Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains. Existing CDCF models are either based on matrix factorization or deep neural networks. Either of the techniques in isolation may result in suboptimal performance for the prediction task. Also, most of the existing models face challenges particularly in handling diversity between domains and learning complex non-linear relationships that exist amongst entities (users/items) within and across domains. In this work, we propose an end-to-end neural network model -- NeuCDCF, to address these challenges in a cross-domain setting. More importantly, NeuCDCF follows a wide and deep framework and it learns the representations combinedly from both matrix factorization and deep neural networks. We perform experiments on four real-world datasets and demonstrate that our model performs better than state-of-the-art CDCF models.

LOS ALAMOS, N.M., July 18, 2019--Three teams who applied novel machine learning methods to successfully predict the timing of earthquakes from historic seismic data are splitting $50,000 in prize money from an open, online Kaggle competition hosted by Los Alamos National Laboratory and its partners. "Crowdsourcing for new approaches in earthquake forecasting helps us leverage a wide range of expertise in addressing one of the most important problems in Earth science, because of the devastating consequences of large quakes," said Bertrand Rouet-Leduc, a Los Alamos researcher who prepared the data for the competition. "The winning teams' results could have the potential to improve earthquake hazard assessments that could save lives and billions of dollars in infrastructure." Current scientific studies related to earthquake forecasting focus on three key points: when the event will occur, where it will occur, and how large it will be. The Kaggle competition provided a challenging dataset that was based on previously published laboratory analysis, to give the competitors a taxing project to explore.

When we talk about Artificial intelligence, we immediately recollect the science fiction blockbuster movies of the '80s and '90s like the Terminator, the Fifth Element and AI. These movies set a pathway for future scientists who have then researched the possibilities of simulating human behavior in robots. But back then, there was no concept of Big data processing which is vital for machine learning since there has to be huge processing of data as responses to different human emotions. When the technology matured with respect to Big data processing, it opened up infinite possibilities of applications of Artificial intelligence. Autonomous cars, AI chatbots, and AI enabled security checks, are some of the recent advents in the field of AI and machine learning.

Prevalent efforts have been put in automatically inferring genres of musical items. Yet, the propose solutions often rely on simplifications and fail to address the diversity and subjectivity of music genres. Accounting for these has, though, many benefits for aligning knowledge sources, integrating data and enriching musical items with tags. Here, we choose a new angle for the genre study by seeking to predict what would be the genres of musical items in a target tag system, knowing the genres assigned to them within source tag systems. We call this a translation task and identify three cases: 1) no common annotated corpus between source and target tag systems exists, 2) such a large corpus exists, 3) only few common annotations exist. We propose the related solutions: a knowledge-based translation modeled as taxonomy mapping, a statistical translation modeled with maximum likelihood logistic regression; a hybrid translation modeled with maximum a posteriori logistic regression with priors given by the knowledge-based translation. During evaluation, the solutions fit well the identified cases and the hybrid translation is systematically the most effective w.r.t. multilabel classification metrics. This is a first attempt to unify genre tag systems by leveraging both representation and interpretation diversity.

We introduce natural adversarial examples -- real-world, unmodified, and naturally occurring examples that cause classifier accuracy to significantly degrade. We curate 7,500 natural adversarial examples and release them in an ImageNet classifier test set that we call ImageNet-A. This dataset serves as a new way to measure classifier robustness. Like l_p adversarial examples, ImageNet-A examples successfully transfer to unseen or black-box classifiers. For example, on ImageNet-A a DenseNet-121 obtains around 2% accuracy, an accuracy drop of approximately 90%. Recovering this accuracy is not simple because ImageNet-A examples exploit deep flaws in current classifiers including their over-reliance on color, texture, and background cues. We observe that popular training techniques for improving robustness have little effect, but we show that some architectural changes can enhance robustness to natural adversarial examples. Future research is required to enable robust generalization to this hard ImageNet test set.

The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace. Knowledge and query authoring using natural language, especially controlled natural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced the KALM system (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introduces KALM-QA (KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, called MetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark. It is under consideration for acceptance in TPLP.

The developer of a popular app which transforms users' faces to predict how they will look as older people has insisted they are not accessing users' photographs without permission. FaceApp, which was launched by a Russian developer in 2017, uses artificial intelligence allowing people to see how they would look with different hair colour, eye colour or as a different gender. The app has topped download charts again this week, after users homed in on its ageing filter, which has since been used by dozens of celebrities and prominent figures to picture how they will supposedly look in several decades' time. This surge of interest has in turn created concerns that FaceApp is systematically harvesting users' images. People who upload their image to the app transfer the picture to a server controlled by the developer, with the photograph processing done remotely, rather than on their phone. These concerns have been heightened by growing awareness of online privacy issues in recent years and the fact that the developer is based in Russia, where many high-profile online misinformation campaigns have been based, in addition to a loosely-phrased privacy policy.

TTEC Holdings, Inc. (NASDAQ: TTEC), a leading digital global customer experience (CX) technology and services company focused on the design, implementation and delivery of transformative customer experience, engagement and growth solutions, has recently been recognized by Chief Learning Officer magazine as a 2019 LearningElite Silver Award winner. This robust, peer-reviewed ranking and benchmarking program recognizes those organizations that employ exemplary workforce development strategies that deliver significant business results. Special emphasis was placed this year on how these learning teams are helping their organizations adapt to and prepare for change. Winners were recently announced during the ninth annual LearningElite Awards program at the CLO Symposium conference. "TTEC is honored to be recognized as an elite learning organization and appreciates this award from Chief Learning Officer," said Steve Pollema, Executive Vice President, TTEC Digital.

When Mark Zuckerberg told Congress Facebook would use artificial intelligence to detect fake news posted on the social media site, he wasn't particularly specific about what that meant. Given my own work using image and video analytics, I suggest the company should be careful. Despite some basic potential flaws, AI can be a useful tool for spotting online propaganda – but it can also be startlingly good at creating misleading material. Researchers already know that online fake news spreads much more quickly and more widely than real news. My research has similarly found that online posts with fake medical information get more views, comments and likes than those with accurate medical content.