5)
You probably don’t even think of it, but we are already surrounded by Machine Learning. Whenever you use a computer device, chances are high that ML is involved. ML prevents your e-mail from getting overloaded with spam, ML helps you buy books at Amazon and select films at Netflix, ML helps you avoid traffic in rush hour, ML helps you find and translate information at Google, ML helps you check your spelling and grammar in Microsoft Word, ML helps you find friends on Facebook, ML helps you win chess games and much, much more.
Digital giants such as Google, Facebook, Netflix, and Baidu as well as industrial companies such as Intel, Hewlett Packard Enterprise, and GE are leading the way in these innovations, seeing Machine Learning as fundamental to their core business and strategy. The most immediate question for businesses is how Machine Learning algorithms could be applied and where they are likely to have the biggest impact. What kind of intelligence problems is Machine Learning best suited to tackle?
The potential uses of machine learning are remarkably broad. The value potential is everywhere, even extending into sectors that have hitherto been slow to apply data and analytics. As applications of this technology are adopted, they could generate tremendous productivity gains and an improved quality of life. However, they could also unleash a wave of job losses and other disruptions, not to mention thorny ethical and societal questions that will have to be addressed as machines gain greater intellectual capabilities.
Image: Thanks to Machine Learning, a search for Roche, Oncology and FDA provides a cluster of results where the main trends are immediately visible and easier to analyze.
THE NETFLIX RECOMMEND SYSTEM
Netflix is an excellent example of how Machine Learning can be used to sift through large amounts of content in a smart way. One of the reasons Netflix has quickly surpassed the traditional TV networks is that it uses machine learning to recommend what viewers might be interested in, based on their viewing history and other factors. Netflix looks at the content you watch and offers suggestions to viewers based on categories such as actors, genre, filming location etc. Over 75% of what people watch comes from these recommendations and the company estimates its algorithms produce $1 billion a year in value from customer retention.
Personalized content also helps to “find an audience even for relatively small niche videos that would not make sense for broadcast TV models because their audiences would be too small to support significant advertising revenue, or to occupy a broadcast or cable channel time slot.”
Image: All Netflix recommendations are driven by Machine Learning algorithms
For the future Netflix is already working on a new technology that are intended to make its recommendation engine even better. The goal of this technology is not only to recommend movies based on what you've seen in the past, but also to make suggestions based on what you actually like about your favorite shows and movies.
More to come...
This was part FIVE in our series, based on the article “Machine Learning Implications for Intelligence and Insights”, written by Jesper Martell, Comintelli, and Paul Santilli, Hewlett Packard Enterprise.