As machine translation (MT) systems progress at a rapid pace, questions of their adequacy linger. In this study we focus on negation, a universal, core property of human language that significantly affects the semantics of an utterance. We investigate whether translating negation is an issue for modern MT systems using 17 translation directions as test bed. Through thorough analysis, we find that indeed the presence of negation can significantly impact downstream quality, in some cases resulting in quality reductions of more than 60%. We also provide a linguistically motivated analysis that directly explains the majority of our findings. We release our annotations and code to replicate our analysis here: https://github.com/mosharafhossain/negation-mt.

Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning. However, most existing GNN variants aggregate the neighborhood information in a fixed non-injective fashion, which may map different graphs or nodes to the same embedding, reducing the model expressiveness. We present a theoretical framework to design a continuous injective set function for neighborhood aggregation in GNN. Using the framework, we propose expressive GNN that aggregates the neighborhood of each node with a continuous injective set function, so that a GNN layer maps similar nodes with similar neighborhoods to similar embeddings, different nodes to different embeddings and the equivalent nodes or isomorphic graphs to the same embeddings. Moreover, the proposed expressive GNN can naturally learn expressive representations for graphs with continuous node attributes. We validate the proposed expressive GNN (ExpGNN) for graph classification on multiple benchmark datasets including simple graphs and attributed graphs. The experimental results demonstrate that our model achieves state-of-the-art performances on most of the benchmarks.

The accuracy of deep convolutional neural networks (CNNs) generally improves when fueled with high resolution images. However, this often comes at a high computational cost and high memory footprint. Inspired by the fact that not all regions in an image are task-relevant, we propose a novel framework that performs efficient image classification by processing a sequence of relatively small inputs, which are strategically selected from the original image with reinforcement learning. Such a dynamic decision process naturally facilitates adaptive inference at test time, i.e., it can be terminated once the model is sufficiently confident about its prediction and thus avoids further redundant computation. Notably, our framework is general and flexible as it is compatible with most of the state-of-the-art light-weighted CNNs (such as MobileNets, EfficientNets and RegNets), which can be conveniently deployed as the backbone feature extractor. Experiments on ImageNet show that our method consistently improves the computational efficiency of a wide variety of deep models. For example, it further reduces the average latency of the highly efficient MobileNet-V3 on an iPhone XS Max by 20% without sacrificing accuracy. Code and pre-trained models are available at https://github.com/blackfeather-wang/GFNet-Pytorch.

Interpreting how persuasive language influences audiences has implications across many domains like advertising, argumentation, and propaganda. Persuasion relies on more than a message's content. Arranging the order of the message itself (i.e., ordering specific rhetorical strategies) also plays an important role. To examine how strategy orderings contribute to persuasiveness, we first utilize a Variational Autoencoder model to disentangle content and rhetorical strategies in textual requests from a large-scale loan request corpus. We then visualize interplay between content and strategy through an attentional LSTM that predicts the success of textual requests. We find that specific (orderings of) strategies interact uniquely with a request's content to impact success rate, and thus the persuasiveness of a request.

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.

Call it a lucky find on Twitter. Santiago tweeted 20 questions you need to ace before getting a machine learning job. I figured I'd use these questions to understand developers' work better and maybe get a glimpse into future applications. The first questions were about various basic concepts of machine learning. Let's imagine, for example, that we are given a puzzle as a gift.

The dawn of the Artificial Intelligence (AI) era is upon us. The buzz-words Artificial Intelligence, Machine Learning (ML) and Deep Learning (DL) are used quite frequently in recent times. Let us introspect about each aspect individually to really appreciate these concepts. Firstly, I will list out the more boring formal definition and then proceed to explain them more intuitively with analogies to try and understand the concepts better. For today, let's start off with the big daddy of them all -- Artificial Intelligence (AI).

GPT-3 Bot Spends a Week Replying on Reddit, Starts Talking About the Illuminati Rhett Jones Gizmodo "..the length of the replies was especially unusual in that they were sometimes coming within a minute of the question first being asked. After an impressive run, the user was revealed to be a bot using OpenAI's remarkable language model GPT-3. The Quantum Internet Will Blow Your Mind. Here's What It Will Look Like Dan Hurley Discover "Fifty or so miles east of New York City, on the campus of Brookhaven National Laboratory, Eden Figueroa is one of the world's pioneering gardeners planting the seeds of a quantum internet. Capable of sending enormous amounts of data over vast distances, it would work not just faster than the current internet but faster than the speed of light--instantaneously, in fact, like the teleportation of Mr. Spock and Captain Kirk in Star Trek." This Robot Fry Chef on Rails Can Be Yours for $30,000 James Vincent The Verge "Like Flippy before it, Flippy ROAR is designed to automate simple food prep, specifically anything involving fryers and grills.

People around the world wish to talk to her or see her in real life. Sometimes listening to her interviews, her knowledge about various fields and her thought process as natural intelligence makes us forget that she is an Artificially Intelligent robotic machine; created by Hanson Robotics and an excellent example of AI, ML and Deep Learning. This robot is getting ready to revolutionize health care sector and the humanoid is already being used to help research autism and other diseases. Artificial Intelligence is ready to rule the world. Machines are becoming smarter day by day to help people.

Spiking Neural Networks requires less frequency while communicating, and involves minimum calculations for performing the task. The neural networks are the brain of Artificial Intelligence. Just like the neurons in the human body, these neural networks precede every process of AI. The modern neural networks are efficient in performing tasks but are lacks energy efficiency. That's why performing tasks like speech recognition, ECG and gesture recognition entails consumption of extensive energy.