Adversarial examples are inputs to machine learning models designed to cause the model to make a mistake. They are useful for understanding the shortcomings of machine learning models, interpreting their results, and for regularisation. In NLP, however, most example generation strategies produce input text by using known, pre-specified semantic transformations, requiring significant manual effort and in-depth understanding of the problem and domain. In this paper, we investigate the problem of automatically generating adversarial examples that violate a set of given First-Order Logic constraints in Natural Language Inference (NLI). We reduce the problem of identifying such adversarial examples to a combinatorial optimisation problem, by maximising a quantity measuring the degree of violation of such constraints and by using a language model for generating linguistically-plausible examples. Furthermore, we propose a method for adversarially regularising neural NLI models for incorporating background knowledge. Our results show that, while the proposed method does not always improve results on the SNLI and MultiNLI datasets, it significantly and consistently increases the predictive accuracy on adversarially-crafted datasets -- up to a 79.6% relative improvement -- while drastically reducing the number of background knowledge violations. Furthermore, we show that adversarial examples transfer among model architectures, and that the proposed adversarial training procedure improves the robustness of NLI models to adversarial examples.

Inspired by STEM-focused YouTuber carykh, Indian developer Jaison Saji has produced a deep network system, DanceNet, that can automatically generate dance moves. Synced used DanceNet to produce a short clip (below) with code published on Github. Interested readers can use the system to improve this work or create their own. DanceNet uses a variational autoencoder (VAE) to automatically generate thousands of single dance pose pictures, then sequentially connect them to produce vigorous dance movements through joint training on Long Short-Term Memory (LSTM) and Mixture Density Networks (MDN). VAE is a commonly used generative model with two parts: an encoder transfers the image into a dense representation that has few dimensions and occupies less space than the original source and stores latent information about the input; while a decoder transfers dense-represented code back to its corresponding image.

Americans spend about 104 hours per year in traffic. Americans spend an average of 25.4 minutes commuting to work, according to U.S. Census Bureau data. For those who are counting, that amounts to 104 hours per year spent in traffic, with averages steadily rising every year. In Southern California, commutes are double the national average and considered the most stressful in the nation. The number of cars on highways increases annually, leading to more intense bottlenecks at interchanges, slower speeds on packed roads, and a higher frequency of accidents. Engineers at USC Viterbi are hoping to reverse that trend by adding a new type of artificial intelligence to traffic speed forecasting technology, giving drivers adaptive and predictive information for the fastest commute in every probable way.

Google recently successfully put its DeepMind artificial intelligence system to work recognising eye diseases. With AI also being used to diagnose cancer, and the launch of AI-driven smartphone apps that can discuss symptoms and triage patients, it might sound like we're not too far from the creation of a fully fledged AI doctor. Similar progress is being made putting AI to work writing software and evaluating legal contracts. AI has even started to make its mark in the creative world, generating artworks and fashion, evaluating graphic design, and helping people to create music. So does AI pose a threat to highly skilled jobs in the same way it does to ones that involve simple, repetitive tasks?

In a timely new paper, Young and colleagues discuss some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus of the paper is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks such as visual question answering (QA) and machine translation. In this comprehensive review, the reader will get a detailed understanding of the past, present, and future of deep learning in NLP. In addition, readers will also learn some of the current best practices for applying deep learning in NLP. Natural language processing (NLP) deals with building computational algorithms to automatically analyze and represent human language. NLP-based systems have enabled a wide range of applications such as Google's powerful search engine, and more recently, Amazon's voice assistant named Alexa.

Natural language processing (NLP) is an area of computer science and artificial intelligence that is concerned with the interaction between computers and humans in natural language. The ultimate goal of NLP is to enable computers to understand language as well as we do. It is the driving force behind things like virtual assistants, speech recognition, sentiment analysis, automatic text summarization, machine translation and much more. In this post, you will learn the basics of natural language processing, dive into some of its techniques and also learn how NLP benefited from the recent advances in Deep Learning. Natural Language Processing (NLP) is the intersection of Computer Science, Linguistics and Machine Learning that is concerned with the communication between computers and humans in natural language. NLP is all about enabling computers to understand and generate human language.

A'chatbox expert' shares'Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data' so you can'see' what you're getting into, if you want to understand artificial intelligence and-or virtual reality. Meaning, there is a circular relationship between mind and matter (virtual and real) (artificial and intelligent). Because there is a circular relationship between zero and one. Because there is a circular relationship between circumference and diameter. Where all of the diagrams explain the basic relationship between a zero and a one (a circle and a line).

We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation. Our approach requires no changes to the model architecture of a standard NMT system, but instead introduces a new component, the contextual parameter generator (CPG), that generates the parameters of the system (e.g., weights in a neural network). This parameter generator accepts source and target language embeddings as input, and generates the parameters for the encoder and the decoder, respectively. The rest of the model remains unchanged and is shared across all languages. We show how this simple modification enables the system to use monolingual data for training and also perform zero-shot translation. We further show it is able to surpass state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and that the learned language embeddings are able to uncover interesting relationships between languages.

Emotions are very important for human intelligence. For example, emotions are closely related to the appraisal of the internal bodily state and external stimuli. This helps us to respond quickly to the environment. Another important perspective in human intelligence is the role of emotions in decision-making. Moreover, the social aspect of emotions is also very important. Therefore, if the mechanism of emotions were elucidated, we could advance toward the essential understanding of our natural intelligence. In this study, a model of emotions is proposed to elucidate the mechanism of emotions through the computational model. Furthermore, from the viewpoint of partner robots, the model of emotions may help us to build robots that can have empathy for humans. To understand and sympathize with people's feelings, the robots need to have their own emotions. This may allow robots to be accepted in human society. The proposed model is implemented using deep neural networks consisting of three modules, which interact with each other. Simulation results reveal that the proposed model exhibits reasonable behavior as the basic mechanism of emotion.

Artificial intelligence (AI) is the core technology of technological revolution and industrial transformation. As one of the new intelligent needs in the AI 2.0 era, financial intelligence has elicited much attention from the academia and industry. In our current dynamic capital market, financial intelligence demonstrates a fast and accurate machine learning capability to handle complex data and has gradually acquired the potential to become a "financial brain". In this work, we survey existing studies on financial intelligence. First, we describe the concept of financial intelligence and elaborate on its position in the financial technology field. Second, we introduce the development of financial intelligence and review state-of-the-art techniques in wealth management, risk management, financial security, financial consulting, and blockchain. Finally, we propose a research framework called FinBrain and summarize four open issues, namely, explainable financial agents and causality, perception and prediction under uncertainty, risk-sensitive and robust decision making, and multi-agent game and mechanism design. We believe that these research directions can lay the foundation for the development of AI 2.0 in the finance field.