Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent, modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.

Deep Reinforcement Learning has made significant progress in multi-agent systems in recent years. In this review article, we have mostly focused on recent papers on Multi-Agent Reinforcement Learning (MARL) than the older papers, unless it was necessary. Several ideas and papers are proposed with different notations, and we tried our best to unify them with a single notation and categorize them by their relevance. In particular, we have focused on five common approaches on modeling and solving multi-agent reinforcement learning problems: (I) independent-learners, (II) fully observable critic, (III) value function decomposition, (IV) consensus, (IV) learn to communicate. Moreover, we discuss some new emerging research areas in MARL along with the relevant recent papers. In addition, some of the recent applications of MARL in real world are discussed. Finally, a list of available environments for MARL research are provided and the paper is concluded with proposals on the possible research directions.

Acquiring new robot motor skills is cumbersome, as learning a skill from scratch and without prior knowledge requires the exploration of a large space of motor configurations. Accordingly, for learning a new task, time could be saved by restricting the parameter search space by initializing it with the solution of a similar task. We present a framework which is able of such knowledge transfer from already learned movement skills to a new learning task. The framework combines probabilistic movement primitives with descriptions of their effects for skill representation. New skills are first initialized with parameters inferred from related movement primitives and thereafter adapted to the new task through relative entropy policy search. We compare two different transfer approaches to initialize the search space distribution with data of known skills with a similar effect. We show the different benefits of the two knowledge transfer approaches on an object pushing task for a simulated 3-DOF robot. We can show that the quality of the learned skills improves and the required iterations to learn a new task can be reduced by more than 60% when past experiences are utilized.

Understanding big data and artificial intelligence is not something reserved for computer scientists, said Kirk Borne, principal data scientist for the international consulting firm, Booz Allen Hamilton. Manipulating massive amounts of data that are becoming available will affect every form of intellectual and business pursuit. "Data literacy is a way of thinking, not a thing to think about," Borne told attendees of BDA Edcon, the International Big Data and Analytics Education Conference hosted by University of Maryland University College on June 3 and 4. The conference explored how the convergence of big data analytics, artificial intelligence and cognitive computing can be implemented into teaching and learning experiences today to meet industry demand. The two-day event included the final judging and presentation of awards for the annual Global Analytics Competition. Data literacy is "a way of business, a way of doing whatever we do in the world. It really is for everyone, not just for data scientists," according to Borne.

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Disney's ESPN has chosen not to broadcast a recent video-game competition -- one that features gun violence -- in the wake of last weekend's mass shootings in Texas and Ohio, according to a person familiar with the plans. ESPN is delaying its planned Aug. 10 broadcast of a recent tournament for Apex Legends, a popular battle royale game made by publisher Electronic Arts Inc., the person said, asking not to be identified as the matter is internal. The decision comes in the wake of the two shootings that prompted politicians, including President Donald Trump, to say video games that glorify violence could be contributing to the country's shooting epidemic. ESPN2 will air the taped segment on three nights in October, according to the person. It will still be available this weekend on ESPN's digital channels, including its app.

thinking is important for becoming smarter and have the ability to solve problems. Are you an abstract thinker? If not, it's time to change this situation. Good problem-solving skills and logical reasoning skills is your top priority now. Machine learning revolves around finding patterns in data.

Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning. This program consists of 9 courses providing you with latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets. It is a myth that to become a data scientist you need a Ph.D.

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images.

This theory is supported by a new survey conducted by Northeastern University and Gallup that reveals an international cross-section of opinions about artificial intelligence as economies around the world undergo the transformative move to automation. The poll shows that the majority of people in the United States, Canada, and the United Kingdom think that artificial intelligence will improve their lives, but they believe that higher education, government, and employers are not doing enough to improve their skills.