Television news coverage brings to mind images of newsreaders in studios, reporters in the field, previously recorded footage and rapid-fire barrages of vivid advertising imagery. This raises the question of just how long a typical "shot" lasts and whether there are substantial differences between television news stations. Using the "Shot Change" detection feature of Google's Video AI platform to analyze a week of television news, what new insights could we learn about the speed at which television news narratives move? Google's Video AI API brings the company's image analysis algorithms to the world of video. While in the past videos had to be split into frames and analyzed as still images, the Video AI API enables videos to be analyzed natively, enabling time-based analysis like detecting shot changes.

An image of a Syrian refugee using virtual reality to help researchers design a shelter has been chosen as the winner of the 2019 national science photography competition organised by the Engineering and Physical Sciences Research Council.

Don DeSanto, director of strategic partnerships for the BAE Systems Intelligence & Security sector, said of the project: "RPAs fuel machine learning tools by feeding them high volumes of structured data necessary for it to begin learning and improving automatically, without being programmed. Human-machine teaming is the future of technology, and RPAs serve as workforce multipliers that can be designed to automate many common tasks performed in organizations every day." According to information from BAE Systems, the tools allow human analysts to shift their attention to managing more critical challenges.; RPAs are capable of searching, sorting, and in some cases, processing large data sets to complete work that currently takes employees hours to complete.

Deep learning has revolutionized the machine understanding of imagery. Yet today's image recognition models are still limited by the availability of large annotated training datasets upon which to build their libraries of recognized objects and activities. To address this, Google's Vision AI API expands its native catalog of around 10,000 visually recognized objects and activities with the ability to perform the equivalent of a reverse Google Images search across the open Web and tally up the top topics used to caption the given image everywhere it has previously appeared, lending unprecedentedly rich context and understanding, even yielding unique labels for breaking news events. What might this process yield for a week of television news? Google's Vision AI API represents a unique hybrid between traditional deep learning-based image labeling based on a library of previously trained models and the ability to leverage the open Web to annotate images based on the most common topics visually similar images are captioned with. Using its Web Entities feature, the Vision AI API performs what amounts to a reverse Google Images search over the open Web, identifying images across the entire Web that look most similar to the given image.

Lionbridge, the world's most trusted globalization partner, is pleased to announce the launch of Lionbridge AI. Marrying the market-leading human-annotated AI training data services and linguistic capabilities of Lionbridge, formerly known as Machine Intelligence, with the data training platform and marketplace of recently-acquired Gengo, Lionbridge AI provides a suite of capabilities and services that meets the end-to-end needs of companies building the next generation of machine learning and artificial intelligence (AI) systems. "This is an incredible opportunity to bring together our services, technology platform and voice capabilities into a single offering," said Lionbridge CEO John Fennelly. "We are confident that Lionbridge AI will help our customers deliver improved, more engaging, and increasingly human-like experiences to their artificial intelligence initiatives." As nearly every company contemplates how to use AI to build smarter products and services, while determining how to derive greater predictive capabilities to strengthen the customer experience, Lionbridge AI is perfectly positioned to support the ever-expanding array of uses for artificial intelligence.

Dance is an art and when technology meets this kind of art, it's a novel attempt in itself. Several researchers have attempted to automate several aspects of dance, right from dance notation to choreography. Furthermore, we have encountered several applications of dance automation like e-learning, heritage preservation, etc. Despite several attempts by researchers for more than two decades in various styles of dance all round the world, we found a review paper that portrays the research status in this area dating to 1990 \cite{politis1990computers}. Hence, we decide to come up with a comprehensive review article that showcases several aspects of dance automation. This paper is an attempt to review research work reported in the literature, categorize and group all research work completed so far in the field of automating dance. We have explicitly identified six major categories corresponding to the use of computers in dance automation namely dance representation, dance capturing, dance semantics, dance generation, dance processing approaches and applications of dance automation systems. We classified several research papers under these categories according to their research approach and functionality. With the help of proposed categories and subcategories one can easily determine the state of research and the new avenues left for exploration in the field of dance automation.

Two years ago IBM set new performance records on conversational telephone speech (CTS) transcription, by benchmarking its deep neural network based speech recognition systems on the Switchboard and Callhome corpora, two popular publicly available data sets for automatic speech recognition [1]. Here we show that this impressive performance holds on other audio genres. Similar to the CTS benchmarks, the industry has for many years evaluated system performances on multimedia audio signals with broadcast news (BN) captioning. We have now achieved a new industry record of 6.5% and 5.9% on two BN benchmarks: RT04 and DEV04F [2]. Both these test sets have been released in the past by the Linguistic Data Consortium (LDC) [3].

I have an alarm set for 3 a.m. on Sunday morning, which is the earliest that we can start making restaurant reservations for our trip. There's even a spreadsheet with all the restaurants we want to visit, as meticulously researched from videos and handy guides from places like Disney Food Blog and WDW Prep School. So I was more than receptive when someone recommended that I check out TouringPlans.com-- a site that uses complex algorithms to help you plan the perfect vacation at Disney World, Disneyland, or a handful of other theme parks like Universal Studios Orlando. It's a premium service, with subscriptions priced starting at $16 per year. It has seven full-time employees on the staff, and 12 part-time employees, says Testa, as well as a business in publishing "Unofficial Guides" to Disney World, Disneyland, and other popular tourist destinations.

When the self-driving delivery vans finally arrive, it's going to be a challenge getting packages from the vehicle to the doorstep. That's where Ford's partnership with Digit – a bipedal robot from Agility Robotics – comes in handy. The main problem is that watching a two-legged robot like Digit can be creepy and disconcerting, especially when it awkwardly walks on its creature-like legs and then bends over – or when it sneakily unfolds itself from the back of the delivery van. Ford plans to use Digit to carry packages up to 40 pounds when humans aren't around to help grab deliveries from the back of a truck or van. Digit can walk up and down stairs, navigate around obstacles in its path, and stay balanced even after getting bumped. The humanoid can also fold up, so it stows away in the back of the delivery van ready to unfold and carry packages once the van arrives.

The BBC's chart mentions a margin of error It uses data generated by artificial intelligence (AI) -- specifically, machine learning -- and it's a good example of some of the challenges that journalists are increasingly going to face as they come to deal with more and more algorithmically-generated data. Information and decisions generated by AI are qualitatively different from the sort of data you might find in an official report, but journalists may fall back on treating data as inherently factual. Here, then, are some of the ways the article dealt with that -- and what else we can do as journalists to adapt. The story draws on data from an external organisation, Ceretai, which "uses machine learning to analyse diversity in popular culture." The organisation claims to have created an algorithm which "has learned to identify the difference between male and female voices in video and provides the speaking time lengths in seconds and percentages per gender."