VentureBeat has a job opening for an AI reporter with experience covering tech news via a wide variety of story types, including breaking news, analysis, and features. This is a full-time position. You have to enjoy covering the business of technology, but more important is a critical eye, an ability to develop sources, and a knack for breaking news. For this role, a demonstrable interest in AI is required, including machine learning, computer vision, natural language processing, neural networks, and self-driving cars. A readiness to cover the major tech giants as well as the little guys is a plus.

The recommendation engine is one of the biggest martech innovations of the last few years, and it's shaping our entire digital experience. For example, let's say I visit the Harvard Business Review website and read about manufacturing marketing tactics (as one does). Soon, I'm getting retargeted marketing emails recommending the latest manufacturing supply chain management articles and other related content, as well as customized recommendations the next time I visit the site. When I fire up Netflix, I know that my experience is going to be managed in part because Netflix knows I've been binging on the show Haven and it's going to suggest other supernatural crime fighting series. The development of recommendation engine technology has brought digital personalization to a whole new level.

Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functionality of any OMR pipeline. In this paper, we introduce a novel object detection method, based on synthetic energy maps and the watershed transform, called Deep Watershed Detector (DWD). Our method is specifically tailored to deal with high resolution images that contain a large number of very small objects and is therefore able to process full pages of written music. We present state-of-the-art detection results of common music symbols and show DWD's ability to work with synthetic scores equally well as on handwritten music.

We present a novel deep learning architecture to address the cloze-style question answering task. Existing approaches employ reading mechanisms that do not fully exploit the interdependency between the document and the query. In this paper, we propose a novel \emph{dependent gated reading} bidirectional GRU network (DGR) to efficiently model the relationship between the document and the query during encoding and decision making. Our evaluation shows that DGR obtains highly competitive performance on well-known machine comprehension benchmarks such as the Children's Book Test (CBT-NE and CBT-CN) and Who DiD What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate our model by ablation and attention studies.

SiFive does a quarterly series of tech talks, not necessarily directly to do with SiFive or even RISC-V. For example, last quarter it was Paul Kocher (and if you don't know that name, you need to go and read my post about that talk Paul Kocher: Differential Power Analysis and Spectre). This quarter it was Krste Asanović on Accelerating AI: Past, Present, and Future. This post will cover the past. The present and future have to wait (good title for a movie?).

Nielsen-owned media data specialist Gracenote wants to help music services make better mixes with fewer outliers: The company announced a new music dataset called Sonic Style Wednesday that classifies 90 million tracks not by the genre the artist is known for, but the actual style of the recording. This will allow services making use of Gracenote's data to for instance compile a playlist of all of Taylor Swift's dance pop hits, while keeping anything that sounds too much like country out of the mix. Or combine The Clash's old-school punk tracks without adding some of the bands new wave fare. "Now that playlists are the new albums, music curators are clamoring for deeper insights into individual recordings for better discovery and personalization," said Gracenote music and auto GM Brian Hamilton. Gracenote has been in the music data business for close to 20 years.

Modern motion-capture systems are the product of a century of tinkering, innovation and computational advances. Mocap was born a lifetime before Gollum hit the big screen in The Lord of the Rings, and ages before the Cold War, Vietnam War or World War II. It was 1915, in the midst of the First World War, when animator Max Fleischer developed a technique called rotoscoping and laid the foundation for today's cutting-edge mocap technology. Rotoscoping was a primitive and time-consuming process, but it was a necessary starting point for the industry. In the rotoscope method, animators stood at a glass-topped desk and traced over a projected live-action film frame-by-frame, copying actors' or animals' actions directly onto a hand-drawn world.

Located in Toronto's downtown core at MaRS Discovery District, the new Samsung AI Centre will contribute to building the connected future by accelerating the adoption of intelligence on multiple devices ranging from household appliances to cars. The Toronto AI Centre is a part of a network of research Centres dedicated to research and development in the field of AI. The Centre is the second Samsung AI Centre to be established in North America, with the other in Mountain View, California. The North America AI Centres are led by senior vice president, Dr. Larry Heck, a renowned expert in machine learning for spoken and text language processing, who also co-leads the expansion of Samsung's AI Centres around the globe. "Toronto and the GTA are epi-centres of machine learning and one of the world's foremost hubs for AI research and development. Home to not only world-class talent, but also some of the most innovative start-ups in the artificial intelligence field," said Dr. Larry Heck, Co-Head of Global Artificial Intelligence Research.

Remember that video US president Donald Trump tweeted in which he wrestled someone to the ground and started punching them? It was genuine footage of Trump from a popular wrestling show but he had the image doctored to replace the victim's head with the CNN logo and added the hashtag #FraudNewsCNN, just in case we didn't get the memo that he really dislikes the news network. But are these news networks as biased as he thinks? Do Fox News journalists say mostly nice things while those at CNN are busy portraying him in a negative light? Artificial intelligence (AI) in the form of sentiment analysis and stance detection can tell us what is really happening.

The concept of artificial intelligence (AI) is definitely not a new one. For most of us, our first encounter was through the science fiction (Sci-fi) movies. We have been gripped by The Terminator series, The Matrix, I. Robot, Ex Machina, all depicting the amazing imagination of humans to innovate and create machines that can analyze information, solve problems, reason, and function even more efficiently than humans. Though we might not have attained the level of artificial intelligence displayed in these movies, AI is very much a part of our lives today even though we might not be aware. It influences our work, entertainment, and leisure.