The phrase'deal of a lifetime' tends to get thrown around a lot, but a few keen deal-seekers during Amazon's prime day may have actually found it. Due to a pricing glitch, Amazon shoppers have reported being able to buy high-end camera equipment that retails at as much as $13,000, for just $100 or less. One user, posting on Reddit under the name SoccerMomDeals, said they spent $500 on $65,000 worth of camera lenses. On the discount forum Slickdeals, waves of users reported news of their plunder, which included items like the Sony Alpha a6000 Mirrorless Digital Camera with a 16-50 mm lens, marked down from $550 to just $94. Camera gear markdowns had shoppers scrambling to order merchandise for Amazon caught on to what appears to be a pricing glitch.

Objective To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).

"Artificial intelligence should improve all that it touches and now that we're seeing the impact of AI on advancement, it's critical that we apply this …

Technology provides us with the toolkit to change lives for the better – but if unchecked, it also has the power to discriminate and reinforce stereotypes and bias. This is true of any technology past, present and future. But it is the advancement of distributed systems capable of storing and processing massive amounts of previously disparate data coupled with the emergence of artificial intelligence (AI) into our everyday lives that requires us to urgently refocus on how technology is being architected, engineered, tested, deployed and governed, to ensure that its impact remains positive – and only positive. Whether we explicitly asked for it or not, the fact is that, today and right at this very moment, artificial intelligence is impacting your life. The majority of the time, AI is invisible to us – whenever we enter a search term into Google, visit websites, use social media, browse Netflix, use online banking, use public transport or walk the streets of any major city, AI algorithms are busy working away in the background to return relevant search results, analyse your browsing habits, social network, viewing preferences, detect signs of fraud and to capture an image of, and process, your face.

The possible impact of algorithmic recommendation on the autonomy and free choice of Internet users is being increasingly discussed, especially in terms of the rendering of information and the structuring of interactions. This paper aims at reviewing and framing this issue along a double dichotomy. The first one addresses the discrepancy between users' intentions and actions (1) under some algorithmic influence and (2) without it. The second one distinguishes algorithmic biases on (1) prior information rearrangement and (2) posterior information arrangement. In all cases, we focus on and differentiate situations where algorithms empirically appear to expand the cognitive and social horizon of users, from those where they seem to limit that horizon. We additionally suggest that these biases may not be properly appraised without taking into account the underlying social processes which algorithms are building upon.

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.