During the last decade, deep neural networks (DNN) have demonstrated impressive performances solving a wide range of problems in various domains such as medicine, finance, law, etc. Despite their great performances, they have long been considered as black-box systems, providing good results without being able to explain them. However, the inability to explain a system decision presents a serious risk in critical domains such as medicine where people's lives are at stake. Several works have been done to uncover the inner reasoning of deep neural networks. Saliency methods explain model decisions by assigning weights to input features that reflect their contribution to the classifier decision. However, not all features are necessary to explain a model decision. In practice, classifiers might strongly rely on a subset of features that might be sufficient to explain a particular decision. The aim of this article is to propose a method to simplify the prediction explanation of One-Dimensional (1D) Convolutional Neural Networks (CNN) by identifying sufficient and necessary features-sets. We also propose an adaptation of Layer-wise Relevance Propagation for 1D-CNN. Experiments carried out on multiple datasets show that the distribution of relevance among features is similar to that obtained with a well known state of the art model. Moreover, the sufficient and necessary features extracted perceptually appear convincing to humans.

War is always going to be fought with people and weapons. It is also always going to involve "platforms," such as tanks and the capabilities they have. It is important to understand that in discussions of the future of warfare the issue is not just about the person or the platform but also tying it all together. At the heart of that effort today are attempts to develop better algorithms and artificial intelligence. This will play an increasing role in war, especially in hi-tech militaries, in the future.

Will early retirement be the norm this century? With every passing AI headline, even the futurists among us are increasingly shaken. In what reads like the Book of Revelation, we've been forewarned of an impending robot Armageddon. Make no mistake, there is enough in the air that reeks of a slowly percolating paradigm shift. In fact, don't be so hard on thee as the trepidation, that's going around like some super-bug, follows.

In this paper, we investigate data augmentation for text generation, which we call GenAug. Text generation and language modeling are important tasks within natural language processing, and are especially challenging for low-data regimes. We propose and evaluate various augmentation methods, including some that incorporate external knowledge, for finetuning GPT-2 on a subset of Yelp Reviews. We also examine the relationship between the amount of augmentation and the quality of the generated text. We utilize several metrics that evaluate important aspects of the generated text including its diversity and fluency. Our experiments demonstrate that insertion of character-level synthetic noise and keyword replacement with hypernyms are effective augmentation methods, and that the quality of generations improves to a peak at approximately three times the amount of original data.

Anomaly detection suffers from unbalanced data since anomalies are quite rare. Synthetically generated anomalies are a solution to such ill or not fully defined data. However, synthesis requires an expressive representation to guarantee the quality of the generated data. In this paper, we propose a two-level hierarchical latent space representation that distills inliers' feature-descriptors (through autoencoders) into more robust representations based on a variational family of distributions (through a variational autoencoder) for zero-shot anomaly generation. From the learned latent distributions, we select those that lie on the outskirts of the training data as synthetic-outlier generators. And, we synthesize from them, i.e., generate negative samples without seen them before, to train binary classifiers. We found that the use of the proposed hierarchical structure for feature distillation and fusion creates robust and general representations that allow us to synthesize pseudo outlier samples. And in turn, train robust binary classifiers for true outlier detection (without the need for actual outliers during training). We demonstrate the performance of our proposal on several benchmarks for anomaly detection.

Although the prediction of dialects is an important language processing task, with a wide range of applications, existing work is largely limited to coarse-grained varieties. Inspired by geolocation research, we propose the novel task of Micro-Dialect Identification (MDI) and introduce MARBERT, a new language model with striking abilities to predict a fine-grained variety (as small as that of a city) given a single, short message. For modeling, we offer a range of novel spatially and linguistically-motivated multi-task learning models. To showcase the utility of our models, we introduce a new, large-scale dataset of Arabic micro-varieties (low-resource) suited to our tasks. MARBERT predicts micro-dialects with 9.9% F1, ~76X better than a majority class baseline. Our new language model also establishes new state-of-the-art on several external tasks.

Predictive coding is an influential theory of cortical function which posits that the principal computation the brain performs, which underlies both perception and learning, is the minimization of prediction errors. While motivated by high-level notions of variational inference, detailed neurophysiological models of cortical microcircuits which can implements its computations have been developed. Moreover, under certain conditions, predictive coding has been shown to approximate the backpropagation of error algorithm, and thus provides a relatively biologically plausible credit-assignment mechanism for training deep networks. However, standard implementations of the algorithm still involve potentially neurally implausible features such as identical forward and backward weights, backward nonlinear derivatives, and 1-1 error unit connectivity. In this paper, we show that these features are not integral to the algorithm and can be removed either directly or through learning additional sets of parameters with Hebbian update rules without noticeable harm to learning performance. Our work thus relaxes current constraints on potential microcircuit designs and hopefully opens up new regions of the design-space for neuromorphic implementations of predictive coding.

Of all corporate functions, sales by its very nature is surely the most people-focused. While it may no longer involve quite as much face-to-face interaction as it once did, selling has remained emphatically a job for people rather than machines. However, artificial intelligence (AI) and machine-learning are already starting to make major inroads into the sales process, adding an extra dimension to everything from marketing automation to customer relationship management. According to Salesforce Research, high-performing teams are at least twice as likely to be using intelligent sales technologies such as artificial intelligence, sentiment analysis, next-step analysis and deep-learning. So, what further changes in the sales environment can we expect to see over the coming years?

Many orchid species are threatened by land conversion and illegal harvesting. However, only a fraction of those species is included in the IUCN Red List of Threatened Species, because assessments require a lot of time, resources and expertise. A new approach, an automated assessment developed under the lead of biodiversity researchers from Central Germany, now shows that almost 30% of all orchid species are possibly threatened. The new approach could speed up conservation assessments of all species on Earth. Orchids are more than just decorative--they are also economically important in horticulture, in the pharmaceutical industry and even in the food industry.

"We've witnessed ten years of change in a month" is a typical description of how the pandemic is accelerating the use of telemedicine. Before the virus, video appointments made up only 1% of the 350m consultations which Britain's National Health Service handles each year. Companies like Docly, eConsult and AccuRx are changing that. The latter claims that 90% of primary care clinics in England are now using its video-calling system. The most dramatic form of telemedicine is remote surgery.