My wife is a physician, so we often find ourselves in social engagements with other physicians. When asked what I do, many are intrigued by what it means to be a computer science professor. My graduate students do that now. Do you teach people how to program? No, though some of my colleagues do.

We first adopt a human-guided machine learning framework to develop a new dataset for hyperpartisan news title detection with 2,200 manually labeled and 1.8 million machine-labeled titles that were posted from 2014 to the present by nine representative media organizations across three media bias groups - Left, Central, and Right in an active learning manner. The fine-tuned transformer-based language model achieves an overall accuracy of 0.84 and an F1 score of 0.78 on an external validation set. Next, we conduct a computational analysis to quantify the extent and dynamics of partisanship in news titles. While some aspects are as expected, our study reveals new or nuanced differences between the three media groups. We find that overall the Right media tends to use proportionally more hyperpartisan titles. Roughly around the 2016 Presidential Election, the proportions of hyperpartisan titles increased in all media bias groups where the relative increase in the proportion of hyperpartisan titles of the Left media was the most. We identify three major topics including foreign issues, political systems, and societal issues that are suggestive of hyperpartisanship in news titles using logistic regression models and the Shapley values. Through an analysis of the topic distribution, we find that societal issues gradually receive more attention from all media groups. We further apply a lexicon-based language analysis tool to the titles of each topic and quantify the linguistic distance between any pairs of the three media groups. Three distinct patterns are discovered. The Left media is linguistically more different from Central and Right in terms of foreign issues. The linguistic distance between the three media groups becomes smaller over recent years. In addition, a seasonal pattern where linguistic difference is associated with elections is observed for societal issues.

There are, largely speaking, two camps when it comes to artificial intelligence. On one hand, there's those who are wary of the negatives that have been reported on in the press and worry that the future isn't as bright as we'd hoped for. Then there's those who see the remarkable opportunity it offers and are working hard to mine its advantages. Undeniably, AI will shape society, it's unavoidable; history's not exactly littered with examples of technological advancements that have simply been rejected. While the tightrope of personal intrusion needs to be very carefully walked, it would be remiss of us to reject its benefits outright.

The health of people and communities around the world has been improving over time. For example, the steep decline in child and maternal mortality is a key indicator of positive momentum. However, progress has not been equal across the globe, and there is a great need to focus on societal issues such as reducing health inequity and improving access to care for underserved populations. While researchers work to unlock life-saving discoveries and develop new approaches to pressing health issues, advancements in technology can help accelerate and scale new solutions. That is why we are launching AI for Health, a new $40 million, five-year program to empower researchers and organizations with AI to improve the health of people and communities around the world.

You've likely already encountered artificial intelligence several times today. But for most people, the term AI still conjures images of The Terminator. We don't need to worry about hulking armed robots terrorizing American cities, but there are serious ethical and societal issues we must confront quickly -- because the next wave of computing power is coming, with the potential to dramatically alter -- and improve -- the human experience. Full disclosure: I am general counsel and chair of the AI Ethics Working Group at a company that is bringing AI to processor technology in trillions of devices to make them smarter and more trustworthy. Enabled by high-speed wireless capacity and rapid advances in machine learning, new applications for artificial intelligence are created every day. But for the rest of us, we're right to ask a few questions.