Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records, which is published alongside this paper. These data allow to reproducibly assess classification approaches, such as tree-based models and neural networks. According to our experiments with 20 base classes (multi-label classification), multi-layer perceptron models perform best with a f1-macro score of 0.760 closely followed by Long Short-Term Memory models (f1-macro score of 0.755). A possible application of the trained classification models is the quantitative analysis of trends towards interdisciplinarity of digital scholarly output or the characterization of growth patterns of research data, stratified by discipline of research. Both applications perform at scale with the proposed models which are available for re-use.

Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms. However, the communication cost of gradient aggregation and model synchronization between the master and worker nodes becomes the major obstacle for efficient learning as the number of workers and the dimension of the model increase. In this paper, we propose DORE, a DOuble REsidual compression stochastic gradient descent algorithm, to reduce over $95\%$ of the overall communication such that the obstacle can be immensely mitigated. Our theoretical analyses demonstrate that the proposed strategy has superior convergence properties for both strongly convex and nonconvex objective functions. The experimental results validate that DORE achieves the best communication efficiency while maintaining similar model accuracy and convergence speed in comparison with start-of-the-art baselines.

FISHDBC is a flexible, incremental, scalable, and hierarchical density-based clustering algorithm. It is flexible because it empowers users to work on arbitrary data, skipping the feature extraction step that usually transforms raw data in numeric arrays letting users define an arbitrary distance function instead. It is incremental and scalable: it avoids the $\mathcal O(n^2)$ performance of other approaches in non-metric spaces and requires only lightweight computation to update the clustering when few items are added. It is hierarchical: it produces a "flat" clustering which can be expanded to a tree structure, so that users can group and/or divide clusters in sub- or super-clusters when data exploration requires so. It is density-based and approximates HDBSCAN*, an evolution of DBSCAN.

In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with collaborative filtering, and interacts group information with users/items for bridging similar users/items. Therefore, a user's preference over an unobserved item, in DBRec, can be bridged by the users within the same group who have rated the item, or the user-rated items that share the same group with the unobserved item. In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations. We jointly integrate collaborative filtering, latent group discovering and hierarchical modelling into a unified framework, so that all the model parameters can be learned toward the optimization of the objective function. We validate the effectiveness of the proposed model with two real datasets, and demonstrate its advantage over the state-of-the-art recommendation models with extensive experiments.

Across the world, governments are investing in machines that they hope will run their social security systems and other services more cheaply and effectively than humans. The Guardian's Automating Poverty series includes reports from the US, Australia and India as well as the UK. The roles played by technology in these countries are all different. But taken together, the articles reveal how automation, machine learning and artificial intelligence are extending their reach into people's lives through the delivery of public services. As with all automation processes, speed and efficiency provide the rationale.

Employees have more trust in robots than they do their human managers, a global study has revealed. A survey across 10 countries have found that 64 percent prefer to seek advice or guidance from artificial intelligence over their boss and 82 percent feels it does a better job. The majority of workers are also optimistic, excited and grateful about having robot co-workers and nearly a quarter reported having a loving and gratifying relationship with the intelligent-style software. The study was conducted by the US technology company Oracle and research firm Future Workplace. The team surveyed 8,370 employees, managers and HR leaders and'found that AI has changed the relationship between people and technology at work and is reshaping the role HR teams and managers need to play in attracting, retaining and developing talent.'

Workers in India (89%) and China (88%) are more trusting of robots over their managers, followed by Singapore (83%), Brazil (78%), Japan (76%), UAE (74%), Australia/New Zealand (58%), the U.S. (57%), the U.K. (54%), and France (56%). More men (56%) than women (44%) have turned to AI over their managers.

AI has continued to rapidly advance. With intelligent tools now available to businesses, how can enterprises transform their processes and insights using the latest AI techniques? If your business doesn't have an AI roadmap, it will suffer significant losses and damage your ability to compete. This is the stark message that many industry watchers are defining, as we enter a new era of AI. The headlines in the technology press that shout that AI is the next seismic shift in how your business will operate, need to be tempered with a reality check.

Conferencing solution company Zoom announced a slew of new features this week at its Zoomtopia 2019 conference in San Jose, California -- 300 in total, to be exact. Among the highlights are AI-powered transcripts and meeting notes in Zoom Meetings, in addition to a Zoom Rooms people counter informed by facial recognition. On the Zoom Meetings side, live transcripts tap startup Otter.ai's Now, attendees can take notes directly in the Zoom interface or use live transcription for voice note taking, the latter of which is parsed by algorithms to derive action items automatically in the Meeting Timelines interface. Furthermore, meeting hosts can now bring their own interpreter with a mutli-channel audio experience that mixes the original and interpreter audio, enabling listeners to understand the interpreter while hearing the original speaker's tone.

Fledgling gal-bots are the latest hires in the virtual assistant landscape. Meet Mia and Marge: two virtual assistants in the banking world – each brought into existence by women, both of whom carry deep institutional knowledge, subject matter expertise and long-standing credibility. UBank's Lee Hatton (Mia) and The Royal Bank of Scotland's (RBS) MaryAnn Fleming (Marge) are among 40 women who have been recognized as 2019's women leaders in A.I. by IBM. These leaders have succeeded in garnering acceptance of A.I. in the workplace, elevating their customers' experience and their companies' brands. It seems mortgage consumer complaints consistently surface around the loan application process according to UBank CEO, Lee Hatton.