It’s Halloween and that means every article you come across will have some spooky twist. That is, until the smell of turkey starts to fill the streets.
But witches and demons are no longer the threat they once were. In recent years, Halloween has summoned eerie thoughts and ideas about “the rise of the machines.”
So, in the spirit of the spookiest day of the year, here are some of the latest articles discussing the present and future of Machine Learning. Some shed a bright light on it, and some…not so much.
Fraud has become a big problem in the cellular networking market, with a yearly cost of approximately $38 billion a year to the industry. Current fraud detection approaches in the industry, against methods like PBX hacking, subscription fraud, dealer fraud, service abuse, and account takeover, rely on static rules with pre-set volume or frequency thresholds.
This article, based on an interview with Ole J. Mengshoel, examines how adaptive AI and machine learning can help address reduce fraud in the cellular services market.
Target is famous in the retail industry for having employed statisticians and data scientists to use purchase behavior to identify shoppers who were pregnant and then market to them. These statisticians used data from Target’s baby registry system, for instance, to identify pregnancy-driven buying patterns and write algorithms that offer discounts or coupons to the relevant target audience, thus helping to turn them into loyal customers.
This article by Armando Roggio examines how machine learning can be used to identify shopping pattern of different audiences by itself, and optimize common ecommerce systems.
Google’s Gary Illyes took journalist Barry Schwartz on a journey to Google’s recent use of machine learning. More specifically, how Google is looking at two or more different existing non-machine-learning signals and see if adding machine learning to the aggregation of them can help improve search rankings and quality.
A lot has been thought and said about machine learning, and, in recent years, quite a few myths have surfaced about what it can or cannot do and cause. In this article, Alexey Malanov goes over 5 common misconceptions associated with the use of machine learning in the field of cybersecurity.
“What, exactly, is the machine learning?” In this article, John Naughton takes a step back, and goes over some of the common (and uncommon) approaches we encounter today. It actually contains one of the best definitions: “Machine learning, you see, is best understood as a giant computer-powered sausage-making machine.
Into the machine is fed a giant helping of data (called a training set) and, after a bit of algorithmic whirring, out comes the sausage – in the shape of a correlation or a pattern that the algorithm has “learned” from the training set.”
Pokemon Go may have sparked the world’s craving for augmented reality, but billionaire Mark Cuban says the industry still has a long way to go. He’s banking on machine learning as the next big breakthrough in technology.
On October 25, 2016, IBM has changed the name of their Predictive Analytics service. The new name is “IBM Watson Machine Learning”.
According to IBM: “Our new name reflects the direction we are taking to provide deeper and more sophisticated self-learning capabilities as well as enhanced model management and deployment functionality within the service. This cognitive ability for computers and models to learn and evolve without changes having to be explicitly programmed is best understood within the analytics space as “Machine Learning” and so we are aligning our service name with this terminology.”
Analyst firm MarketsandMarkets says machine learning will be the biggest component in the explosive expansion of the artificial-intelligence market, projected to reach $5.5 billion by 2020, with 50 percent compound annual growth. This article explores the possibilities that come with the development of ML, examining its effect on the marketing industry.