Facial recognition technology was introduced in the 1960s, languished through the AI winter, and in recent years has taken off -- boosted by increasingly powerful deep neural networks. Facial recognition has been applied in Face ID device unlocking functions, public security services, smart payment systems and more. During Taylor Swift's 2018 "Reputation" tour, the American singer-songwriter's security team utilized the tech to safeguard her from stalkers. Now, a research team from the Hong Kong University of Science and Technology and Harbin Engineering University has adopted facial recognition technology to analyze students' emotions in the classroom through a visual analytics system called "EmotionCues." Paper co-author Huamin Qu says the system "provides teachers with a quick and convenient measure of students' engagement level in a class. Knowing whether the lectures are too hard and when students get bored can help improve teaching."

It goes without saying that blogging has slowly and steadily evolved into an indispensable marketing tool. While marketers have been continually using the best possible strategies to improve the existing global blogging landscape, the inclusion of artificial Intelligence has taken the ballgame to a whole different level. Moreover, blogging is one marketing activity that needs to be initiated flawlessly for massive returns and any deviation from the perfect path can readily diminish the gains and profits. For someone looking to start his or her own blog, speed, scalability, and consistency matter the most. Moreover, blogging shouldn't be about random topics but a process that needs intelligent execution to produce remarkable results. Artificial Intelligence, therefore, is essential to the fate of modern-day bloggers as it offers a sense of proactivity to the proceedings besides making room for hard-hitting insights pertaining to creative content creation.

Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public. In this paper, we consider the existence of confounding variables in the features of fake news and use Propensity Score Matching (PSM) to select generalizable features in order to reduce the effects of the confounding variables. Experimental results show that the generalizability of fake news method is significantly better by using PSM than using raw frequency to select features. We investigate multiple types of fake news methods (classifiers) such as logistic regression, random forests, and support vector machines. We have consistent observations of performance improvement.

Nowadays, Internet is a primary source of attaining health information. Massive fake health news which is spreading over the Internet, has become a severe threat to public health. Numerous studies and research works have been done in fake news detection domain, however, few of them are designed to cope with the challenges in health news. For instance, the development of explainable is required for fake health news detection. To mitigate these problems, we construct a comprehensive repository, FakeHealth, which includes news contents with rich features, news reviews with detailed explanations, social engagements and a user-user social network. Moreover, exploratory analyses are conducted to understand the characteristics of the datasets, analyze useful patterns and validate the quality of the datasets for health fake news detection. We also discuss the novel and potential future research directions for the health fake news detection.

Millions of news articles are published online every day, which can be overwhelming for readers to follow. Grouping articles that are reporting the same event into news stories is a common way of assisting readers in their news consumption. However, it remains a challenging research problem to efficiently and effectively generate a representative headline for each story. Automatic summarization of a document set has been studied for decades, while few studies have focused on generating representative headlines for a set of articles. Unlike summaries, which aim to capture most information with least redundancy, headlines aim to capture information jointly shared by the story articles in short length, and exclude information that is too specific to each individual article. In this work, we study the problem of generating representative headlines for news stories. We develop a distant supervision approach to train large-scale generation models without any human annotation. This approach centers on two technical components. First, we propose a multi-level pre-training framework that incorporates massive unlabeled corpus with different quality-vs.-quantity balance at different levels. We show that models trained within this framework outperform those trained with pure human curated corpus. Second, we propose a novel self-voting-based article attention layer to extract salient information shared by multiple articles. We show that models that incorporate this layer are robust to potential noises in news stories and outperform existing baselines with or without noises. We can further enhance our model by incorporating human labels, and we show our distant supervision approach significantly reduces the demand on labeled data.

The "Hey, Update My Voice" movement, in partnership with UNESCO, was born out of this context with the goal of teaching respect towards virtual assistants and, in addition, asking tech companies to update their assistants' responses. Because if that happens to them, imagine what happens in real life to real women. Every day around the world, virtual assistants suffer abuse and harassment of all kinds. In Brazil, for example, Lu, the virtual assistant of Magazine Luiza stores, has been victimized by this sort of violence. Worldwide, cases have been reported involving Siri and Alexa, among others.

Over the past few years, the Canada Revenue Agency has been using data analytics and AI, such as machine-learning algorithms that predict tax non-compliance and detect activity in the underground economy. Since 2018, the Department of Justice Canada has licensed the use of Tax Foresight, AI software developed by Blue J Legal Inc. in Toronto, which employs machine learning to predict – with about 90% accuracy, according to the company – how a court might rule on a particular tax scenario. "It's not just about speeding up [analysis] that would otherwise happen," says Benjamin Alarie, co-founder and CEO of Blue J Legal and Osler Chair of Business Law at the University of Toronto. "It's about making [widely] available a really good prediction that would otherwise be the domain of an experienced [lawyer]." AI technology could bring more certainty to the interpretation of tax law, Alarie adds: "Everyone benefits from that."

The term "Artificial Intelligence" conjures, in many, an image of an anthropomorphized Terminator-esque killer robot apocalypse. Hollywood movies, in recent decades, have served to only further this notion. Physicists and moral philosophers like Max Tegmark and Sam Harris, however, claim we need not fear a runaway superintelligence to adequately worry about the deleterious effects endemic to the AI space, but rather that competence on behalf of machines is a sufficiently frightening springboard from which an irreversibly harmful future can be launched. That said, there are currently a number of far more nefarious, insidious, and relevant ethical dilemmas which warrant our attention. In a world increasingly controlled by automated processes, rapidly approaching is a time in which adaptive, self-improving algorithms guide or even dictate most of the decisions that define human experience.

UPDATE Oct, 2019: We just added a new season with 4 new podcasts focused on artificial intelligence, machine learning, data science, and data orchestration. Building a data foundation is essential to driving innovation. This is just as true for mid-market companies as for large enterprise companies. Mid-market and large enterprise companies have different challenges, so we've brought together experts from each size company to discuss key trends that are reshaping the way successful companies use their data: from data management and data foundation to spatial and machine learning to data-based process and information excellence. Listen to this chat series on all things data!

Marketplace is the home for Spotify's music industry products, such as Spotify for Artists, Spotify Label Analytics and Spotify Publisher Analytics. The Marketplace Creator Product Insights Team is a thriving insights community of 20 Data Scientists and User Researchers who blend their skills to help us shape the future of the Creator Marketplace by defining the next generation of music industry products. We are looking for a Data Scientists – Associate to Senior level to help us continue to develop our music industry products, and develop best-in-class expression and promotion tools for Creators and their teams by exploring the following questions: How do we help artists stand out? How do we help them grow and reach new audiences? How do we help them connect with their fans? Our mission is to turn terabytes of data into insights and get a deep understanding of the world of music and their creators.