The following post was first published in an earlier edition of Marketing Insider: Cross-Channel. Creating original branded content solves many problems for marketers, but also presents challenges -- among them distribution and realizing ROI from what can be a costly investment. Time Inc., CBS and Telepictures are among hundreds of publishers working with IRIS.TV, which recently introduced a product to manage the distribution of branded content. Its video personalization solution uses artificial intelligence and machine learning technology so publishers can automate the programming of their video libraries for the individual based on that person's preferences and behavior. We spoke with Rohan Castelino, director of business development and marketing with IRIS.TV, about how this works.

Apple's Siri, Microsoft's Cortana and all the search algorithms out there today show that robots are often the first point of contact for marketers trying to reach customers. For those concerned about a robot takeover, remember that artificial intelligence (AI) can be a marketer's best friend. Robots, unlike people, are consistent. They are good at tracking and delivering high-quality results which save us time, money and effort. Robots ultimately answer to us, making them effective helpers upon which we can increasingly rely.

When Amazon first coined the strapline "Ask Alexa" for its virtual assistant, it couldn't have predicted the X-rated nature of some of the requests. "She" may boast an encyclopaedic knowledge, but research by consumer behaviour analysts Canvas8 reveals that some users are more interested in a virtual hook-up than fact finding. And she's not the only target: the equally smooth voice of Microsoft's Cortana is getting customers just as hot under the collar apparently. From perma-smiling avatars in traditionally female support roles, to hyper-sexualised "fembots" pandering to male fantasies, the female form is everywhere in techno-world - attractive, servile and at your command. A little more conservative, but just as eager to please, is virtual personal assistant Amy Ingram, the brainchild of New York start-up X.ai.

I can sift through millions of data points, analyze them using sophisticated analytics models and respond with answers in seconds! I'm also learning continuously to improve recommendations as we go along." This creates new users and unbelievably new usages for Analytics Maya's Causality Model uses Machine Learning and Neural networks to predict and explain cause and effect.

In September 2016, Google launched its new chat app Allo in India. The app's highlight was the Artifical Intelligence powered Google Assistant -- a conversational bot that could help perform tasks such setting reminders, searching for restaurants, booking movie tickets, sending a text to someone and much more. The main caveat was that the Assistant works only online. While it is quite obvious that one would not be able to get the latest movie shows or their favorite football team scores without the Internet, there are phone functions, such as reminders and calls, which can surely be handled by an AI bot without an Internet connection. While Google Now does support some functions offline, Google Assistant and Apple's Siri always asks for online support when you tap the button.

Amazon Alexa, the voice assistant of Amazon, was everywhere on CES 2017. Integrated in cars, refrigerators, assistant devices and more, there's an emerging trend which requires us to talk to devices. But are we actually going to talk to machines on a structural level? The first sales forecasts of the Amazon echo are good. Google's voice assistant also had significant sales numbers last Christmas.

The number of registered AI start-up firms increased by over than 50 per cent globally in 2016 – with funding almost doubling to $9.89bn during the same period. According to market analysts and research firm Venture Scanner, since March last year, the number of registered AI firms has risen globally from 957 to 1,535 across 71 different countries. Categories covered in its findings include businesses involved in; computer vision / image recognition, computer vision / image recognition, context aware computing, deep learning, machine learning, gesture control, natural language procession, personalised recommendation engines, smart robots, speech recognition, speech to speech translation, video automatic content recognition and virtual assistants. Venture Scanner claims of the 1,535 companies tracked, 731 of them have received funding, totalling $9.89 billion - up from $4.8 billion a year earlier. Venture Scanner has also revealed that the US currently leads the way for AI start-up companies, with more than 740 registered.

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, held in March 2015, and several articles in this issue are extensions of work presented at that symposium. The articles in the collection address key modeling, methodological, and computational challenges in achieving effective coordination with machines, propose solutions that overcome these challenges under sensory, cognitive, and resource restrictions, and illustrate how such solutions can facilitate coordination across diverse and challenging domains. The contributions highlight turn-taking and coordination in human-machine interaction as an emerging and evolving research area with important implications for future applications of AI.

Today, even though similar approaches are in use, they are usually just one part of complex recommendation approaches that can include large collections of algorithms and data sources. The data set was again provided year that the summer school on Recommender Systems by Moviepilot and was co-organized by TU Berlin. By 2007, the Netflix Prize had The second track focused on recommendation of scientific attracted thousands of participating teams, and the papers. The challenge attracted 30 participating Netflix Prize concluded. At the by Simon Fraser University and Yelp who also 2010 ACM RecSys conference, the seed for what provided the data. CAMRa attracted a moderate the 2014 challenge did not focus on classical recommendation, number of participants, but contributed to establishing but rather on prediction of user engagement, the RecSys Challenge series.

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