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Interview with Remus Lazar, Executive Director at IBM Watson, Moving Models From Experimentation to Production
For large organizations moving data science projects for experimentation to production have always been a challenge.  It requires careful collaboration with both data scientists, technologies and business end users. Once machine learning models are in production it is another challenge to keep them performing well.  Sophisticated... Read more
Delta to Test Facial-Recognition Baggage System
Ever had trouble recognizing your luggage at the airport? Well, soon your luggage will be able to recognize you—in a way—with Delta Air Lines’ self-service bag drop system that uses facial-recognition technology to identify travelers. Delta this summer will install four self-service bag-drop machines at Minneapolis-St. Paul International... Read more
Insurance Companies Will Shape the Future of Cyber Security
For too long, vendors have capitalized on industry fear of breaches to sell confusing products that may or may not provide value. But fanning the flames of cyber hysteria has started to backfire. Companies now consider potential losses from a cyber breach as a cost of... Read more
Deep Learning Is Going to Teach Us All the Lesson of Our Lives: Jobs Are for Machines
(An alternate version of this article was originally published in the Boston Globe) On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath... Read more
The normalcy of online learning: the more you study, the better you do
Online learning, after all, is just a form of learning: time spent studying is one of the best predictors of success. Both the pattern (and the exceptions) can be seen quite clearly on the Open University Learning Analytics dataset, which collects anonymized data about the personal characteristics and,... Read more
What’s the difference between data science, machine learning, and artificial intelligence?
When I introduce myself as a data scientist, I often get questions like “What’s the difference between that and machine learning?” or “Does that mean you work on artificial intelligence?” I’ve responded enough times that my answer easily qualifies for my “rule of three”: When you’ve... Read more
GDPR and the right to be forgotten
General Data Protection Regulation The European GDPR (General Data Protection Regulation) was adopted in 2016 and becomes enforceable in May of this year. Article 17 mandates a right to erasure, more commonly called the right to be forgotten. A right to be forgotten is tricky. It’s not immediately... Read more
Deep(ly) Unsettling: The ubiquitous, unspoken business model of AI-induced mental illness
“The junk merchant,” wrote William S. Burroughs, “doesn’t sell his product to the consumer, he sells the consumer to his product. He does not improve and simplify his merchandise. He degrades and simplifies the client.” He might as well have been describing the commercial, AI-mediated, social-network-driven... Read more
This is the second post in a two-part series that discusses healthcare predictive and propensity modeling and selecting the optimal analytics partner to support your growth and engagement efforts. The first post in this series shares five best practices in healthcare propensity modeling. In our last post, we... Read more
Which customers are more likely to respond to banks’ marketing campaigns?
A quick demonstration on business consulting with data science Audience The intended audience for this blog post is marketers who have read the earlier post on 5-step data science consulting framework, and are keen to learn more about the actual implementation of such projects. We will be using the caret package in... Read more