Will the technology be coming for your job next? The question of whether marketing is more science or art has never seemed more relevant now that highly sophisticated cognitive learning technology is able to assume many of the tasks involved in marketing -- in some cases, even doing them better than a human could. But visions of a completely automated campaign may be premature, according to executives from IBM and other companies at the forefront of AI who weighed in on the technology's impact during a panel discussion at ad:tech New York last week. In good news for creative directors, the experts said cognitive technology has the ability to free up marketers to spend more time tackling bigger picture responsibilities, such as finding the inspiration for the right voice and vision to make an emotional connection with consumers. By laying the groundwork for significantly more sophisticated one-to-one marketing, AI could even create a need to beef up analytics, content and other areas for businesses that are able to gain a competitive edge through customer-centric marketing.

At its core, the Internet of Things is about sensors embedded into devices of all kinds, which provide streams of data via internet connectivity to one or more central locations. The purposes for transmitting sensor data are myriad, but the assumption in all cases is that that data can then be analyzed and acted upon in some way that is beneficial to the user. If I ask myself how the IoT came to be, the shortest answer I can provide is that good'ol Moore's Law made the first three steps in this chain (Sense, Transmit, and Store) ubiquitous and commoditizable. The hardware, software, and connectivity required to perform these steps has become very small, very cheap, very efficient, and very broadly available. When we hit the point of critical mass a few years back when all of those "verys" became applicable qualifiers, the IoT was born.

Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. It provides much-needed broad perspective and mathematical preliminaries for software engineers and students entering the field, and serves as a reference for authorities. Written by major contributors to the field, it is clear, comprehensive, and authoritative. If you want to know where deep learning came from, what it is good for, and where it is going, read this book. There was a need for a textbook for students, practitioners, and instructors that includes basic concepts, practical aspects, and advanced research topics.

Computing was some pretty exciting stuff for those of us back in the 80s who still remember the first time we booted up our 386DX. While nobody could really say what the advantages of the "DX" were, better at math or something, we still ponied up the extra $200 USD to pick up that 386DX 16Mhz along with a Super VGA graphics card, then hooked that bad boy up to CompuServe via our lightning fast 14,400 baud U.S. Robotics "Sportster" modem. That was well before Al Gore created the Internet, and a lot has changed since then. So are we, so let's go through and define some of these terms and what they mean for investors. "The Cloud" – The idea here is that instead of purchasing applications then installing them onto a computer, you lease the applications on demand and access them over the internet.

A team of researchers from Pittsburgh's Carnegie Mellon University have created sets of eyeglasses that can prevent wearers from being identified by facial recognition systems, or even fool the technology into identifying them as completely unrelated individuals. In their paper, Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition, presented at the 2016 Computer and Communications Security conference, the researchers present their system for what they describe as "physically realisable" and "inconspicuous" attacks on facial biometric systems, which are designed to exclusively identify a particular individual. The attack works by taking advantage of differences in how humans and computers understand faces. By selectively changing pixels in an image, it's possible to leave the human-comprehensible facial image largely unchanged, while flummoxing a facial recognition system trying to categorise the person in the picture. Where the researchers struck gold was by realising that a large (but not overly large pair of glasses) could act to "change the pixels" even in a real photo.

When Elon Musk isn't outlining plans to use his massive rocket to leave a decaying Planet Earth and colonize Mars, he sometimes talks about his belief that Earth isn't even real and we probably live in a computer simulation. "There's a billion to one chance we're living in base reality," he said at a conference in June. Musk is just one of the people in Silicon Valley to take a keen interest in the "simulation hypothesis", which argues that what we experience as reality is actually a giant computer simulation created by a more sophisticated intelligence. If it sounds a lot like The Matrix, that's because it is. According to this week's New Yorker profile of Y Combinator venture capitalist Sam Altman, there are two tech billionaires secretly engaging scientists to work on breaking us out of the simulation.

Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.

Make it easy for your audience to quickly determine what they're about to digest. Use an abstract or introduction to recall your objectives and clearly state them for your readers. What is the problem that you've set out to solve? If you have a desired outcome or any expectations of your audience, say it, as this is the entire reason you're presenting them with your analysis. You then cover everything from your preamble in this section: the question you've been on a mission to answer, your hypothesis, and the methodology you've used.

DeepMind will not build an unstoppable AI on its own. Instead, both companies will release a series of programming tools on early 2017 that will allow researchers and hobbyist around the world build and train their bots to play Starcraft II. Google's DeepMind researcher Orion Vinyals made the announcement during BlizzCon 2016 at Anaheim, California. Vinyals was the top-ranked SC2 player in Spain's leaderboards before becoming a top scientist in the British AI startup. Vinyals believes the results of the investigation could translate to the real life.

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