Applying Machine Learning to Intelligence Problems
November 2, 2017
Machine Learning Risks
November 13, 2017
We are already surrounded by Machine Learning (ML). 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 and much, much more.
Digital giants such as Google, Facebook, Netflix, and Baidu as well as industrial companies such as Intel 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.
Put into action, Machine Learning has the potential to 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.
The Netflix Recommender System
Netflix is a great example of using Machine Learning in a smart way. It looks at all the content you watch and recommends what the viewer might be interested in based their viewing history and 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.
For the future Netflix is already working on improving its recommendation engine with the goal to not only 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.
This was part six in our new blog series, based on the article “Machine Learning Implications for Intelligence and Insights”, written by Jesper Martell, Comintelli, and Paul Santilli, Hewlett Packard Enterprise.
