Artificial intelligence (AI) technology has the potential to boost productivity but increase wealth inequality and wipe out millions of jobs, a research report by the White House claimed on Tuesday. An increasing number of industries are set to be impacted by automation technology over the coming years which could displace jobs, a fear that has been voiced by academics and business leaders. Auto companies are developing driverless cars while factories could are seeing the increased use of robotics, which has the ability to eat into jobs. But many developments are at the early stage and the impact of automation technology could affect different industries are varying speeds. "Because AI is not a single technology, but rather a collection of technologies that are applied to specific tasks, the effects of AI will be felt unevenly through the economy. Some tasks will be more easily automated than others, and some jobs will be affected more than others--both negatively and positively," the White House report said.

Alex Tabarrok draws my attention to an article in the New York Times Magazine this weekend. It's about machine learning in general, but it starts out with this: Late one Friday night in early November, Jun Rekimoto, a distinguished professor of human-computer interaction at the University of Tokyo, was online preparing for a lecture when he began to notice some peculiar posts rolling in on social media. Apparently Google Translate, the company's popular machine-translation service, had suddenly and almost immeasurably improved. Rekimoto visited Translate himself and began to experiment with it. He had to go to sleep, but Translate refused to relax its grip on his imagination.

Imagine pulling into a Starbucks drive-thru and seeing not just your drink order but your name on the screen -- along with the suggestions of what foods you might like with your drink, automatically generated by the weather, your buying history, and the choices that others with similar preferences have made. Coming soon to a Starbucks drive-through near you -- and to your smartwatch, and possibly to each store's cash register -- are serving suggestions generated by artificial intelligence. It's all part of the coffee giant's plan to use AI and the cloud to drive sales and growth, as laid out in a 45-minute presentation this month at Starbucks Investor Day in Manhattan. "I hope you're convinced we're continuing to set the bar for digital in brick-and-mortar," said Matt Ryan, Starbucks chief strategy officer, after laying out the company's technology plans. It gives us the advantage we have moving forward." Seattle-based Starbucks is going strong -- growing steadily and last month ...

Or Meaningless New research offers insights into what gives work meaning -- as well as into common management mistakes that can leave employees feeling that their work is meaningless. GE's Big Bet on Data and Analytics This case study focuses on GE's "industrial internet" strategy. Aligning the Organization for Its Digital Future In this report, MIT Sloan Management Review and Deloitte explore how digitally savvy executives are aligning their people, processes, and culture with an eye toward long-term digital success. Data Sharing and Analytics Drive Success with IoT This MIT Sloan Management Review study concluded that obtaining business value from the internet of things depends on companies' willingness to share data with other organizations. Beyond the Hype: The Hard Work Behind Analytics Success This report by MIT Sloan Management Review and SAS found that few companies have a strategic plan for analytics or are executing a strategy for what they hope to achieve with analytics.

Would you have sex with a robot? Would a robot have the right to say no to such a union? These were just a few of the questions being asked at the second Love and Sex with Robots conference hastily rearranged at Goldsmiths University in London after the government in Malaysia - the original location - banned it. It has proved controversial, not only to countries with conservative views. There were no representatives from the sex industry in attendance and no sex robots on display, leading some to question the point of the event.

The report anticipates economic effects across several fronts. AI, like any new technology, is key to growth because it increases output without requiring increases in labor or capital. "In the last decade, despite technology's positive push, measured productivity growth has slowed in 30 of the 31 advanced economies, slowing in the United States from an average annual growth rate of 2.5% in the decade after 1995 to only 1.0% growth in the decade after 2005," the report states. Any increase in aggregate productivity from adopting artificial intelligence would be a welcomed change. But the resultant job automation is causing alarm.

This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary graph. Such structured sequences can represent series of frames in videos, spatio-temporal measurements on a network of sensors, or random walks on a vocabulary graph for natural language modeling. The proposed model combines convolutional neural networks (CNN) on graphs to identify spatial structures and RNN to find dynamic patterns. We study two possible architectures of GCRN, and apply the models to two practical problems: predicting moving MNIST data, and modeling natural language with the Penn Treebank dataset. Experiments show that exploiting simultaneously graph spatial and dynamic information about data can improve both precision and learning speed.

Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes should thus be modeled jointly in order to prevent the potential bias introduced by independent modelling. Commonly, joint models are estimated in likelihood based expectation maximization or Bayesian approaches using frameworks where variable selection is problematic and which do not immediately work for high-dimensional data. In this paper, we propose a boosting algorithm tackling these challenges by being able to simultaneously estimate predictors for joint models and automatically select the most influential variables even in high-dimensional data situations. We analyse the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry which collects longitudinal lung function data on patients with cystic fibrosis together with data regarding the onset of pulmonary infections. This is the first approach to combine state-of-the art algorithms from the field of machine-learning with the model class of joint models, providing a fully data-driven mechanism to select variables and predictor effects in a unified framework of boosting joint models.

This paper investigates a type of instability that is linked to the greedy policy improvement in approximated reinforcement learning. We show empirically that non-deterministic policy improvement can stabilize methods like LSPI by controlling the improvements' stochasticity. Additionally we show that a suitable representation of the value function also stabilizes the solution to some degree. The presented approach is simple and should also be easily transferable to more sophisticated algorithms like deep reinforcement learning.

Most of the existing studies on voice conversion (VC) are conducted in acoustically matched conditions between source and target signal. However, the robustness of VC methods in presence of mismatch remains unknown. In this paper, we report a comparative analysis of different VC techniques under mismatched conditions. The extensive experiments with five different VC techniques on CMU ARCTIC corpus suggest that performance of VC methods substantially degrades in noisy conditions. We have found that bilinear frequency warping with amplitude scaling (BLFWAS) outperforms other methods in most of the noisy conditions. We further explore the suitability of different speech enhancement techniques for robust conversion. The objective evaluation results indicate that spectral subtraction and log minimum mean square error (logMMSE) based speech enhancement techniques can be used to improve the performance in specific noisy conditions.