While dating has resorted into more of a swiping situation these days – and your image on social media and pixels account for more than the person you are face to face, relief comes in the form of knowing exactly what you can rely on to amp up your match-game. For all those wondering how Tinder knows exactly the kind of person you're most likely to "superlike" – that is a feature that represents greater interest than your regular right swipe – Tinder has revealed that it is all powered by artificial intelligence. Also read: Let's do polygamy: Married men can now look for another wife with Tinder-style dating app As told to Tech Crunch, the app uses a history of one's interaction to figure out a person who would possibly be "of special interest to you." They didn't get into extensive details about their choice making process, but broadly, it's an AI behind it all. "At Tinder we are focused on creating simple, fun and useful experiences for our users around the world," says Brian Norgard, Chief Product Officer at Tinder.

Q: I have an Amazon Echo and I am really concerned that Amazon is listening all the time. Does the Echo have any privacy settings? A: Ever since Echo hit the market, people have wondered how much the little glowing tower hears -- and remembers. Is Echo silently eavesdropping on our conversations, even between "wake phrases"? So far, Echo hasn't caused any mass hysteria, and most people are pretty content with the performance of their virtual assistant.

Samsung Electronics Co. Ltd. is stepping up its artificial intelligence game with the opening of a new research center focused on machine learning. The move doesn't come too soon, because Samsung is generally perceived to be trailing its rivals when it comes to AI. The plan is for the new AI research center to be operated by two of Samsung's main businesses, namely its consumer electronics and mobile arms, the company said. Those businesses will use the center to develop new technologies, Samsung told Reuters. Most of Samsung's AI efforts thus far have been focused on its Bixby digital assistant, which was launched earlier this year.

We've already seen more than we expected to in the world of AI with VR in video games, IoT in medicine, and smart cities being brought to life. We are really close to living in some sort of sci-fi movie, so it's a good idea to take a look at the most possible and promising machine learning and AI trends for the upcoming year and ask ourselves if we are ready for them. Healthcare is one of the biggest and most crucial industries in the world -- no wonder it's the one that is heavily using the latest technologies. First of all, with artificial intelligence and big data, scientists will soon get the opportunity to prevent certain diseases, like cancer. AI can analyze patients' history and records to understand the mechanisms of disease and enable doctors to be proactive instead of reactive.

The other day, I was at a meetup of retail leaders in Vancouver. There were about 20 of us in a room, listening to experts present on the future of the industry. Topics ranged from conversion optimization to omnichannel retail, to digital marketing best practices and digital transformation. The presentations were strong, but one moment, in particular, struck me. Near the end of one expert's deck, he brought up a slide titled "Machine Learning and Retail". He chuckled and said something along the lines of "you can't have a presentation today without mentioning'machine learning', so here it is". With that statement, he quickly moved on and wrapped up. And I thought to myself, that is the definition of a buzzword. Business and marketing leaders, today, know that artificial intelligence (AI) is a topic of conversation. They know they need to know about it. They know they should plan for it. But over and over again, we see examples of leaders who don't understand artificial intelligence.

Virtual medical and behavioral healthcare provider MDLive has made it easier for patients to access telehealth services with the introduction of Sophie, a digital personal health assistant. An artificial intelligence-based chatbot that is part of MDLive's platform, Sophie guides patients through the registration process by first asking a series of questions to open an MDLive account. Next, Sophie walks patients through the steps to download the MDLive app. Once downloaded, the MDLive app helps patients determine whether scheduling a virtual or in-person visit with a healthcare professional is necessary. Chatbot's are computer programs that simulate conversation with human users through digital devices such as Apple Inc.'s Siri and Amazon.com

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.

In this special guest feature from Scientific Computing World, David Yip, HPC and Storage Business Development at OCF, provides his take on the place of GPU technology in HPC. There was an interesting story published earlier this week in which NVIDIA's founder and CEO, Jensen Huang, said: 'As advanced parallel-instruction architectures for CPU can be barely worked out by designers, GPUs will soon replace CPUs'. There are only so many processing cores you can fit on a single CPU chip. There are optimized applications that take advantage of a number of cores, but typically they are used for sequential serial processing (although Intel is doing an excellent job of adding more and more cores to its CPUs and getting developers to program multicore systems). By contrast, a GPU has massively parallel architecture consisting of many thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

Artificial Intelligence, more commonly known as AI, isn't a new topic and has been around for years; however, those who have tracked its' progress have noted that 2017 is the year that has seen it accelerate than years previously. This hot topic has made its' way into the media, in boardrooms and within the government. One reason for this is things that haven't functioned for decades has suddenly began to work; this is going beyond embedded functions or just tools and expectations are high for 2018. There are several reasons why this year has recorded the most progress when working with AI. Before going into these four preconditions that allowed AI to progress over the past five years, it is important to understand what Artificial Intelligence means.

THAT a computer program can repeatedly beat the world champion at Go, a complex board game, is a coup for the fast-moving field of artificial intelligence (AI). Another high-stakes game, however, is taking place behind the scenes, as firms compete to hire the smartest AI experts. Technology giants, including Google, Facebook, Microsoft and Baidu, are racing to expand their AI activities. Last year they spent some $8.5 billion on deals, says Quid, a data firm. That was four times more than in 2010.