In the fast-paced world of today, knowledge of global trends is imperative when it comes to success. Whether you're about to give an elevator pitch or sit down with your employees, what's going on in the world should always inform on your strategy, your content, and your tone. Outdated information is not only useless in the startup world, but also detrimental to your overall success. But with all the information flying around the world today, what is the best way to figure out what's relevant and what's outdated? It can take hours to check emails and respond to important messages. But what if a robot could undertake it all.

What better way to create a movie trailer about an artificially enhanced human than to use the reality behind the premise; artificial intelligence (AI). That's just what a partnership between IBM Research and 20th Century Fox recently set out to do, when they used machine learning techniques to produce what they described as the "first ever cognitive movie trailer." You'll have to judge the merits of the result yourself, but what is beyond doubt is this is just one example of the many ways AI and machine learning techniques are already changing the face of the entertainment industry. It's only makes sense that creative industries are leading the pack when it comes to the adoption of and experimentation with AI. Media, entertainment, and advertising are all the on the cutting edge when it comes to the adoption of AI and machine learning.

Given that Sinovation Ventures founder Dr. Kai-Fu Lee has around 50 million followers on Chinese social networks, he has become an oracle when it comes to predicting the future of tech in China. Kai-Fu Lee talked about the most important trends in Chinese startups at TechCrunch Beijing 2016. Sinovation Ventures recently raised the equivalent of $675 million in total across a Chinese and an American fund, and the firm has over 300 companies in its portfolio. "We can invest up to $15 million per company now," Kai-Fu Lee said. And by far, the most important area for future Sinovation Ventures investments will be artificial intelligence.

Artificial intelligence pops up as a buzzword every few years, but it has never moved beyond novelty status. This time, though, it is here to stay, and startups are poised to drive the AI economy forward. Newcomer ROSS Intelligence, for example, has gained law firm clients by developing a fully automated AI "lawyer" capable of supporting the legal research needs of large offices. Developed on IBM's Watson, ROSS is well on its way to becoming a fixture in the legal industry, automating tasks that could take days or weeks for humans to complete. Popular business messaging app Slack -- another startup -- is working on incorporating AI to act as an intelligent personal assistant capable of talking back and answering questions that have been asked before, saving companies time.

Tucked into a courtyard in central Paris, the 35 employees of Snips are hunched over their computers trying to put the finishing touches on a new version of the company's artificial intelligence app for smartphones. The company is packed full of big brains, many of them products of France's leading universities and a culture that is historically strong in mathematics. And they're not afraid to let you know it. Etched into winding wooden staircases that lead to Snips offices are a series of math puzzles that job recruits are asked to solve as they work their way upstairs. Snips' app wants to scan all the data across the apps on your smartphones to deliver insight about you and eventually become a hyper-smart personal assistant. But it promises to take a privacy-friendly approach by keeping all the data on your phone, where it will do the processing, rather than hoovering data up into the cloud.

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.