Airlines are coming off a rough six months of brand perception. Forget about mishandled luggage, the bigger problem is mishandled passengers. Customer service – or lack of it – has given the airline industry a "black eye." Even the most customer-focused airlines are not immune to computer outages and winter storms that cause thousands of flights to be canceled. And because of the negative publicity the industry has received, it is under a microscope.

The technology entrepreneur Elon Musk recently urged the nation's governors to regulate artificial intelligence "before it's too late." Mr. Musk insists that artificial intelligence represents an "existential threat to humanity," an alarmist view that confuses A.I. science with science fiction. Nevertheless, even A.I. researchers like me recognize that there are valid concerns about its impact on weapons, jobs and privacy. It's natural to ask whether we should develop A.I. at all. I believe the answer is yes.

While the phone you carry may be significantly less bulky than some of the original mobile devices, it's one heck of a lot more powerful. It has a touch screen, lacks a hinge and is filled with apps. And those apps are getting smarter every day with the help of machine learning and AI. Whether it's making your email smarter, streamlining tasks or solving the riddle of incurable diseases, AI and machine learning will probably have a huge impact in your life. As we use our phones, we provide key information to the apps and software installed on them.

Recruitment systems are evolving so candidates can use artificial intelligence to match themselves to roles in a similar way they might seek out a partner online. This has benefits for employers too, explains Cath Everett. The idea of being able to find the perfect job candidate who is not only ideal in terms of qualifications and experience but also in terms of cultural fit, ethos and personality sounds too good to be true. Technology is disrupting work, but what do staff think? Nonetheless, it is something that a number of artificial intelligence (AI) systems are claiming to offer recruiters.

Industrial Internet of Things (IIoT) is already revolutionizing domains such as manufacturing, automobiles and healthcare. But the real value of IIoT will be realized only when Machine Learning (ML) is applied to the sensor data. This article attempts to highlight how ML augments IIoT solutions by bringing intelligent insights. Cloud computing has been the biggest enabler of connected devices and enterprise IoT. Cheaper storage combined with ample computing power is the key driver behind the rise of IIoT.

Early in August, NYU professor Siddharth Garg checked for traffic, and put a yellow Post-it onto a stop sign outside the Brooklyn building in which he works. When he and two colleagues showed a photo of the scene to their road-sign detector software, it was 95 percent sure the stop sign in fact displayed a speed limit. The stunt demonstrated a potential security headache for engineers working with machine learning software. The researchers showed it's possible to embed silent, nasty surprises into artificial neural networks, the type of learning software used for tasks such as recognizing speech or understanding photos. Malicious actors can design that behavior to emerge only in response to a very specific, secret, signal, as in the case of Garg's Post-it.

Gaurav Chakravorty, co-founder of AI investment advisors qplum, likes to use sporting analogies to illustrate changing trends within finance. The way high frequency trading (HFT) seemed to work like magic in the old days reminds him of Manchester United under Sir Alex Ferguson. Between 1993 and 2013 Manchester United won the English Premier League 13 times, an incredible record. The truth was Ferguson used a machinery that other clubs had not yet happened upon. He would scout clubs in Europe for talented youngsters and be willing to pay top dollar for young stars without a proven track record at a big club.

Such enthusiasm will be tempered by a generally conservative medical community trying to cope with a technology that promises to transform livelihoods and businesses. Where life and death considerations have to be made, regulatory hurdles will be put in place to protect patients and providers. Nevertheless, the precarious economic model of the current healthcare industry in the US will yield to the inexorable forces of supply and demand. And, by all accounts, the supply side of that equation is going to be driven to a large degree by AI.

Amazon isn't synonymous with high fashion yet, but the company may be poised to lead the way when it comes to replacing stylists and designers with ever-so-chic AI algorithms. Researchers at the e-commerce juggernaut are currently working on several machine-learning systems that could help provide an edge when it comes to spotting, reacting to, and perhaps even shaping the latest fashion trends. The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail--making recommendations based on products popping up in social-media posts, for instance. And it could help the company expand its clothing business or even dominate the area. "There's been a whole move from companies like Amazon trying to understand how fashion develops in the world," says Kavita Bala, a professor at Cornell University who took part in a workshop on machine learning and fashion organized by Amazon last week.

Despite the fact that he does not see very well, Alexei Efros, recipient of the 2016 ACM Prize in Computing and a professor at the University of California at Berkeley, has spent most of his career trying to understand, model, and recreate the visual world. Drawing on the massive collection of images on the Internet, he has used machine learning algorithms to manipulate objects in photographs, translate black-and-white images into color, and identify architecturally revealing details about cities. Here, he talks about harnessing the power of visual complexity. You were born in St. Petersburg (Russia), and were 14 when you came to the U.S. What drew you to computer science?