David Cearley, vice president and Gartner Fellow, wrote that promises of artificial intelligence (AI) magically performing intellectual tasks that humans do and dynamically learning as much as humans is "speculative at best." However with 2018 rapidly approaching, AI is clearly on the minds of many businesses. Where are businesses practically applying AI in their digital workplaces? In October 2017, Cearley noted at the Gartner 2017 Symposium/ITxpo in Orlando, FL that Narrow AI currently holds the most promise. Narrow AI is composed of "highly scoped machine-learning solutions that target a specific task (such as understanding language or driving a vehicle in a controlled environment) with algorithms chosen that are optimized for that task," he says.

The Alexa voice assistant has been Amazon's remarkable runaway hit this past year. And as competitors such as Google and Apple try to catch up in the home, Amazon is pushing into a new market with Alexa for Business. Office workers will be able to use the firm's Echo smart speakers to set up meetings with colleagues, book conference rooms and other basic tasks. But the critical question isn't whether the technology works, but whether people will trust it in a business setting. "Without a doubt privacy and security is the number one issue," said Geoff Blaber, analyst at CCS Insight.

Skype just released a new version of its app for iOS devices, and this one is specifically targeted at iPhone X owners. For one thing, the update is modifying the look of the app, so it can now make use of the space surrounding the TrueDepth camera system's notch on top of the 10th anniversary iPhone. Just this past Wednesday, Skype introduced version 8.12 of its iOS app on the App Store. The update, which is clocking in at 113 MB, requires iOS 9.0 or later versions of Apple's mobile operating system. Based on the release notes for the app, version 8.12 adds support for iPhone X.

A petition calls on Amazon and Apple to reprogram Alexa and Siri so the voice assistants can push back against sexual harasments comments from users toward the voice assistant. The call comes as the #MeToo movement gives a voice to those who have been sexually harassed. Actress Alyssa Milano started the online campaign in October after Miramax co-founder Harvey Weinstein was accused of sexual harassment and rape by multiple women. The recent Siri and Alexa petition was launched on Care2 and has more than 6,000 supporters out its 10,000 signature goal. "In this #MeToo moment, where sexual harassment may finally be being taken seriously society, we have a unique opportunity to develop AI in a way that creates a kinder world," the petition said.

The bourbon whiskey brand Jim Beam released a smart decanter that speaks in a Kentucky accent for six months. The product sold out within two hours of its debut, USA Today reported Friday. The bourbon whiskey brand created JIM, the artificial intelligence decanter, to answer questions about bourbon and pour a shot when asked. The voice of the decanter is 7th Generation Master Distiller Fred Noe, who has a clear Kentucky accent. Although it seemed like Jim Beam was attempting to ridicule recent artificial intelligence products such as Amazon Echo or Google Home, JIM is a real device.

For Amazon, this was a week of war and peace. On Tuesday, Google said it would cut off Amazon's Fire TV streaming devices from YouTube, starting on January 1. Google also immediately began blocking YouTube from Amazon's Echo Show smart screen device, marking the second time it has done so. The search giant said it was responding to unfair treatment by Amazon, which won't sell Google devices through its online store and won't bring Amazon Prime Video support to Chromecast. The following morning, Amazon made good on its plans to launch a Prime Video app on Apple TV. The new app, first announced back in June, is a full endorsement of Apple's platform, supporting 4K HDR video and unique tvOS features like Siri search and TV app aggregation.

The yes-or-no swipe is the de facto way to find matches in dating apps these days, but it has its limits. Do you really want to sift through dozens of people just to find the one or two that share your interests? Even those sites that do offer search tend to focus just on basics like age or relationship goals. OKCupid, at least, thinks it can do better. It's launching a Discovery feature that lets you search for people who share similar interests.

In this Zetabyte Era, the abundance of information calls for personalization systems to ease the navigation of users. Among these systems, recommenders are becoming mainstream, and are used by major service providers such as Facebook, Amazon and Netflix. Some recommenders make use of the content of the items: these include popularity-based, knowledge-based or demographic-based schemes [8]. Others are content-agnostic: these are mainly collaborative filtering (CF) [14], [44] schemes, and are predominant today for they achieve good recommendation quality without requiring any prior knowledge of the content of the items recommended. Recommenders typically collect user preferences using explicit feedback [32], such as numerical ratings (star ratings in Imdb, Netflix, Amazon), binary preferences (likes/dislikes in Youtube), or unary preferences (retweets in Twitter). Yet, relying on explicit feedback raises issues regarding feedback sparsity (in systems where the item catalog is large, users tend to give feedback on a trace amount of those items, impacting the quality of recommendations [8]), and limited efficiency for recommending fresh items in reaction to recent user actions [37]. A few implicit recommenders have been proposed to answer those shortcomings.

Recommenders have become widely popular in recent years because of their broader applicability in many e-commerce applications. These applications rely on recommenders for generating advertisements for various offers or providing content recommendations. However, the quality of the generated recommendations depends on user features (like demography, temporality), offer features (like popularity, price), and user-offer features (like implicit or explicit feedback). Current state-of-the-art recommenders do not explore such diverse features concurrently while generating the recommendations. In this paper, we first introduce the notion of Trackers which enables us to capture the above-mentioned features and thus incorporate users' online behaviour through statistical aggregates of different features (demography, temporality, popularity, price). We also show how to capture offer-to-offer relations, based on their consumption sequence, leveraging neural embeddings for offers in our Offer2Vec algorithm. We then introduce BoostJet, a novel recommender which integrates the Trackers along with the neural embeddings using MatrixNet, an efficient distributed implementation of gradient boosted decision tree, to improve the recommendation quality significantly. We provide an in-depth evaluation of BoostJet on Yandex's dataset, collecting online behaviour from tens of millions of online users, to demonstrate the practicality of BoostJet in terms of recommendation quality as well as scalability.

The development of artificial intelligence gathers pace. Every day you can read some new information about chatbots, voice recognition, virtual assistants, robots, etc. More artificial intelligence software get on your phones and computers. However, are these technologies worth the money you pay? Though many scientists, professors, and entrepreneurs talk about how dangerous artificial intelligence can be, AI is still in the focus of Google, Facebook, Microsoft, and other companies.