Technology is a key topic at the World Economic Forum's Annual Meeting 2017. Artificial Intelligence (AI) may be the single most disruptive technology the world has seen since the Industrial Revolution. Granted, there is a lot of hype out there on AI, along with doomsday headlines and scary movies. But the reality is that it will positively and materially change how we engage with the world around us. It's going to improve not only how business is done, but the kind of work we do – and unleash new levels of creativity and ingenuity.

A contest aimed at automating the detection of lung cancer shows how machine learning may be poised to overhaul medical imaging. The challenge offered $1 million in prizes for the algorithms that most accurately identified signs of lung cancer in low-dose computed tomography images. The winning algorithms won't necessarily be adopted by clinicians, but they could inspire algorithmic innovations that find their way into medical imaging. Low-dose CT scans have shown great potential in recent years for detecting lung cancer earlier. They use less radiation and do not require a contrast dye to be injected into the body.

Global travel has grown to become a $166 billion industry that powers over 190,000 businesses, while global tourism generates over $2 trillion in annual revenue, fueling more than 2 million businesses worldwide. For today's generation of mobile millennials, representing the most diverse demographic in U.S. history, traveling has become a popular alternative to obsessively scrolling social feeds and frequenting local hot spots. What curious young people crave more than making connections are memorable experiences, feeding a growing hunger for exploring different parts of the world to expand their perspective. A 2016 Topdeck Travel survey, pooling over 31,000 millennials from 13 different countries, revealed 88% traveled abroad between one and three times a year, with 30% choosing to travel alone. Additionally, 76% credited peer recommendations, social media, and targeted online ads as their primary drivers for making travel decisions.

Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities.

The words people use, the way they phrase things during conference calls, in reports or in regular conversations; all contain many of the quantitative keys that make Yin Luo such a highly respected analyst. "It's called natural language processing, using computer algorithms to, for example, analyze news stories" and draw conclusions, said Luo, vice chairman for quantitative research, economics and strategy at Wolfe Research, LLC. For example: A CEO has a conference call with analysts and says "I think" before offering insight. There is a distinct difference between beginning a sentence with "We think," which indicates a level of uncertainty seeking cover of the universal "we." "If they say'we think,' they are less certain," said Luo. This sort of analysis is the type of "alternative data" that excites Luo most about his job.

The technology titan is bullish on AI lately and that zeal is extending beyond its own product portfolio and into its investment strategy. Late last year, Microsoft Ventures invested in Element AI, an AI research lab and incubator based in Montreal, using a new fund earmarked for AI investments. The latest AI innovator to catch Microsoft's attention is Agolo, a New York City software maker that uses contextual AI to analyze content and distill it into summaries that deliver key points to users depending on their interests. On May 3, the startup announced it had raised $3.5 million in a round of financing led by Microsoft Ventures and CRV. Remarks from Nagraj Kashyap, corporate vice president at Microsoft Ventures, reveal how Agolo's technology aligns with Microsoft's focus on productivity technologies that help "empower every person and every organization on the planet to achieve more," according to the company's mission statement. "By applying its summarization system to news, chat, voice and video content, Agolo enables efficient consumption of large amounts of data, increasing productivity in the workplace," Kashyap wrote in a blog post.

Artificial intelligence is pushing into every area of our life with deep inroads to our mobile devices, our video games, and even our own brains. Video surveillance could the next step if NVIDIA's new video analytics platform, Metropolis, is successful. The initiative, announced just ahead of the annual GPU conference this week, will use learning AI to analyze the massive amount of data from surveillance video for "public safety, traffic management and resource optimization." According to NVIDIA, there are already hundreds of millions of surveillance cameras around the globe, with the number expected to rise to the 1 billion mark by 2020. Human beings have a hard time sifting through the flood of moving images, storing the majority of it on hard drives for later viewing.

NASA has revealed incredible'action cam' footage from a spacewalk on the International Space Station, giving an up-close look at what happens when astronauts step outside the orbiting craft. As the astronauts go about their tasks, it might at first be easy to forget that they are 250 miles above the surface – but, a quick change of perspective revealing the curve of Earth far below serves as a jolting reminder. During the spacewalk, the ESA's Thomas Pesquet and NASA's Shane Kimbrough prepared the Pressurized Mating Adapter-3 for a second International Docking Adapter, which will allow commercial crew vehicles to dock. In March, astronauts on the ISS began to make preparations for the installation of the second of two parking spots for space taxis, known as the International Docking Adapters. New crew spaceships, being designed by SpaceX and Boeing, will use them when they begin flying astronauts to the station in the coming years, as early as 2018.

A new working paper finds that the arrival of one new industrial robot in a local labor market coincides with an employment drop of 5.6 workers. These papers have not been peer-reviewed, but are circulated by their authors for comment and discussion. With the NBER's blessing, Making Sen$e is pleased to feature these summaries regularly on our page. The following summary was written by the NBER and doesn't necessarily reflect the views of Making Sen$e. With America's workers already squeezed by forces ranging from international competition to offshoring to new information technologies, concern is growing about the impact of robots on jobs and wages.

Editor's note: See also part 1 of 17 More Must-Know Data Science Interview Questions and Answers. Overfitting is when you build a predictive model that fits the data "too closely", so that it captures the random noise in the data rather than true patterns. As a result, the model predictions will be wrong when applied to new data. We frequently hear about studies that report unusual results (especially if you listen to Wait Wait Don't Tell Me), or see findings like "an orange used car is least likely to be a lemon", or learn that studies overturn previous established findings (eggs are no longer bad for you). Many such studies produce questionable results that cannot be repeated.