There are various thought leaders who believe that we are experiencing the Fourth Industrial Revolution, which is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human. I am certain that healthcare will be the lead industrial area of such a revolution and one of the major catalysts for change is going to be artificial intelligence. With the evolution of digital capacity, more and more data is produced and stored in the digital space. The amount of available digital data is growing by a mind-blowing speed, doubling every two year. In 2013, it encompassed 4.4 zettabytes, however by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes (!).

Short answer (but you will want to keep on reading): both. Artificial intelligence (AI) is no longer science fiction with thrilling tales of robots taking over the world; it is real, current, practical science. Artificial intelligence has already surrounded us in many facets of our day-to-day life. For example, predictive text, or voice recognition such as when you use Siri or Google are commonplace examples of AI usage among laypeople. Technology driven industries are the ones most likely to try and exploit AI to its fullest potential, and the financial industry is no exception.

They might be tall, dark and handsome but men on the dating app Tinder suffer from low self-esteem, a study found. A survey of more than 1,300 men and women revealed that those who use the highly popular smartphone app tend to be less happy with their looks. Psychologists warn the app could be bad for your health, with users drawn into a downward spiral of physical comparisons. Researchers looked at more than 1,300 undergraduate students and asked them to complete questionnaires based on psychological state. They found that one in ten of them used the Tinder app.

The company recently sent out an email to its users announcing the service would shut down on Friday, September 12. "When we introduced Reachit, we wanted to give people the convenience of accessing all of their files from one place...we now have new priorities on Smart Devices, and as a result, we will be withdrawing Reachit," Lenovo said in its email. Reachit was first introduced in May 2015 during the Lenovo Tech World conference as a partnership between Lenovo and Microsoft. It then entered a public beta period, but only became available as a mainstream product in early June 2016. The story behind the story: Although Lenovo says Reachit is going away due to a repositioning of priorities, it's likely Cortana was already useful enough without the app. For example, you could ask a Cortana-powered REACHit to help you find the spreadsheet you were editing on Tuesday while at a local cafe.

We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical observations and a hybrid matrix factorization over time re-weighted interactions. Second, we exploit sequence properties in user-items activities and develop a RNN-based recommendation model. Our solution achieved 5$^{th}$ place in the challenge among more than 100 participants. Notably, the strong performance of our RNN approach shows a promising new direction in employing sequence modeling for recommendation systems.

Given a set $U$ of users and a set of items $I$, a dataset of recommendations can be viewed as a sparse rectangular matrix $A$ of size $|U|\times |I|$ such that $a_{u,i}$ contains the rating the user $u$ assigns to item $i$, $a_{u,i}=?$ if the user $u$ has not rated the item $i$. The goal of a recommender system is to predict replacements to the missing observations $?$ in $A$ in order to make personalized recommendations meeting the user's tastes. A promising approach is the one based on the low-rank nonnegative matrix factorization of $A$ where items and users are represented in terms of a few vectors. These vector can be used for estimating the missing evaluations and to produce new recommendations. In this paper we propose an algorithm based on the nonnegative matrix Factorization approach for predicting the missing entries. Numerical test have been performed to estimate the accuracy in predicting the missing entries and in the recommendations provided and we have compared our technique with others in the literature. We have tested the algorithm on the MovieLens databases containing ratings of users on movies.

Say hello to Nadine, a "receptionist" at Nanyang Technological University (NTU Singapore). She is friendly, and will greet you back. Next time you meet her, she will remember your name and your previous conversation with her. She looks almost like a human being, with soft skin and flowing brunette hair. She smiles when greeting you, looks at you in the eye when talking, and can also shake hands with you. And she is a humanoid.

It's fun to imagine all the other names Microsoft's voice assistant, Cortana, could have had: "Bill," "Explorer," "Pathfinder." None of these were considered, as far as we know, but Cortana Group Product Manager Marcus Ash did tell me recently, "Early on we thought about some names that are more generic. I won't share any specific names, but think of a typical Microsoft name circa 1998 and you get the idea." The decision to give Microsoft's consumer-facing intelligence platform a name was a strategic decision, one Ash and his team thought necessary to help people connect with the idea of Cortana and understand how they might engage with it. "Giving a name helps orient people to the kind of communications we're going to encourage them to use with Cortana. If it doesn't have this characteristic of human interaction… it's harder for someone to visualize, how am I supposed to interact?" said Deborah Harrison one of the writers on the Cortana team.

In 2011, Watson made a dramatic public debut on the Jeopardy! Using IBM Power Systems and analytics software developed with open frameworks like Apache Hadoop and UIMA, Watson showcased a cutting-edge ability to understand complex natural language questions and sift through vast libraries of unstructured human knowledge. And Watson proved to know a thing or two about Beatles songs and Olympic history too. But while the show served as a great platform to introduce amazing new technology to the world, the intelligence behind Watson was designed to solve real business problems. IBM Watson Analytics allows business users to better compete by helping them get more value from their data.

Assistants are all the rage right now. Everyone seems to be working on one. People imagine them becoming the new interface to computers -- why bother with apps and searching the web when you can just ask your assistant to do it for you? Yet, a major challenge stands between the dream assistant and the current reality. It's called the multi-agent problem, and most companies are reluctant to talk about it.