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.

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.

In a corner of Alphabet's campus, there is a team working on piece of software that may be the key to self-driving cars. No journalist has ever seen it in action until now. They call it Carcraft, after the popular game World of Warcraft. The software's creator, a shaggy-haired, baby-faced young engineer named James Stout, is sitting next to me in the headphones-on quiet of the open-plan office. On the screen is a virtual representation of a roundabout.

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?

As excited as we are about the forthcoming generation of social home robots (including Jibo, Kuri, and many others), it's hard to ignore the fact that most of them look somewhat similar. They tend to feature lots of shiny white and black plasticky roundness. That's for admittedly very good reasons, but it comes at the cost of both uniqueness and visual and tactile personality. Guy Hoffman, who is well known for the fascinating creativity of his robot designs, has been working on a completely new kind of social robot in a collaboration between his lab at Cornell and Google ZOO's creative technology team in APAC. The robot is called Blossom, and we'd describe it for you, except that it's designed to be handmade out of warm natural materials like wool and wood so that every single one is a little bit different.

One response to the call by experts in robotics and artificial intelligence for an ban on "killer robots" ("lethal autonomous weapons systems" or Laws in the language of international treaties) is to say: shouldn't you have thought about that sooner? Figures such as Tesla's CEO, Elon Musk, are among the 116 specialists calling for the ban. "We do not have long to act," they say. "Once this Pandora's box is opened, it will be hard to close." But such systems are arguably already here, such as the "unmanned combat air vehicle" Taranis developed by BAE and others, or the autonomous SGR-A1 sentry gun made by Samsung and deployed along the South Korean border.

Before autonomous trucks and taxis hit the road, manufacturers will need to solve problems far more complex than collision avoidance and navigation (see "10 Breakthrough Technologies 2017: Self-Driving Trucks"). These vehicles will have to anticipate and defend against a full spectrum of malicious attackers wielding both traditional cyberattacks and a new generation of attacks based on so-called adversarial machine learning (see "AI Fight Club Could Help Save Us from a Future of Super-Smart Cyberattacks"). As consensus grows that autonomous vehicles are just a few years away from being deployed in cities as robotic taxis, and on highways to ease the mind-numbing boredom of long-haul trucking, this risk of attack has been largely missing from the breathless coverage. It reminds me of numerous articles promoting e-mail in the early 1990s, before the newfound world of electronic communications was awash in unwanted spam. Back then, the promise of machine learning was seen as a solution to the world's spam problems. And indeed, today the problem of spam is largely solved--but it took decades for us to get here.

You need just two eyes and two ears to drive. Those remarkable sensors provide all the info you need to, say, know that a fire engine is coming up fast behind you, so get out of the way. Autonomous vehicles need a whole lot more than that. They use half a dozen cameras to see everything around them, radars to know how far away it all is, and at least one lidar laser scanner to map the world. Yet even that may not be enough.

When it comes to digital assistants like Amazon's Alexa, my four-year-old niece Hannah Metz is an early adopter. Her family has four puck-like Amazon Echo Dot devices plugged in around her house--including one in her bedroom--that she can use to call on Alexa at any moment. "Alexa, play'It's Raining Tacos,'" she commanded on a recent sunny afternoon, and the voice-controlled helper immediately complied, blasting through its speaker a confection of a song with lines like "It's raining tacos from out of the sky" and "Yum, yum, yum, yum, yumidy yum." I think this ability to get music on demand is neat, too, and I didn't want to be rude, so I danced with her. But at the same time I was wondering what it's going to mean for her to grow up with computers as servants.