You can now apply for the November Term of Udacity's Self-Driving Car Engineer Nanodegree! (udacity.com) Has anyone here heard back about the October term yet? I think emails were going to be sent out today. I'd also like to know... plus, you win the handle of the year award LOLz

As a new sub-discipline of Data Science, I notice that SYSTEMS Analytics is starting to get some traction! There are a couple of Analytics graduate level programs with *Systems* in its title (Stevens Institute of Technology and University of North Carolina are the only ones I know). Web search brings up NO books on *Systems* Analytics. With the publication of my book with *Systems* in the title, that gap has been filled now! "SYSTEMS Analytics: Adaptive Machine Learning workbook". My last Analytics startup launched in 2013 explicitly used SYSTEMS Analytics in our Retail Recommendation and Uplift SaaS product; my initial bias for the Systems approach was confirmed by the success of our product.

In recent years Artificial Intelligence (AI) has rapidly gone from an obscure academic research field, to an ever more useful and ubiquitous applied discipline. We increasingly rely on AI for more and more of our everyday tasks, and whole lines of work are being thoroughly transformed by its advances. AI's increasing ubiquity is not making it any easier to understand. AI concepts and techniques are still domain of advanced undergraduate or graduate school level courses. There are a few popular AI books out there, but most of them don't get "under the hood" of how AI actually works. "Artificial Intelligence for Humans, Volume 2: Nature-Inspired Algorithms" is the second volume in the series of short introductions to the general field of modern Artificial Intelligence.

In the burgeoning field of computer science known as machine learning, engineers often refer to the artificial intelligences they create as "black box" systems: Once a machine learning engine has been trained from a collection of example data to perform anything from facial recognition to malware detection, it can take in queries--Whose face is that? Is this app safe?--and spit out answers without anyone, not even its creators, fully understanding the mechanics of the decision-making inside that box. But researchers are increasingly proving that even when the inner workings of those machine learning engines are inscrutable, they aren't exactly secret. In fact, they've found that the guts of those black boxes can be reverse-engineered and even fully reproduced--stolen, as one group of researchers puts it--with the very same methods used to create them. In a paper they released earlier this month titled "Stealing Machine Learning Models via Prediction APIs," a team of computer scientists at Cornell Tech, the Swiss institute EPFL in Lausanne, and the University of North Carolina detail how they were able to reverse engineer machine learning-trained AIs based only on sending them queries and analyzing the responses.

Children today have grown up with the Internet being an integral part of their lives. Babies use tablets to swipe through games and interactive programs. Toddlers can navigate apps on a smartphone. By the time children hit middle school, technology is a natural part of their everyday life, both in school and at home. And of course, their grasp on technological concepts comes, in some cases, faster for them than for their parents. How do you explain a concept like contextual search to a 12-year-old girl or boy?

Soon scientists and the public will have the chance to easily test hypotheses about America's ecological challenges with the help of an ensemble of technologies, including artificial intelligence. Researchers at Georgia Institute of Technology will link their technology for systems thinking with IBM Watson and the Encyclopedia of Life at the Smithsonian. Scientists will then be able to use the information to create their own models about the environment and efficiently test them. The project is one of 10 "Big Data Spokes" announced by the National Science Foundation (NSF). The NSF's 10 million initiative was created to improve the ability to solve the nation's most pressing challenges with the use of big data.

Barely a day goes by without a story taking the internet by storm about the use of Artificial Intelligence (A.I.), Machine Learning and Big Data, how these technologies will impact marketing, advertising agencies and nearly every type of company and industry. What's lesser discussed, is why these technologies are gaining so much traction. Digerati sat down with technology observer Cami Rosso to ask why, and why now. What do you think is driving this interest in A.I., Big Data and Machine Leaning? One aspect of this demand is that machine learning has quickly become the new'must-have' capability for forward-thinking software providers, principally because Machine Learning, a subset of A.I., enables computers to learn without hard-coding.

On weekdays, Daisuke Kuramoto, 36, is just another computer engineer who develops education materials for an e-learning content provider. But once a month, he becomes Qramo, organizer of a computer programming workshop for children. "If you say I am'teaching' programming, that's incorrect," said Kuramoto, who heads the Tokyo-based volunteer group Otomo. "At the workshop, I'm just a participant who loves to play around with programming." Kuramoto started the workshop in 2008 and launched Otomo the following year, recruiting professional programmers, computer science students, parents and others with a knack for the activity.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.