Digital life is augmenting human capacities and disrupting eons-old human activities. Code-driven systems have spread to more than half of the world's inhabitants in ambient information and connectivity, offering previously unimagined opportunities and unprecedented threats. As emerging algorithm-driven artificial intelligence (AI) continues to spread, will people be better off than they are today? Some 979 technology pioneers, innovators, developers, business and policy leaders, researchers and activists answered this question in a canvassing of experts conducted in the summer of 2018. The experts predicted networked artificial intelligence will amplify human effectiveness but also threaten human autonomy, agency and capabilities. They spoke of the wide-ranging possibilities; that computers might match or even exceed human intelligence and capabilities on tasks such as complex decision-making, reasoning and learning, sophisticated analytics and pattern recognition, visual acuity, speech recognition and language translation. They said "smart" systems in communities, in vehicles, in buildings and utilities, on farms and in business processes will save time, money and lives and offer opportunities for individuals to enjoy a more-customized future. Many focused their optimistic remarks on health care and the many possible applications of AI in diagnosing and treating patients or helping senior citizens live fuller and healthier lives.

This week in Princeton, New Jersey, Siemens brought together several hundred technologists, from inside the company to partners and media, to share some expected updates about the accelerated evolution of their work in artificial intelligence (AI) and the "rise of autonomous systems." After nearly 200 years in existence, and several decades of developing everything from smart factory robotics (starting in their own manufacturing facilities) to intelligent machines and connected manufacturing systems, the company has most recently been reaching out to build tech ecosystems with an open embrace, while also working more intensely with universities, government agencies, start-ups, and entrepreneurs. Siemens has long been considered one of the world's top companies in industrial and B2B technologies and has been developing software before the world understood what software was and would mean, long before Industry 4.0 became "a thing." The thing is, while Siemens is one of the world's top ten largest software companies, it is under-recognized for its work in Industrial IoT and the software, systems and networks that make manufacturing more efficient and companies more profitable. The Siemens team, including a number of incredibly bright and passionate engineering experts, recent PhDs, data scientists and industry experts, enthusiastically shared demos across digital remote services using VR glasses and computer vision software, an "Ag Pod" developed to address the global food shortage, voice control car tech, autonomous power restoration for energy grids, and 3-D printing for the "on-demand" economy.

Elon Musk is worried about the perils of artificial intelligence. The year is 2030, and artificial intelligence has changed practically everything. Is it a change for the better or has AI threatened what it means to be human, to be productive and to exercise free will? You've heard the dire predictions from some of the brightest minds about AI's impact. Tesla and SpaceX chief Elon Musk worries that AI is far more dangerous than nuclear weapons.

Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio. In this chapter, we present neural network architectures that are able to learn efficient representations of molecules and materials. In particular, the continuous-filter convolutional network SchNet accurately predicts chemical properties across compositional and configurational space on a variety of datasets. Beyond that, we analyze the obtained representations to find evidence that their spatial and chemical properties agree with chemical intuition.

With the emergence of the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services on October 1, 2012, forecasting unplanned patient readmission risk became crucial to the healthcare domain. There are tangible works in the literature emphasizing on developing readmission risk prediction models; However, the models are not accurate enough to be deployed in an actual clinical setting. Our study considers patient readmission risk as the objective for optimization and develops a useful risk prediction model to address unplanned readmissions. Furthermore, Genetic Algorithm and Greedy Ensemble is used to optimize the developed model constraints.

This paper proposes a model of information cascades as directed spanning trees (DSTs) over observed documents. In addition, we propose a contrastive training procedure that exploits partial temporal ordering of node infections in lieu of labeled training links. This combination of model and unsupervised training makes it possible to improve on models that use infection times alone and to exploit arbitrary features of the nodes and of the text content of messages in information cascades. With only basic node and time lag features similar to previous models, the DST model achieves performance with unsupervised training comparable to strong baselines on a blog network inference task. Unsupervised training with additional content features achieves significantly better results, reaching half the accuracy of a fully supervised model.

Senior Technical Fellow PeopleTec, Inc. Huntsville, AL, USA ABSTRACT For predictive maintenance, we examine one of the largest public datasets for machine failures derived along with their corresponding precursors as error rates, historical part replacements and sensor inputs. To simplify the timeaccuracy comparisonbetween 27 different algorithms, we treat the imbalance between normal and failing states with nominal under-sampling. We identify 3 promising regression and discriminant algorithms with both higher accuracy (96%) and twenty-fold faster execution times than previous work. Because predictive maintenance success hinges on input features prior to prediction, we provide a methodology to rank-order feature importance and show that for this dataset, error counts prove more predictive than scheduled maintenance might imply solely based on more traditional factors such as machine age or last replacement times. INTRODUCTION Successful predictive maintenance is challenging not only because failures can prove multifactorial but also because maintenance forecasters often lack good training data.

In 2016, the majority of full-time employed women in the U.S. earned significantly less than comparable men. The extent to which women were affected by gender inequality in earnings, however, depended greatly on socio-economic characteristics, such as marital status or educational attainment. In this paper, we analyzed data from the 2016 American Community Survey using a high-dimensional wage regression and applying double lasso to quantify heterogeneity in the gender wage gap. We found that the gap varied substantially across women and was driven primarily by marital status, having children at home, race, occupation, industry, and educational attainment. We recommend that policy makers use these insights to design policies that will reduce discrimination and unequal pay more effectively.

The question addressed in this paper is: If we present to a user an AI system that explains how it works, how do we know whether the explanation works and the user has achieved a pragmatic understanding of the AI? In other words, how do we know that an explanainable AI system (XAI) is any good? Our focus is on the key concepts of measurement. We discuss specific methods for evaluating: (1) the goodness of explanations, (2) whether users are satisfied by explanations, (3) how well users understand the AI systems, (4) how curiosity motivates the search for explanations, (5) whether the user's trust and reliance on the AI are appropriate, and finally, (6) how the human-XAI work system performs. The recommendations we present derive from our integration of extensive research literatures and our own psychometric evaluations.

Trump's election ushered in a political winter doomed to last at least four years, assuming he escapes impeachment. Since then, creatives in virtually every industry have responded by turning Trump's inflammatory soundbites into kindling for the artistic fire. TV shows such as Netflix's Dear White People and The Handmaid's Tale have played on the anxieties induced by the barely veiled misogyny and racism in his rhetoric. In cinema we see films such as BlacKkKlansman, Battle of the Sexes and The Post capturing the tension of the era with prescience, given their long production cycles. Resistance politics has also erupted off the screen in the #MeToo movement.