ZHEJIANG – A Chinese factory is hatching giant inflatable chickens resembling Donald Trump to usher in the Year of the Rooster. The five-meter (16-foot) fowls sport the distinctive golden mane of the U.S. president-elect and mimic his signature hand gestures with their tiny wings. Cartoon figures of animals from the Chinese zodiac are ubiquitous around Chinese New Year at the end of this month. The balloon factory is selling its presidential birds for as much as 14,400 yuan ($2,080) on Chinese shopping site Taobao for a 10-meter version. "I saw his image on the news and he has a lot of personality, and since Year of the Rooster is coming up I mixed these two elements together to make a Chinese chicken," factory owner Wei Qing said.

Since the earliest days of the Internet, most government agencies have eagerly explored how to use technology to better deliver services to citizens, businesses and other public-sector organizations. Early on, observers recognized that these efforts often varied widely in their implementation, and so researchers developed various frameworks to describe the different stages of growth and development of e-government. While each model is different, they all identify the same general progression from the informational, for example websites that make government facts available online, to the interactive, such as two-way communication between government officials and users, to the transactional, like applications that allow users to access government services completely online. However, we will soon see a new stage of e-government: the perceptive. The defining feature of the perceptive stage will be that the work involved in interacting with government will be significantly reduced and automated for all parties involved.

Last week I went to the workshops at NIPS (biggest ML conference in the world) and I also attended part of the ML and the Law symposium the day before. I found out a little bit too late about the symposia but I was still able to attend two panels on which there were both lawyers and computer scientists. They were very insightful and informative -- did you know that this Spring, the European Union passed a regulation giving its citizens a "right to an explanation" for decisions made by machine-learning systems? The panel discussions were motivated by the problem of explaining ML-powered decisions which have an important impact on people's lives: We need to be able to test how systems get to their conclusions; if we can't test, we can't contest. Individuals are entitled to know which data is being processed of them, and to explanations of how predictions & decisions work, in terms they can understand.

Border control agencies are already using self-service kiosks to manage the crowds of international travelers entering their countries, but a high-tech type of kiosk in development can do more than just scan passports. The AVATAR--which stands for Automated Virtual Agent for Truth Assessments in Real-Time--can detect travelers trying to lie their way through customs, according to Vocativ. The self-service kiosks, created by the National Center for Border Security and Immigration at the University of Arizona in partnership with the Department of Homeland Security [PDF], scan travelers' passports and ask the kinds of questions posed by human agents, such as "Do you have any fruits or vegetables?" Sensors can identify body cues like facial expression, vocal tics, pupil dilation--and even cues that human agents can't see, like cardiorespiratory data--which could indicate that the person is lying and should be subject to additional screening. They can even see that you're curling your toes, according to a press statement from AVATAR researcher Aaron Elkins of San Diego State University, a professor who studies deception. The kiosks can be programmed to display several virtual agents, choosing from a woman or a man and a stern or a friendly face.

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.

Do you know key benefits of AI and appropriate changes? This article is based on the White House's report "Artificial intelligence, automation and the economy". The report notes that automation will keep making a profit, and will demand new abilities from the employees to succeed in the new world condition. New changes have to be available to each person, so policymakers need to help strengthen the best results of automation and decrease the risk. New technology allows reducing the time required to perform tasks and frees up employees to work on more difficult assignments.

In this paper, we develop a new sequential regression modeling approach for data streams. Data streams are commonly found around us, e.g in a retail enterprise sales data is continuously collected every day. A demand forecasting model is an important outcome from the data that needs to be continuously updated with the new incoming data. The main challenge in such modeling arises when there is a) high dimensional and sparsity, b) need for an adaptive use of prior knowledge, and/or c) structural changes in the system. The proposed approach addresses these challenges by incorporating an adaptive L1-penalty and inertia terms in the loss function, and thus called Inertial Regularization and Selection (IRS). The former term performs model selection to handle the first challenge while the latter is shown to address the last two challenges. A recursive estimation algorithm is developed, and shown to outperform the commonly used state-space models, such as Kalman Filters, in experimental studies and real data.

This paper presents an example of how demographical characteristics of patients influence their susceptibility to certain medical conditions. In this paper, we investigate the association of health conditions to age of patients in a heterogeneous population. We show that besides the symptoms a patients is having, the age has the potential of aiding the diagnostic process in hospitals. Working with Electronic Health Records (EHR), we show that medical conditions group into clusters that share distinctive population age densities. We use Electronic Health Records from Brazil for a period of 15 months from March of 2013 to July of 2014. The number of patients in the data is 1.7 million patients and the number of records is 47 million records. The findings have the potential of helping in a setting where an automated system undergoes the task of predicting the condition of a patient given their symptoms and demographical information.

In a variety of research areas, the weighted bag of vectors and the histogram are widely used descriptors for complex objects. Both can be expressed as discrete distributions. D2-clustering pursues the minimum total within-cluster variation for a set of discrete distributions subject to the Kantorovich-Wasserstein metric. D2-clustering has a severe scalability issue, the bottleneck being the computation of a centroid distribution, called Wasserstein barycenter, that minimizes its sum of squared distances to the cluster members. In this paper, we develop a modified Bregman ADMM approach for computing the approximate discrete Wasserstein barycenter of large clusters. In the case when the support points of the barycenters are unknown and have low cardinality, our method achieves high accuracy empirically at a much reduced computational cost. The strengths and weaknesses of our method and its alternatives are examined through experiments, and we recommend scenarios for their respective usage. Moreover, we develop both serial and parallelized versions of the algorithm. By experimenting with large-scale data, we demonstrate the computational efficiency of the new methods and investigate their convergence properties and numerical stability. The clustering results obtained on several datasets in different domains are highly competitive in comparison with some widely used methods in the corresponding areas.

Japanese auto major Nissan is working on autonomous driving cars and to that effect, it will use the Seamless Autonomous Mobility (SAM) system – derived from NASA-sourced technology – that utilises artificial intelligence to determine if human intervention is required in unusual and unexpected situations. Nissan believes NASA's technology built for the Mars Rover will help control fleets of autonomous vehicles. SAM is adapted from NASA's Visual Environment for Remote Virtual Exploration (VERVE), designed to supervise interplanetary robots like the Mars rovers. NASA scientists may use the system to chart out safe driving paths on other planets. Nissan uses the example of a police officer using hand signals at a traffic junction.