The new world order taking shape is likely to be more complex than a simple bi-polar structure, especially since data is being produced at a pace that boggles the mind. For one, we recognize that the digital trace that is generated by computers around the world spans a very wide range of activities, from sending an SMS text message to making a financial transaction. That said, we acknowledge that in the near-term there could be some countries – China being the pre-eminent example – where data-sharing between public and private sector agencies with very little mobility beyond the national borders could violate privacy and openness norms and yet yield a temporary advantage in training algorithms inside a "walled garden." If one were to take the point of view that the biggest and highest impact AI applications are the ones that serve the greatest public purpose, access to data is key. While the U.S. scores well on all three criteria – and this might seem counter-intuitive to prevailing wisdom -- China operates with a handicap if global accessibility of the data is considered essential for creating successful AI applications in the future.

Process Management in the Era of Work from Home is all about "Deskless" workers, distributed all over in various time zones doing work in concert with each other and potentially robots and bots. The future is now for the future of work. Almost overnight, millions of us are working from home, across the globe, doing our jobs, being good social citizens, and embracing hardship and navigating the current circumstances with skills, will, and a bit of serenity. We are in the middle of a massive tidal shift of deskless and distributed employees working from home. Employers have to navigate several minefields to make work happen from home.

It has already been established that digital traces can be used to predict various human attributes. In most cases, however, predictive models rely on features that are specific to a particular source of digital trace data. In contrast, short texts written by users $-$ tweets, posts, or comments $-$ are ubiquitous across multiple platforms. In this paper, we explore the predictive power of short texts with respect to the academic performance of their authors. We use data from a representative panel of Russian students that includes information about their educational outcomes and activity on a popular networking site, VK. We build a model to predict academic performance from users' posts on VK and then apply it to a different context. In particular, we show that the model could reproduce rankings of schools and universities from the posts of their students on social media. We also find that the same model could predict academic performance from tweets as well as from VK posts. The generalizability of a model trained on a relatively small data set could be explained by the use of continuous word representations trained on a much larger corpus of social media posts. This also allows for greater interpretability of model predictions.

Imagine sending a text message to a friend. As your fingers tap the keypad, words and the occasional emoji appear on the screen. Perhaps you write, "I feel blessed to have such good friends:)" Every character conveys your intended meaning and emotion. But other information is hiding among your words, and companies eavesdropping on your conversations are eager to collect it. Every day, they use artificial intelligence to extract hidden meaning from your messages, such as whether you are depressed or diabetic.

Which countries are the top data producers? After all, with data-fueled applications of artificial intelligence projected, by McKinsey, to generate $13 trillion in new global economic activity by 2030, this could determine the next world order, much like the role that oil production has played in creating economic power players in the preceding century. While China and the U.S. could emerge as two AI superpowers, data sources can't be limited to concentrations in a few places as we have with an oil-driven economy -- it needs to be drawn from many, diverse sources and future AI applications will emerge from new and unexpected players. The new world order taking shape is likely to be more complex than a simple bi-polar structure, especially since data is being produced at a pace that boggles the mind. Building on our past work mapping the digital evolution and digital competitiveness of different countries around the world, we wanted to try to locate the deepest and widest pools of useful data. This is essential to run the myriad machine learning models critical to AI.

With the emergence of connected devices (e.g., smartphones and smartmeters), pervasive systems generate growing amounts of digital traces as users undergo their everyday activities. These traces are crucial to service providers to understand their customers, to increase the degree of personalization, and enhance the quality of their services. For instance, personal digital traces stemming from public transit smartcards help transportation providers understand the commuting patterns of users; the usage statistics of home appliances can be used to improve energy efficiency; on-street cameras provide police officers with new ways of investigating crimes; content generated through mobile and wearables (such as posts in online social media or GPS running routes in specialized websites such as those for fitness) can be used to provide tailored content to individuals; bank transaction logs can be used to spot unusual activity in accounts. However, sharing these digital traces generated by pervasive systems with service providers might raise concerns with regards to privacy. Indeed, the processing and analysis of these digital traces can surface latent information about the behavior of the users. While service providers have to store the usergenerated data in large databases that guarantee a certain level of privacy (e.g., from storing the traces in an anonymized manner using randomly-generated identifiers instead of the real user's name and surname to using more sophisticated privacy-preserving techniques such as differential privacy), third parties such as advertisers that have access to the traces can leverage machine learning techniques to reveal personal information about the users and expose their privacy [1]. This includes inferring personal information about users and identifying a single individual from a collection of user-generated traces. Moreover, these traces might reveal information about the significant places routinely visited by the user, enabling the service provider to infer a wide range of personal information, including the user's place of residence and work and their future locations. To a further extent, presence traces can also be used to identify a specific individual in a population.

In this article, we present a method for predicting the view count of a YouTube video using a small feature set collected from a synchronous sharing tool. We hypothesize that videos which have a high YouTube view count will exhibit a unique sharing pattern when shared in synchronous environments. Using a one-day sample of 2,188 dyadic sessions from the Yahoo! Zync synchronous sharing tool, we demonstrate how to predict the video's view count on YouTube, specifically if a video has over 10 million views. The prediction model is 95.8% accurate and done with a relatively small training set; only 15% of the videos had more than one session viewing; in effect, the classifier had a precision of 76.4% and a recall of 81%. We describe a prediction model that relies on using implicit social shared viewing behavior such as how many times a video was paused, rewound, or fast-forwarded as well as the duration of the session. Finally, we present some new directions for future virality research and for the design of future social media tools.