The real value of machine learning comes from its ability to create predictive models which can guide an organization's future actions and discover never seen before patterns. Take the online advertising industry. The conventional tools provide advertisers with the ability to set budgets and channels to target. But with historical data of previous auctions, it is possible to create projections for future ad campaigns so that advertisers may increase revenues while optimizing cost. Even a cent saved through machine learning can bring about millions of dollars in savings on an annual basis.

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Did you know that the origin of the term "red tape" comes from red ribbons used to tie up oodles of legal documents? I learned it in Brian O'Keefe's masterful explication of the pesky problem of red tape, which appears on the cover of the just-out issue of Fortune. This is one of the articles we now refer to as "longform." It can't be summarized in a tweet, or even an essay topping a daily newsletter about the technology industry. What I can tell you briefly is that the problem is more persistent than you think.

After 23 years in office, the notorious Maricopa County Sheriff Joe Arpaio (R) might not win re-election. The latest Arizona Republic/Morrison/Cronkite News poll of the Arizona county sheriff's race shows Arpaio nearly 15 points behind Democrat Paul Penzone, 45.9 percent to 31.1 percent. Arpaio, who calls himself "America's toughest sheriff," made national headlines last year when he spoke out in favor of Donald Trump and joined him in the mission to "seek the truth" about President Obama's birth certificate. Arpaio's massive slip is likely due -- at least in part -- to voters' widespread opposition to building a border wall and deporting all undocumented immigrants, both proposals Arpaio supports. The poll found that 30.8 percent of the county's voters strongly disagree with mass deportation, while 41.7 percent disagree.

There are a few moments in the history of artificial intelligence (AI) that are considered major breakthroughs – events that showed the power of machine intelligence in matching or surpassing human performance. Two examples are Deep Blue versus Kasparov in 1997 and Watson versus Jennings in 2008. Most recently, AlphaGo versus Lee Sedol became another major victory, this time driven by a fast developing field known as "deep learning." Deep learning–a machine learning technique based on artificial neural networks–is growing in popularity due to a series of developments in the science and business of data mining. Prior to AlphaGo's victory over the currently best Go player Lee Sedol, computer programs that played Go had only been able to beat average players.

There would always be a first death in a driverless car and it happened in May 2016. Joshua Brown had engaged the autopilot system in his Tesla when a tractor-trailor drove across the road in front of him. It seems that neither he nor the sensors in the autopilot noticed the white-sided truck against a brightly lit sky, with tragic results. Of course many people die in car crashes every day – in the USA there is one fatality every 94 million miles, and according to Tesla this was the first known fatality in over 130 million miles of driving with activated autopilot. In fact, given that most road fatalities are the result of human error, it has been said that autonomous cars should make travelling safer.

Under the simple skin of Uber lies complexity you may not have considered: the logistics of predicting how long it will take rides (or meals) to arrive, setting pricing and even where to wait to give a driver the best odds of finding you for pick-up. Underlying those decisions is machine learning, using computers to find patterns and make predictions without explicitly programming them to do so. And that very important job falls to Danny Lange, a Danish-born researcher who joined the ride-hailing giant 11 months ago after a nearly two-year stint leading machine learning efforts for Amazon Web Services. Before that, Lange wrangled big data for Microsoft and even launched a Silicon Valley startup. He heads a growing team of researchers in San Francisco and Seattle.

The increasing pace of digital is changing civilization as we know it, according Peter Sondergaard, senior vice president of Gartner Research, who spoke on Oct. 17 from the middle of a harsh spotlight on a darkened stage at the Gartner Symposium ITxpo 2016 in Orlando, Florida. The digital world around us is in a permanent state of upgrade," he warned. The dramatic words were followed by other speeches delivered by Daryl Plummer, vice president and Gartner fellow, and Hung LeHong, vice president and Gartner fellow. The speeches were no less dramatic, but rather less dark, taking their tone from another early passage in Sondergaard's speech: "CIOs are builders again." CIOs are building an infrastructure for an increasingly digital business, Sondergaard said, noting that Gartner is estimating that within three years more than half the value of most company's products will arise from their digital content. That digital content will be built on a digital platform and an infrastructure that is critical because, according to Sondergaard, "When you build it, it will bring the capability to reach customers and things more intelligently." Traditional core IT systems remain important to the organization, because the business must continue to operate while the digital transformation takes place. This traditional IT is Mode 1 in Gartner's Bimodal IT model, with Mode 2 as the dynamic, transformative digitalization mode. LeHong said, "You don't need two organizations for bimodal.

Sounds great, but how does this organization plan to do that?

In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a type of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. When trained on a set of examples in an unsupervised way, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors on inputs. After this learning step, a DBN can be further trained in a supervised way to perform classification. DBNs can be viewed as a composition of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, where each sub-network's hidden layer serves as the visible layer for the next.