It's real and it's here to stay. Whether you are in marketing, operations or finance, machine learning data can help you do what you do better. In this blog post, I outline 7 reasons why Machine Learning is here to stay: Customer Churn, Customer Segmentation, Buyer Behavior, Asset Monitoring, Demand Forecasting, Fraud Detection and Anomaly Detection. We often say "You have 1,000 customers on January 1. You have 1,000 customers on December 31. How many customers did you lose?"

Some great minds, including Elon Musk, are clearly worried about Artificial Intelligence (AI). With the recent innovations like Google's self-driving car, the Hong Kong metro automating its own maintenance and computers that are able to read human emotion, this fear is understandable. Technology is finally catching up to science fiction -- but business leaders who wait until humanoid robots are walking around to begin implementing AI in their business will have missed the boat and let their competitors eat their lunch. With so many new developments in AI, the conscientious business leader should be thinking about how AI will impact their business today. AI Is Already Here Sci-fi has us looking for the wrong signs for the "arrival" of AI (e.g.

To Dr. Ryszard Michalski, the man who coined the term "Machine Learning", did a lot to advance the field and who, sadly, passed away on September 20, 2015. When in 1990 I first visited Dr. Ryszard Michalski, the famous AI researcher and the man who coined the term "Machine Learning", his research division in George Mason University (Virginia) was called "The Center for Artificial Intelligence." Two years later, I visited this place again, and now it was called "The Center for Machine Learning." I was curious and asked Dr. Michalski why he renamed his center in spite of no visible change of direction in his research and that of his colleagues. He smiled and said that in the eyes of many people, the term "AI" had discredited itself by overpromise.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Boston Dynamics co-founder and president Marc Raibert was at TechCrunch Disrupt in San Francisco this week, where he discussed a host of robot-related topics, including bioinspired design, autonomy, robot abuse, and life as part of Google. How weird is it that Dreamer looks almost as cute with NO FACE ON? That's how you know you've got a robot that's adorable all the way through.

Again, remember that this is not a comprehensive list, but that it is notable in that there have been so very many new additions to the base of literature from many disciplines added in just the last few weeks. Just one year ago, we pulled the hype hat over our eyes to some extent–after all, this was most useful in tagging images on social sites and getting machines to paint pictures. The potential for higher purposes was there (the supercomputing world is seeing it too) but just beyond reach. We are, it is safe to say, at the real beginning of mainstream applications for deep learning.

Whether it's guessing which songs you want to listen to or which ads you should be shown, modern AI is increasingly focused on predicting the future. But there's an enormous gulf between those kind of applications and looking at a scene and guessing what will happen next. That's what researchers at the Massachusetts Institute of Technology have done, with a new paper revealing not just their ability to look at still images and guess what will happen next -- something we've covered in the past -- but to actually generate video of it. "What we're interested in is teaching machines what can happen in a particular setting," Carl Vondrick, a Ph.D. student in computer science, told Digital Trends. "For example, we wanted a machine to recognize what happens on a beach. We want it to know that waves are going to crash, people are going to play in the water -- these are all things it's very difficult to teach a machine. The reason is that it would be very time-consuming for a person to sit down and write rules to explain everything that can happen in any given scenario. What we wanted to do was to teach them from watching massive amounts of video instead."

For the longest time, I was a Google Reader guy. I had a list of sites that I wanted to follow, and Reader was the way that I could quickly triage the news that I cared about. I'm almost embarrassed to admit it because RSS feeds are so last decade, but I like being in control of what I read and what I don't read. When Google Reader was unceremoniously shut down, I shuffled back and forth between different RSS readers until I found Feedly. Feedly makes a solid product, but I've never fully bought in.

We believe specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life,

Artificial Intelligence (AI), long the subject of science fiction, is now becoming more and more widespread and is seen as an increasingly important computer science across multiple industries. In Financial Services in particular, Machine Learning and Natural Language Processing is increasingly used today to make sense of big, complex data in a wide range of areas. One such area is regulatory compliance. The use of AI – particularly Natural Language Understanding (NLU), a subset of Natural Language Processing – can help firms to realise a number of benefits, including improving the speed and efficiency with which they achieve compliance, and making that compliance much more robust. As we've seen over just the last couple of years with the introduction of MiFID I & II, UCITS, AIFMD and the like, there is a constant stream of documents being issued by regulators, which can each run to hundreds, or even thousands, of pages.

Artificial intelligence systems will be able to give managers real-time insights about their business operations -- as well as detect early warnings of problems before they occur. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Editor's Note: This article is one of a special series of 14 commissioned essays MIT Sloan Management Review is publishing to celebrate the launch of our new Frontiers initiative. Each essay gives the author's response to this question: "Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?" Workers on the factory floor have suddenly gathered at a point along the production line.