I recently covered Voice Summit the largest voice tech conference that brings the conversational design ecosystem together in one place. The conference was hosted at New Jersey Institute of Technology. I was amazed about the impact voice technology will have on our lives. Voice search will account for 50 % of all search results by 2020. Voice technology is already in use in many home devices such as Amazon Alexa and Google home.

A team of innovation specialists at the Census Bureau is working to speed up the process for getting security authorizations--known as an authority to operate, or ATO--for new systems and applications. Among their potential solutions: Developing an artificial intelligence bot that offers wisdom from successful ATOs, akin to Microsoft's much-maligned Clippy office assistant. Security officials focused on the ATO process have long urged agencies to reuse authorizations for like-for-like systems. While leaders have said that is happening more often, program managers are often reticent to reuse an authorization that might not track exactly to the app they are standing up. But for a given security control, there is language and considerations for how the documentation is put together that can easily be borrowed from one authorization to the next, according to the Census FISMAtic project team.

High quality metadata plays an outsized role in improving enterprise search results. But convincing people to consistently apply quality metadata has been an uphill battle for most companies. One solution that has been around for a long time now is to automate metadata's creation, using rules-based content auto-classification products. Back in 2004, I ran a large, greenfield enterprise content management program for a big UK university. I was lucky to work with information management experts in the university library and a member of the W3C metadata group on the project.

Last year Kate Crawford, a New York University professor who runs an artificial intelligence research centre, set out to study the "black box" of processes that exist around the hugely popular Amazon Echo device. Crawford did not do what you might expect when approaching AI – namely, study algorithms, computing systems and suchlike. Instead, she teamed up with Vladan Joler, a Serbian academic, to map the supply chains, raw materials, data and labour that underpin Alexa, the AI agent that Echo's users talk to. It was a daunting process – so much so that Joler and Crawford admit that their map, Anatomy of an AI System, is just a first step. The results are both chilling and challenging.

In the past five years, machine learning has come a long way. You might have noticed that Siri, Alexa, and Google Assistant are way better than they used to be, or that automatic translation on websites, while still fairly spotty, is hugely improved from where it was a few years ago. But many still don't quite grasp how far we've come, and how fast. Recently, two images made the rounds that underscore the huge advances machine learning has made -- and show why we're in for a new age of mischief and online fakery. The first was put together by Ian Goodfellow, the director of machine learning at Apple's Special Projects Group and a leader in the field. He looked over machine-learning papers published on the online open-access repository arXiv over the past five years, and found examples of machine learning-generated faces from each year.

Artificial Intelligence is becoming more commonly used in a variety of fintech applications. Our infographic highlights how often a financial services customer can interact with A.I. during everyday routines. It is possible to encounter artificial intelligence in numerous ways while carrying out single tasks, such as using predictive text while talking to a virtual assistant that understands requests and connects with other third party platforms. While customers and employees enjoy increased convenience A.I. is also enabling bank grade security to protect our digital identities and finances. To access secure accounts many organisations apply 2 factor logins using a password, pin or device with biometrics such as voice and facial recognition.

People who use dating apps such as Tinder may be up to 27 times as likely to use drastic or unhealthy techniques to try and stay slim. Deliberately vomiting, taking laxatives and even using anabolic steroids is more common among dating app users, a study found. Researchers found'unrealistic' desires to look like celebrities on television and social media are driving people to damaging behaviour. And with an estimated 50million people around the world signed up to Tinder the scientists warned experts must better understand its damaging effects. Researchers said social media and TV shows reinforce'ideal' body images which drive men to try and become more muscly and women slimmer, which may drive them to drastic weight loss measures (Pictured: Love Island contestants Anton Danyluk and Amber Gill – the show is well-known for displaying young people with extremely honed bodies.

Originally chatbots were defined as any bot that is available on a chat platform. Virtual assistants like Siri and Cortana are also dialogue systems but not chatbots. Some Expert Systems built for making business decisions can also have a dialogue system to interact with them. Those aren't available on chat platforms and definitely don't chat.

Recommender systems have become ubiquitous, transforming user interactions with products, services and content in a wide variety of domains. In content recommendation, recommenders generally surface relevant and/or novel personalized content based on learned models of user preferences (e.g., as in collaborative filtering [Breese et al., 1998, Konstan et al., 1997, Srebro et al., 2004, Salakhutdinov and Mnih, 2007]) or predictive models of user responses to specific recommendations. Well-known applications of recommender systems include video recommendations on YouTube [Covington et al., 2016], movie recommendations on Netflix [Gomez-Uribe and Hunt, 2016] and playlist construction on Spotify [Jacobson et al., 2016]. It is increasingly common to train deep neural networks (DNNs) [van den Oord et al., 2013, Wang et al., 2015, Covington et al., 2016, Cheng et al., 2016] to predict user responses (e.g., click-through rates, content engagement, ratings, likes) to generate, score and serve candidate recommendations. Practical recommender systems largely focus on myopic prediction--estimating a user's immediate response to a recommendation--without considering the long-term impact on subsequent user behavior. This can be limiting: modeling a recommendation's stochastic impact on the future affords opportunities to trade off user engagement in the near-term for longer-term benefit (e.g., by probing a user's interests, or improving satisfaction).

The data scarcity of user preferences and the cold-start problem often appear in real-world applications and limit the recommendation accuracy of collaborative filtering strategies. Leveraging the selections of social friends and foes can efficiently face both problems. In this study, we propose a strategy that performs social deep pairwise learning. Firstly, we design a ranking loss function incorporating multiple ranking criteria based on the choice in users, and the choice in their friends and foes to improve the accuracy in the top-k recommendation task. We capture the nonlinear correlations between user preferences and the social information of trust and distrust relationships via a deep learning strategy. In each backpropagation step, we follow a social negative sampling strategy to meet the multiple ranking criteria of our ranking loss function. We conduct comprehensive experiments on a benchmark dataset from Epinions, among the largest publicly available that has been reported in the relevant literature. The experimental results demonstrate that the proposed model beats other state-of-the art methods, attaining an 11.49% average improvement over the most competitive model. We show that our deep learning strategy plays an important role in capturing the nonlinear correlations between user preferences and the social information of trust and distrust relationships, and demonstrate the importance of our social negative sampling strategy on the proposed model.