Neural Machine Translation (NMT) has seen a tremendous spurt of growth in less than ten years, and has already entered a mature phase. While considered as the most widely used solution for Machine Translation, its performance on low-resource language pairs still remains sub-optimal compared to the high-resource counterparts, due to the unavailability of large parallel corpora. Therefore, the implementation of NMT techniques for low-resource language pairs has been receiving the spotlight in the recent NMT research arena, thus leading to a substantial amount of research reported on this topic. This paper presents a detailed survey of research advancements in low-resource language NMT (LRL-NMT), along with a quantitative analysis aimed at identifying the most popular solutions. Based on our findings from reviewing previous work, this survey paper provides a set of guidelines to select the possible NMT technique for a given LRL data setting. It also presents a holistic view of the LRL-NMT research landscape and provides a list of recommendations to further enhance the research efforts on LRL-NMT.

This work is driven by a practical question, corrections of Artificial Intelligence (AI) errors. Systematic re-training of a large AI system is hardly possible. To solve this problem, special external devices, correctors, are developed. They should provide quick and non-iterative system fix without modification of a legacy AI system. A common universal part of the AI corrector is a classifier that should separate undesired and erroneous behavior from normal operation. Training of such classifiers is a grand challenge at the heart of the one- and few-shot learning methods. Effectiveness of one- and few-short methods is based on either significant dimensionality reductions or the blessing of dimensionality effects. Stochastic separability is a blessing of dimensionality phenomenon that allows one-and few-shot error correction: in high-dimensional datasets under broad assumptions each point can be separated from the rest of the set by simple and robust linear discriminant. The hierarchical structure of data universe is introduced where each data cluster has a granular internal structure, etc. New stochastic separation theorems for the data distributions with fine-grained structure are formulated and proved. Separation theorems in infinite-dimensional limits are proven under assumptions of compact embedding of patterns into data space. New multi-correctors of AI systems are presented and illustrated with examples of predicting errors and learning new classes of objects by a deep convolutional neural network.

Unsupervised continual learning remains a relatively uncharted territory in the existing literature because the vast majority of existing works call for unlimited access of ground truth incurring expensive labelling cost. Another issue lies in the problem of task boundaries and task IDs which must be known for model's updates or model's predictions hindering feasibility for real-time deployment. Knowledge Retention in Self-Adaptive Deep Continual Learner, (KIERA), is proposed in this paper. KIERA is developed from the notion of flexible deep clustering approach possessing an elastic network structure to cope with changing environments in the timely manner. The centroid-based experience replay is put forward to overcome the catastrophic forgetting problem. KIERA does not exploit any labelled samples for model updates while featuring a task-agnostic merit. The advantage of KIERA has been numerically validated in popular continual learning problems where it shows highly competitive performance compared to state-of-the art approaches. Our implementation is available in \textit{\url{https://github.com/ContinualAL/KIERA}}.

For me, Wimbledon is one of the sporting highlights of the year. In previous Championships, I was lucky enough to be invited by IBM to see first-hand how the world's oldest tennis tournament was using cutting-edge technology to create amazing spectator experiences. Today, Wimbledon has become a technology-driven media operation, consistently refining its ability to keep fans engaged with the game in increasingly immersive and personalized ways. The AI of IBM Watson has been at the heart of this digital transformation. Much of it began with the introduction of "SlamTracker."

John Kaweske is Founder & CEO of North Star Holdings, Inc. and Tweedleaf. We like to think we know a thing or two about artificial intelligence. We've seen the ominous technological future depicted in television shows and films of robots slowly amalgamating into society. But this imagery is all wrong. Instead, automation has been in our lives for quite some time now, and many of us are likely not even aware of it.

Technological innovations offer a lot of advancements in the healthcare field, especially as we are looking for more personalized and effective treatments. From artificial intelligence technology to virtual reality technology and many other technologies are finding their application in the healthcare sector. In this article, we will provide an overview of some of the most important tech trends and how they shape this sector. Virtual reality technology is associated with the gaming sector, and for a good reason. There are actually a lot of VR games, and VR headsets have definitely progressed over the years.

You have a big dream of becoming a successful entrepreneur. You have Capital, Finance, intelligence but all your lacking is resources to learn, seek and manifest your burning desire of hailing the business world, then don't worry buddy, you always got our back! Applied Artificial Intelligence gives you a great framework of AI and machine learning with examples that were incredibly useful. It's an informative and useful guide to understanding and implementing AI solutions in an organization and covers both technical and non-technical topics. If you want to expand your knowledge of AI in business this book quickly provides an overview of the field, giving enough explanation of the inner workings of AI to provide a qualitative understanding. Artificial Intelligence and Machine Learning for Business is a quick read that delivers a simple and concise introduction for both business people and managers.

After decades of uninterrupted progress and growth, information technology has so evolved that it can be said we are entering the age of autonomous machines, but there exist many roadblocks in the way of making this a reality. In this article, we make a preliminary attempt at recognizing and categorizing the technical and non-technical challenges of autonomous machines; for each of the ten areas we have identified, we review current status, roadblocks, and potential research directions. It is hoped that this will help the community define clear, effective, and more formal development goalposts for the future.

Dr. Tuomo Hiippala, Assistant Professor in English Language and Digital Humanities in the Department of Languages at the University of Helsinki, has shared his videos and other learning materials for a pair of courses that he teaches, all in a single website for those looking to learn Applied Language Technology. While it appears that some of the material is not available to users beyond the University, specifically at least one hosted instance of the course code notebooks, besides the course website, the videos are all available in a single playlist as well. Together, these two courses provide an introduction to applied language technology for audiences who are unfamiliar with language technology and programming. The learning materials assume no previous knowledge of the Python programming language. Instead of treating text simply as data and a source of some information to be extracted, these learning materials emphasise text as the product of linguistic processes, which are inextricably related to language use in society.

The groundbreaking applications of Artificial intelligence are attracting tech multinationals like Apple, Microsoft, Amazon and Facebook to work on their future projects with more AI focused strategies. The AI effect is influencing the product road map of all such companies having the renowned AI-based applications that are launched at regular intervals in a year to automate their business operations with more promising results. Computer Vision is an important development under AI that has been extensively explored and applied into various industries from outdated to innovative self-driving cars moving on roads without human intervention. Such AI-backed innovative technologies work on such principles that encompass a huge amount of training data for computer vision. All these steps have their own challenges in terms of technical know-how and operational activities, so here we will discuss and help you how to deal with the labeling of training data and other related aspects required to complete this process. Before we start labeling of training data, you need aware where the technology of Computer Vision is effectively used to produce an AI-backed system or machine that can perform without too much human instructions and do their job independently as per the changing situations.