The drones fell out of the sky over China Lake, California, like a colony of bats fleeing a cave in the night. Over 100 of them dropped from the bellies of three Boeing F/A-18 Super Hornet fighter jets, their sharp angles cutting across the clear blue sky. As they encircled their target, the mechanical whir of their flight sounded like screaming. This was the world's largest micro-drone swarm test. Conducted in October 2016 by the Department of Defense's Strategic Capabilities Office and the Navy's Air Systems Command, the test was the latest step in what could be termed a swarm-drone arms race.

During my time at betaworks last summer, I worked on extracting principal topics from messages that Digg's Facebook Messenger Bot received. This post explains a topic extraction method I created specifically for chat messages, which we later used to develop RIO -- a topic mining engine that uses reinforcement to tune its algorithm and automatically predict tags for chat messages and news articles. Conversational bots today understand user messages only when rules are hard-coded to match the exact message pattern. Such scripted bots find it hard to scale and can be frustrating for users who want to have a basic natural language exchange with the bot. Every bot provides a service (for Digg this is news), but users don't necessarily restrict their messages to that.

TRAVELLERS sometimes have to show their travel documents five times when catching a flight: at check-in, at security, then occasionally at outbound immigration, before another check when boarding. Finally there is passport control at the destination. Each is a potential queue. So regular flyers will be interested in anything that might speed up the process. One answer could be facial-recognition technology.

We met Tanmay while he was giving a keynote speech to our tech associates (yes, he gives keynote speeches around the world). Please enjoy Tanmay's article on 5 machine learning services powered by Watson. Computers have never been good at understanding non-mathematical language, but a branch of artificial intelligence aims to change that. Machine learning (ML), a subset of artificial intelligence, aims to enable computers to understand natural language and allow them to reason for themselves. Leading this initiative are services like IBM's Watson, an open platform accessible to developers around the world. In today's world, everyone is using machine learning technologies on a daily basis, whether they realize it or not.

There is more to a story than meets the eye, and some stories deserve to be presented from more than just one perspective. With Flipboard 4.0, we have released story roundups, a new feature that adds coverage from multiple sources to a story and provides you with a fuller picture of an event. With our scale of millions of articles and constant stream of documents, it's impossible to generate these roundups manually. So, we have developed a clustering algorithm that's both fast and scalable, and in this blog post, I will explain how we create these roundups on Flipboard. Although there are many sophisticated automatic clustering algorithms, such as K-means or Agglomerative clustering, story clustering is a non-trivial problem.

Deep learning uses layers of neural networks to look for patterns in data. When a single layer spots a pattern it recognizes, it sends this information to the next layer, which looks for patterns in this signal, and so on. So in face recognition, one layer might look for edges in an image, the next layer for circular patterns of edges (the kind that eyes and mouths make), and the next for triangular patterns such as those made by two eyes and a mouth. When all this happens, the final output is an indication that a face has been spotted. Of course, the devil is in the details.

If you were to ask your device with an AI voice assistant a random question, what gender would respond to you? Female most likely, and that's because only female artificial intelligence assistants have been developed. Microsoft's Cortana, Amazon's Alexa, and Google's Assistant all have female voices. Apple's Siri also started as female only, but added a male voice option three years ago, even though the female voice is still the default. It might be easy to think that female AI voices have been the go-to because of sexism -- lots of men in leadership and developer roles at Silicon Valley -- and that may be at play, but there's more to it. Two studies recently cited by The Wall Street Journal show that men and women prefer a female voice assistant because they believe it's more welcoming and understanding.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. These and many other insights are from a recent survey completed by MIT Technology Review Custom and Google Cloud, Machine Learning: The New Proving Ground for Competitive Advantage (PDF, no opt-in, 10 pp.). Business services (13%) and financial services (10%) respondents are also included in the study.

This is another installment of Mighty AI's "Conversations in Machine Learning" blog series. Each week, our content human, Cassie, shares a summary of a recent conversation we had with a machine learning team and potential customer--what they're building, how they're handling training data today, etc. Read more about the series here. I've been reading a lot (and writing a little) about autonomous vehicles over the past year, and while it's all quite fascinating and sometimes makes me look up from my computer and say aloud to my coworkers "people are building cars that will DRIVE THEMSELVES, you guys" with a look on my face that indicates I've been really thinking about it and it's really blowing my mind, the idea has yet to give me the warm-fuzzies. Since meeting them at June's Conference on Computer Vision and Pattern Recognition (CVPR), we've been chatting with a Corporate Research Engineer and Automated Driving Research Engineer from a behemoth of a company that's working on self-driving car technology. And as I researched the organization for this post, it became clear their main motivations in advancing the field are uniquely noble: increasing safety, protecting the planet, and improving quality of life.

And they're really fun to write about, too. Contently has mastered the buzzword listicle year after year with witty insight on buzzspeak and content marketing lingo that constantly creeps into our conversations. While we've all grown accustomed to yesterday's buzzwords like "thought leadership," "storytelling" and "snackable content," there's a new slew of them that have entered our everyday content marketing language. Here are six more content marketing buzzwords you actually need to pay attention to in 2017 and why. This is the most basic form of personalization and has become a standard practice for email marketing.