A similar process is done on the target images to mask out the faces and intelligently put your own in their place -- and voila! It's not limited to hairstyles, either: put yourself in a movie, a location, a painting -- as long as there's a similarly positioned face to swap yours with, the software can do it. Kemelmacher-Shlizerman has also created systems that do automated age progression, something that can be useful in missing persons cases. "This is a first step in trying to imagine how a missing person's appearance might change over time."

Ever wonder what you would look like with long, wavy hair? I think you'd look great. But how can you try on a few looks without spending a fortune at the salon, or hours in photoshop? All you need is a selfie and Dreambit, the face-swapping search engine. The system analyzes the picture of your face and determines how to intelligently crop it to leave nothing but your face.

The author reflects on new research reports on the impact of artificial intelligence (AI) on the workplace. His prediction: AI will make internal politics nastier within management ranks. Artificial intelligence is a hot topic these days. Not surprisingly, news outlets north of the 49th parallel jumped on the release of a new research report cleverly titled "The Talented Mr. Robot: The Impact of Automation on Canada's Workforce." The study was conducted by the Brookfield Institute for Innovation Entrepreneurship, a newly created independent and nonpartisan institute, housed within the Ryerson University of Toronto, dedicated to advancing Canada's innovation and entrepreneurship.

At first this may come as a disappointment to those who imagined a future of conversing with intelligent machines capable of completing a variety of tasks. But upon closer inspection, there's still plenty of potential value behind the more limited AI apps being developed today. Bowles said one of the chief benefits of today's AI apps is they will help businesses make sense of all the data they've been piling up since the early days of big data. AI systems will be able to quickly deliver insights from large data stores by using machine learning to pick out relevant information and natural language processing to write up human-readable reports, all in a matter of minutes. "It's mostly about helping people discover what's in the data," he said.

You'll have to get in the habit of stepping away from your computer to create the social capital you need. Grab lunch with a developer who may build out your next design. No need for an agenda -- just get to know each other. Spend time with researchers who have their finger on the pulse of your customers, sales people who hear frequent requests, product managers who understand schedules and scope, and customer service agents who know where users struggle the most. All have valuable context to offer you.

Analytics, statistics, operations research, data science and machine learning - with which term do you prefer associate? Are you from the House of Capulet or Montague, or do you even care? That which we call a rose By any other name would smell as sweet." Romeo was from the house of Montague, Juliet, the house of Capulet, and this distinction that meant that their families were sworn enemies. The play is a tragedy, because by the end the two lovers end up dead as a result of this long-running feud. Statistics, data science and machine learning are but a few of the "houses" that feud today over names, and while to my knowledge no deaths have resulted from this debate the competing camps have nearly come to blows. How has the emerging field of Analytics impacted the Operations Research Profession? Is Analytics part of OR or the other way around? Is it good, bad, relevant, a nuisance or an opportunity for the OR profession? Is OR just Prescriptive or is it something more? In this panel discussion, we will explore these topics in a session with some of the leading thinkers in both OR and Analytics. Be sure to attend to have your questions answered on these highly complementary and valuable fields."

You've studied machine learning, you're a dataframe master for massaging data, and you can easily pipe that data through a bunch of machine learning libraries. You go for a job interview at a SAAS company, you're given some raw data and labels and asked to predict churn, and come on - are these guys even trying? You generate the shit out of some features, you overfit the hell out of that multidimensional manifold just so you can back off and show off your knowledge of regularization, and then you put the icing on the cake by cross validating towards a better metric for the business problem than simple accuracy. You roll up on an ecommerce company, and they trick you by basically giving you no features. Ha! Nice try, but you know that's a classic recommender system.

Patterns are what machine-learning algorithms exist to sniff out. But detecting those patterns is almost never the endgame. Typically, we use machine learning (a category in which I also include deep learning) to drill down to the patterns most relevant to some decision-support scenario, such as identifying fine-grained nuances of customer sentiment for use in target marketing or pinpointing the signs of imminent equipment failure through continuous sifting of scattered event-log databases. Once discovered, the statistical patterns can take on a programmatic life of their own that goes far beyond decision support in potential applications. As crystallized in machine-learning models, the patterns can become key assets in the development of other algorithmic applications that have little or no relevance to decision support.

What's more, when you're reading one-handed on your phone, you can use the volume buttons to navigate back and forth. Bubble Zoom is part of the most recent version of Google Play Books, and yes, only Android users are privy to the feature right now. If you're using Google's software, you can employ a technical preview of the reading tool while browsing DC and Marvel volumes that support it. Google will be collecting feedback on the update and plans to make it available on all digital comics and manga. The company says it has to teach its machine learning algorithms how to read more styles before expanding its supported library.

Abdul is a young Entrepreneur, Technical Writer, Security Blogger and IT Analyst. He has a keen eye on the Cyberspace and other tech related developments.