In May, a video appeared on the internet of Donald Trump offering advice to the people of Belgium on the issue of climate change. "As you know, I had the balls to withdraw from the Paris climate agreement," he said, looking directly into the camera, "and so should you." The video was created by a Belgian political party, Socialistische Partij Anders, or sp.a, and posted on sp.a's Twitter and Facebook. It provoked hundreds of comments, many expressing outrage that the American president would dare weigh in on Belgium's climate policy. One woman wrote: "Humpy Trump needs to look at his own country with his deranged child killers who just end up with the heaviest weapons in schools."

Every week, we talk about important data and analytics topics with data science leaders from around the world on Facebook Live. You can subscribe to the DataTalk podcast on iTunes, Google Play, Stitcher, SoundCloud and Spotify. This data science video and podcast series is part of Experian's effort to help people understand how data-powered decisions can help organizations develop innovative solutions and drive more business. To keep up with upcoming events, join our Data Science Community on Facebook or check out the archive of recent data science videos. To suggest future data science topics or guests, please contact Mike Delgado. In this week's #DataTalk, we talked with Kristen Hammilton, CEO & Founder of Koru, about the future of recruitment using predictive hiring. This is going to be a very exciting chat because we're talking about AI and how it's helping recruiters find the right candidates for jobs, and it's actually a fascinating field. Has to do with prediction, has to do with artificial intelligence, and I'm very excited to talk to Kristen Hamilton, who is the CEO and co-founder of a company called Koru, based in Seattle, and Kristen, great to have you on our show today. Kristen: Michael, it's really great to be here, thanks. Mike: So Kristen, can you kind of tell us about your journey that brought you into working in data science? I think every entrepreneurial journey starts with a problem that you've become impassioned with, that you really can't sleep well at night until you figure out how to solve. And the challenge that we initially started facing was actually the gap between education and employment.

We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. filtering. The algorithm is derived from the variational objective in the filtering setting and consists of an optimization procedure at each time step. By performing each inference optimization procedure with an iterative amortized inference model, we obtain a computationally efficient implementation of the algorithm, which we call amortized variational filtering. We present experiments demonstrating that this general-purpose method improves performance across several deep dynamical latent variable models.

This paper presents a novel approach for synthesizing facial affect; either categorical, in terms of the six basic expressions (i.e., anger, disgust, fear, happiness, sadness and surprise), or dimensional, in terms of valence (i.e., how positive or negative is an emotion) and arousal (i.e., power of the emotion activation). In the Valence-Arousal case, a system is created, based on VA annotation of 600,000 frames from the 4DFAB database; in the categorical case, the system is based on the selection of apex frames of posed expression sequences from the 4DFAB. The proposed system accepts at its input: i) either the basic facial expression, or the pair of valence-arousal emotional state descriptors, which need to be synthesized and ii) a neutral 2D image of a person on which the corresponding affect will be synthesized. The proposed approach consists of the following steps: First, based on the provided desired emotional state, a set of 3D facial meshes is produced from the 4DFAB database and is used to build a blendshape model that generates the new facial affect. To synthesize this affect on the 2D neutral image, 3D Morphable Models fitting is performed and the reconstructed face is then deformed to generate the target facial expressions. Finally, the new face is rendered into the original image. Qualitative experimental studies illustrate the generation of realistic images, when the neutral image is sampled from a variety of well known lab-controlled or in-the-wild databases, including Aff-Wild, RECOLA, AffectNet, AFEW, Multi-PIE, AFEW-VA, BU-3DFE, Bosphorus, RAF-DB. Also, quantitative experiments are conducted, in which deep neural networks, trained using the generated images from each of the above databases in a data-augmentation framework, provide affect recognition; better performances are achieved through the presented approach when compared with the current state-of-the-art.

The use of language models for generating lyrics and poetry has received an increased interest in the last few years. They pose a unique challenge relative to standard natural language problems, as their ultimate purpose is reative, notions of accuracy and reproducibility are secondary to notions of lyricism, structure, and diversity. In this creative setting, traditional quantitative measures for natural language problems, such as BLEU scores, prove inadequate: a high-scoring model may either fail to produce output respecting the desired structure (e.g. song verses), be a terribly boring creative companion, or both. In this work we propose a mechanism for combining two separately trained language models into a framework that is able to produce output respecting the desired song structure, while providing a richness and diversity of vocabulary that renders it more creatively appealing.

In September 16, 2011, an anime fan posted a math question to the online bulletin board 4chan about the cult classic television series The Melancholy of Haruhi Suzumiya. Season one of the show, which involves time travel, had originally aired in nonchronological order, and a re-broadcast and a DVD version had each further rearranged the episodes. Fans were arguing online about the best order to watch the episodes, and the 4chan poster wondered: If viewers wanted to see the series in every possible order, what is the shortest list of episodes they'd have to watch? Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. In less than an hour, an anonymous person offered an answer -- not a complete solution, but a lower bound on the number of episodes required.

Humanity at a Crossroads--Artificial Intelligence is one of the most intriguing topics today, filled with various arguments and views on whether it's a blessing or a threat to humanity. We might be at the crossroads, but what if AI itself is already crossing the line? If we look at "I, Robot," a sci-fi film that takes place in Chicago circa 2035, highly intelligent robots powered by artificial intelligence fill public service positions and have taken over all the menial jobs, including garbage collection, cooking, and even dog walking throughout the world. The movie came out in 2004 starring Will Smith as Detective Del Spooner who eventually discovers a conspiracy in which AI-powered robots may enslave and hurt the human race. Stephen Hawking, famed physicist, also once said: "Success in creating effective AI could be the biggest event in the history of our civilization. So we can't know for sure if we'll be infinitely helped by AI, or ignored by it and side-lined, or conceivably destroyed by it."

Although it helps to have powerful image-capturing hardware, it is not the be-all-end-all requirement for getting high-quality pictures. In addition to having a good image-capturing capability, cameras are required to have excellent image processing abilities as well. The software has always been an important part of digital cameras. For instance, it is the software that gives digital cameras their'zoom' factor. Using complex algorithms can minimize the need for dual lenses that are featured on most new phones.

China's Xinhua news agency has become the first to receive a virtual news anchor for its TV channel, and it looks quite real. The news anchor is a male that has been modeled after one of the male news anchors at Xinhua news agency. And the virtual anchor can read the the news as text entered by the news agency. The virtual anchor has human like facial expressions, and reads the news in a synthesized voice that makes it more plausible than anything before. This news anchor was created by the Xinhua news agency of China in collaboration with the Chinese search engine company Sogou.

NEW DELHI: China's state press agency has unveiled a virtual newsreader designed to deliver headlines 24 hours a day. Xinhua's "artificial intelligence news anchor" is a lifelike digitised reporter which can read out text by mimicking the image and voice of a real human presenter. The agency claims the virtual presenter -- a realistic looking man, sharply dressed in a suit -- "can read texts as naturally as a professional news anchor". China's state press agency has unveiled a virtual newsreader designed to deliver headlines 24 hours a day. Xinhua's "artificial intelligence news anchor" is a lifelike digitised reporter which can read out text by mimicking the image and voice of a real human presenter.