Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering gained popularity due to the non-linearity of neural networks, which allows for flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on synthetic data, the MNIST benchmark data set, and a challenging real-world task of defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines and we show that the MoE architecture in the decoder reduces the computational cost of sampling specific data modes with high fidelity.

Among the six challenges of neural machine translation (NMT) coined by ( Koehn and Knowles, 2017), rare-word problem is considered the most severe one, especially in translation of low-resource languages. In this paper, we propose three solutions to address the rare words in neural machine translation systems. First, we enhance source context to predict the target words by connecting directly the source embeddings to the output of the attention component in NMT. Second, we propose an algorithm to learn morphology of unknown words for English in supervised way in order to minimize the adverse effect of rare-word problem. Finally, we exploit synonymous relation from the W ordNet to overcome out-of-vocabulary (OOV) problem of NMT. W e evaluate our approaches on two low-resource language pairs: English-Vietnamese and Japanese-Vietnamese. In our experiments, we have achieved significant improvements of up to roughly 1.0 BLEU points in both language pairs.

Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand. However, most existing automatic taxonomy construction methods can only build hypernymy taxonomies wherein each edge is limited to expressing the "is-a" relation. Such a restriction limits their applicability to more diverse real-world tasks where the parent-child may carry different relations. In this paper, we aim to construct a task-guided taxonomy from a domain-specific corpus and allow users to input a "seed" taxonomy, serving as the task guidance. We propose an expansion-based taxonomy construction framework, namely HiExpan, which automatically generates key term list from the corpus and iteratively grows the seed taxonomy. Specifically, HiExpan views all children under each taxonomy node forming a coherent set and builds the taxonomy by recursively expanding all these sets. Furthermore, HiExpan incorporates a weakly-supervised relation extraction module to extract the initial children of a newly-expanded node and adjusts the taxonomy tree by optimizing its global structure. Our experiments on three real datasets from different domains demonstrate the effectiveness of HiExpan for building task-guided taxonomies.

Domino's Pizza stores in Australia and New Zealand have finally begun using an elaborate new employee monitoring tool to track employee performance. First announced in 2017, the DOM Pizza Checker was finally implemented at a number of Domino's stores in Oceania beginning this August, according to an investor presentation. The device is a high-powered overhead camera connected to machine-learning software that monitors employee performance as they make a pizza. The DOM Pizza Checker (pictured above) is a high powered camera and computer system that observes and evaluates employees as they make pizza. The camera matches a live image of the pizza being made to an image of the pizza that's been ordered.

LAS VEGAS, NV / ACCESSWIRE / October 16, 2019 / ASA Staffing World 2019 (Booth 253) - October 16, 2019 - CEIPAL, a SaaS platform for the front- and back-office business operations of staffing companies, today announced ground-breaking new capabilities to simplify, automate and enhance workflows for recruiting professionals. CEIPAL's integrated applicant tracking system (ATS) is the first-of-its-kind to harness artificial intelligence (AI) and deliver a powerful engine that offers searching, ranking, harvesting and chatbot capabilities to turn any recruiter into a high performer. "CEIPAL's AI functionality has transformed the way we recruit by drastically reducing search time, while greatly improving the quality of our shortlisted candidates," said Mani Kandan, Development and Technology Implementation Head of KRG Systems. "This has greatly improved the consistency of searches, and supercharged our recruiters, while saving our company up to 50 percent of what we would spend on any other ATS. In addition to substantial cost savings, CEIPAL's new AI engine empowers recruiters by speeding searches and improving quality with the following features: "CEIPAL is showing the recruitment world what artificial intelligence actually looks like in practice and our recruiters couldn't be more excited," said Derrick Alex, Head - Delivery Excellence of VDart, Inc. "We've worked with some of CEIPAL's leading competitors before and heard plenty of talk about AI, but never got to see it successfully deployed until we made the switch."

Contrary to common fears around how robots will impact jobs, leaders across the globe are reporting increased adoption of artificial intelligence (AI) and robots at work and many are welcoming it with love and optimism. According to the second annual "AI at Work" study of 8,370 employees, managers and HR leaders across 10 countries, including the UAE, conducted by Oracle and research firm Future Workplace, 64% of the people trust a robot more than their managers and half have turned to a robot instead of their manager for advice. Rahul Misra, vice-president for applications at Oracle Lower Gulf, told TechRadar Middle East that 82% of people think robots can do things better than their managers. In the UAE, respondents said robots are better at maintaining work schedules (42%), problem-solving (34%) and providing unbiased information (32%) while the top three tasks where managers are better than robots were understanding feelings (46%), coaching them (32%) and evaluating team performance (25%). "UAE is building a future based on tech innovation. Anything where the managers' role does not have an emotional quotient, people believe they can work with a fact-based model," he said.

Nations around the world are "stumbling zombie-like into a digital welfare dystopia" in which artificial intelligence and other technologies are used to target, surveil and punish the poorest people, the United Nation's monitor on poverty has warned. Philip Alston, UN rapporteur on extreme poverty, has produced a devastating account of how new digital technologies are revolutionizing the interaction between governments and the most vulnerable in society. In what he calls the rise of the "digital welfare state", billions of dollars of public money is now being invested in automated systems that are radically changing the nature of social protection. Alston's report on the human rights implications of the shift will be presented to the UN general assembly on Friday. It says that AI has the potential to improve dramatically the lives of disadvantaged communities, but warns that such hope is being lost amid the constant drive for cost cutting and "efficiency".

The governmenbt of Australia is subsidizing the study of responsible, ethical, and inclusive autonomous decision-making technologies. The Australian government is providing AU$31.8 million to the Australian Research Council to study responsible, ethical, and inclusive autonomous decision-making technologies. The Center of Excellence for Automated Decision-Making and Society, which will be based at the Royal Melbourne Institute of Technology (RMIT), will house researchers who will work with experts from seven other Australian universities, as well as 22 academic and industry partner organizations in Australia, Europe, Asia, and the U.S. The global research project aims to ensure machine learning and decision-making technologies can be used safely and ethically. Said RMIT researcher Julian Thomas, "Working with international partners and industry, the research will help Australians gain the full benefits of these new technologies, from better mobility, to improving our responses to humanitarian emergencies."

Sporting events are extremely complex and require a multitude of metrics to accurate describe the event. When making multiple predictions, one should make them from a single source to keep consistency across the predictions. We present a multi-task learning method of generating multiple predictions for analysis via a single prediction source. To enable this approach, we utilize a fine-grain representation using fine-grain spatial data using a wide-and-deep learning approach. Additionally, our approach can predict distributions rather than single point values. We highlighted the utility of our approach on the sport of Rugby League and call our prediction engine "Rugby-Bot".

Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to collect, and rarely exist in languages other than English, making training QA systems in other languages challenging. An alternative to building large monolingual training datasets is to develop cross-lingual systems which can transfer to a target language without requiring training data in that language. In order to develop such systems, it is crucial to invest in high quality multilingual evaluation benchmarks to measure progress. We present MLQA, a multi-way aligned extractive QA evaluation benchmark intended to spur research in this area. MLQA contains QA instances in 7 languages, namely English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. It consists of over 12K QA instances in English and 5K in each other language, with each QA instance being parallel between 4 languages on average. MLQA is built using a novel alignment context strategy on Wikipedia articles, and serves as a cross-lingual extension to existing extractive QA datasets. We evaluate current state-of-the-art cross-lingual representations on MLQA, and also provide machine-translation-based baselines. In all cases, transfer results are shown to be significantly behind training-language performance.