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.

Before Kanye West gets to the White House, first, we'll have to survive the robot apocalypse brought about by his A.I.-powered doppelgänger. It's a very real piece of software created by a high school student from West Virginia. You can use Alexa in Amazon's app now, and it's really smart Robbie Barrat, a 17-year-old hip-hop fan and coding whiz, taught himself to code using open source software, according to a report from Quartz. Initially, the software simply rearranged 6,000 Kanye rap phrases to create new songs, but now the software has been modified to create original rap lines using the Kanye word bank. On the YouTube Page demonstrating the software's ability, Barrat says, "Excluding the beat; this song was written 100 percent by a deep neural network."

TL;DR: Machine-driven communications tools are a reality now and artificial intelligence enabled tools will soon dominate the online information space. This paradigm shift isn't limited to artificial personal assistants like Siri and recreational chatbots like Xiaoice. It refers to machine-driven communication overwhelming Facebook, Twitter, YouTube, Match, Reddit, chat rooms, news site comment sections, and the rest of the social web. All of it will be dominated by machines talking. This machine communication will be nearly indistinguishable from human communication. The machines will be trying to persuade, sell, deceive, intimidate, manipulate, and cajole you into whatever response they're programmed to elicit. They will be unbelievably effective. Machine-driven communication is here now. Advances in artificial intelligence will radically increase the efficacy of machine-driven communication tools. A machine-dominated information environment is a rational extrapolation of current technology trends into the near future.

Carmakers and suppliers gave widely differing timelines for the introduction of self-driving vehicles on Thursday, showing the uncertainties surrounding the technology as well as a split between cautious established players and bullish new entrants. Chipmaker Nvidia, facing direct competition with the world's top chipmaker after Intel's $15 billion deal to buy autonomous driving technology firm Mobileye this week, gave the most optimistic predictions. Chief Executive Jen-Hsun Huang forecast carmakers may speed up their plans in the light of technological advances and that fully self-driving cars could be on the road by 2025. "Because of deep learning, because of AI (artificial intelligence) computing, we've really supercharged our roadmap to autonomous vehicles," he said in a keynote speech to the Bosch Connected World conference in Berlin. Germany's Bosch, however, the world's biggest automotive supplier, gave a timetable as much as six years longer to get to the final stage before fully autonomous vehicles, and declined even to forecast when a totally self-driving car might take to the streets.