The Digital Analytics Power HourAttend any conference for any topic and you will hear people saying after that the best and most informative discussions happened in the bar after the show. Ready any business magazine and you will find an article saying something along the lines of "Business Analytics is the hottest job category out there, and there is a significant lack of people, process and best practice." In this case the conference was eMetrics, the bar was….multiple, and the attendees were Michael Helbling, Tim Wilson and Jim Cain (Co-Host Emeritus). After a few pints and a few hours of discussion about the cutting edge of digital analytics, they realized they might have something to contribute back to the community. This podcast is one of those contributions.

Under, behind and inside many of the apps we use every day, a revolution is underway. It's a revolution that started decades ago but today is empowering companies to deliver better, smarter services with greater ease and on broader scales than ever before. At Singularity University's inaugural Global Summit, Neil Jacobstein, chair of Artificial Intelligence and Robotics, provided a primer showing how artificial intelligence literally transforms everything it touches. First of all, it's critical to define the scope of artificial intelligence (AI), which can be categorized into four areas: techniques in pattern recognition, software agency (that is, software that acts like real users), an exponential technology that is accelerating other exponential technologies, and a vision of a future superhuman intelligence (that fortunately hasn't happened yet). Anyone who has seen a science fiction film is likely familiar with this last area, but it's the other three areas where AI is making huge strides at a revolutionary pace.

You can add one more name to the constantly expanding list of companies that want a slice of that autonomous driving pie, as a new company named Drive.ai The new company, which also announced that it has added former General Motors Vice Chairman and Board Member Steve Girsky to its Board of Directors, is looking to put its stamp on the self-driving space with its own deep learning algorithms. These full stack deep learning algorithms, Drive.ai CEO Sameep Tandon says that the team at Drive.ai has been working on these deep learning applications since the company was founded in 2015. For now, the company says it will offer a retrofitted system that can be used in existing vehicle fleets.

Imagine it's 2018 and some scientists from NASA are at the White House to see President Clintrump. There's a piece of space coming toward us; it is rocky, and icy, and big, and the risk of it hitting the Earth is much larger than anyone is comfortable with. Even if there's time to act, there won't be much of it. Where did it come from? How come we didn't spot it until now? What's the best course of action to take?

As the election season rampages on, we categorize voters into broad demographics -- soccer moms, NASCAR dads, blacks, whites, ALICEs, yuppies -- in an attempt to understand and discuss this complex, churning electorate. In doing so we're tapping into something fundamental about how we perceive the world: not as a sequence of singular individuals, but rather as a massive set of overlapping taxonomies that, taken together, comprise an impressively structured human experience. With fewer than 20 yes/no queries on category membership we can often identify a single object amidst a staggering breadth of possibilities. We've grouped everything that we know to exist and the groupings themselves are the primary subject of our thoughts. We can go the other direction as well -- taking an object and placing it in its many groups.

I've spent a lot of time talking about what it takes to become a "real" data scientist. I still believe these skills are imperative if you want to be a legitimate data scientist. However, I think the discipline is about to experience a significant change in how it emphasizes each of these characteristics. And as such, data science may never be the same. When the first machine learning "platforms" came about, most of the data science community was pretty skeptical.

However, if fraud occurs in small distant purchases and in large local ones, as in Figure 1, the task of classification is too complex. Interest in the approach faded for a while, but at the end of the 1970s people worked out how to tackle more complex classification tasks using networks of artificial neurons arranged in layers, so that the outputs of one layer formed the inputs of the next. The difficult part of all this is that the network has to identify the concepts to be captured in the hidden middle layer on the basis of information about how changing the weights on the links between the middle and output layers affects the final classification of transactions as fraud or bona fide. The problem is solved by computing a measure of how a change in the final set of weights changes the rate of errors in the classification and then propagating that measure backwards through the network.

I try to not to get all worked up over recruiters being replaced by robots, or recruiting-is-dead click-bait articles, but some of them are just annoying as heck. I want to try and bring some additional facts and sanity to this discussion, as most of the discussions I have seen are based on emotional and anecdotal responses. I also wanted to bring other industry voices into this discussion like China Gorman, Gerry Crispin, Kevin Wheeler, and Glen Cathey as we believe this particular topic has an enormous impact on the future of our profession. You build a great case for recruiters to take seriously the advancements that AI, algorithms, bots, and VR are making in the talent acquisition space and tie it up in a way that makes one look away at their own peril!" -- China Gorman I am not a futurist. I don't have a crystal ball, but what I do have is 20 years of global recruiting experience leading functions all around the world, and I have been an agency and corporate recruiter.

What do spam discovery, facial acknowledgment, item proposals and credit card fraud detection have common in them? Well, they're all procedures getting to be more quick-witted by the second, all because of machine learning. Putting it better, machine learning is the thing that empowers PCs to pick up insight after some time and as a matter of fact, the product applies few algorithms to the huge chunk of information and distinguishes examples to settling on choices and adjusting in view of what's revealed. With machine learning, organizations can profit by the data that would some way or another require a multitude of individuals to contemplate, translate and follow up on. It does the work which would take an army of people to exploit the data.

People are more into texting than talking. About 65% percent of us would prefer to have a long and painful conversation via Whatsapp than to have one-minute phone call or face-to-face meeting. We text while driving, even though we know it's extremely dangerous. Even when it comes to sacred traditions, families may still sit together at the dinner table, but more and more often, they're texting other people. There is simply too much going on: too many people to talk to, too many contacts to maintain -- it can feel easier to live as a digital introvert, dealing with others at your own pace, than to waste energy on real-world conversations you don't know how to start or how to end, and with frankly zero motivation to explain yourself when you simply are not in the mood.