Reference corpus for word alignment is an important resource for developing and evaluating word alignment methods. For Myanmar - English language pairs, there is no reference corpus to evaluate the word alignment tasks. Therefore, we created the guidelines f or Myanmar - English word alignment annotation between two languages over contrastive learning and built the Myanmar - English reference corpus consisting of verified alignments from Myanmar ALT of the Asian Language Treebank (ALT). This reference corpus conta ins confident labels sure (S) and possible (P) for word alignments which are used to test for the purpose of evaluation of the word alignments tasks. We discuss the most linking ambiguities to define consistent and systematic instructions to align manual w ords. We evaluated the results of annotators agreement using our reference corpus in terms of alignment error rate (AER) in word alignment tasks and discuss the words relationships in terms of BLEU scores. A bilingual corpus aligned at the level of sentences or words is a precious resource for developing machine translation systems. Word alignment is a fundamental step in extracting translation information from bilingual corpus and determines which words and phrases are translations of each other in the original and translated sentence. In most translation systems, translational correspondences are rather complex; for a language pair such as Myanmar and Eng lish that belong to the different word order languages.

How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans. To handle this problem, memory networks are usually a great choice and a promising way. However, existing memory networks do not perform well when leveraging heterogeneous information from different sources. In this paper, we propose a novel and versatile external memory networks called Heterogeneous Memory Networks (HMNs), to simultaneously utilize user utterances, dialogue history and background knowledge tuples. In our method, historical sequential dialogues are encoded and stored into the context-aware memory enhanced by gating mechanism while grounding knowledge tuples are encoded and stored into the context-free memory. During decoding, the decoder augmented with HMNs recurrently selects each word in one response utterance from these two memories and a general vocabulary. Experimental results on multiple real-world datasets show that HMNs significantly outperform the state-of-the-art data-driven task-oriented dialogue models in most domains.

Fire Emblem (FE) is a popular turn-based tactical role-playing game (TRPG) series on the Nintendo gaming consoles. This paper studies the computational complexity of a simplified version of FE (only floor tiles and wall tiles, the HP and other attributes of characters are constants at most 8, the movement distance per character each turn is fixed to 6 tiles), and proves that: 1. Simplified FE is PSPACE-complete (Thus actual FE is at least as hard). 2. Poly-round FE is NP-complete, even when the map is cycle-free, without healing units, and the weapon durability is a small constant. Poly-round FE is to decide whether the player can win the game in a certain number of rounds that is polynomial to the map size. A map is called cycle-free if its corresponding planar graph is cycle-free. These hardness results also hold for other similar TRPG series, such as Final Fantasy Tactics, Tactics Ogre and Disgaea.

An increasingly popular approach to alleviate this issue is to first learn general language representations on unlabeled data, which are then integrated in task-specific downstream systems. This approach was first popularized by word embeddings (Mikolov et al., 2013b; This work was performed during an internship at Facebook AI Research. Pennington et al., 2014), but has recently been superseded by sentence-level representations (Peters et al., 2018; Devlin et al., 2019). Nevertheless, all these works learn a separate model for each language and are thus unable to leverage information across different languages, greatly limiting their potential performance for low-resource languages. In this work, we are interested in universal language agnostic sentence embeddings, that is, vector representations of sentences that are general with respect to two dimensions: the input language and the NLP task.

Artificial intelligence (AI) is everywhere: personal digital assistants answer our questions, robo-advisors trade stocks for us, and driverless cars will someday take us where we want to go. AI has penetrated our lives, and its use is exploding in biomedical research and health care--including across all dimensions of cancer research, where the potential applications for AI are vast. Artificial Intelligence (AI) is a computer performing tasks commonly associated with human intelligence. Humans are coding or programing a computer to act, reason, and learn. An algorithm or model is the code that tells the computer how to act, reason, and learn.

If you are looking to automate your business processes, add bots for customer and business support, uncover business value through Data Analytics, Machine Learning or enhance your mobility solutions using AI, then visit UCS at Gitex 2019! UCS has developed state-of-the-art, innovative technologies to address challenges faced by businesses and to help them successfully meet strategic business needs and continue to advance further! This year as well UCS will be present to showcase their latest innovations in digital marketing with Reson8 and enterprise mobile solutions with SalesWorx, StockTrax & AssetTrax. Come explore our field sales and field marketing solution – SalesWorx; enabled with the newest technologies using data analytics, machine learning and AI to help increase the productivity and efficiency of your reps on the field. Talk to us to find out how we can enhance your digital communications strategy and help automate business processes.

A satellite image of Alaska captured in August 2005 shows the extent of smoke coverage from wildfires in the state's boreal forests. The blazes are likely to become large in exceptionally hot and dry conditions and when there's a high percentage of black spruce trees in the affected areas – key factors in a new predictive model developed by UCI scientists. An interdisciplinary team of scientists at the University of California, Irvine has developed a new technique for predicting the final size of a wildfire from the moment of ignition. Built around a machine learning algorithm, the model can help in forecasting whether a blaze is going to be small, medium or large by the time it has run its course – knowledge useful to those in charge of allocating scarce firefighting resources. The researchers' work is highlighted in a study published today in the International Journal of Wildland Fire.

Google has announced the setting up of Google Research India, an artificial intelligence research team in Bangalore, Karnataka, that will focus on advancing computer science and applying AI research to solve big problems in healthcare, agriculture and education, among other areas. The company said artificial intelligence is opening up the next phase of the technology revolution and India, with its world-class engineering talent, strong computer science programs and entrepreneurial drive, has the potential to lead the way in using this to tackle big challenges. In fact, there are already many examples of this happening in India, from detecting diabetic eye disease to improving flood forecasting and teaching kids to read. To take this trend further, Google has set up the Google Research India lab which will focus on two pillars: advancing fundamental computer science and AI research by building a strong team and partnering with the research community across the country, and applying this research to tackle big problems in core areas. Google Research India will be headed by Manish Gupta, computer scientist and a fellow of the Association for Computing Machinery with a background in deep learning across video analysis and education, compilers and computer systems.

Paris, France – Munich, Germany: Atos, a global digital transformation major has opened its German Artificial Intelligence (AI) Laboratory in Munich. In this innovative lab, Atos is developing solutions for its clients business using AI and other cutting-edge technologies. At the opening ceremony Thierry Breton, Atos – CEO and Chairman highlighted the relevance of AI for the industry and the economy. "Artificial Intelligence solutions have the power to be true game changers: For individual companies as well as entire economies, the holistic implementation of digital techniques leveraging AI is key to their success in the future," said Thierry Breton, Atos CEO and Chairman. "Our AI Lab is the right platform for the collaborative development of tangible digital use cases that deliver long-term value as quickly as possible," added Breton.

The book The Driver in the Driverless Car by Vivek Wadhwa and Alex Salkever explores how technology is changing faster and faster, and what impact that can have on the future of our society. It aims to help anyone - technical or non-technical - frame decisions and thinking about rapidly developing technologies. Salkever and Wadhwa cover a wide variety of such technologies, including robotics, AI, quantum computing, and driverless cars. InfoQ interviewed Wadhwa and Salkever about how the future from a technological point of view can look, how to approach technology in a positive way, what tasks robots are able to do or not do and what the future will bring, the benefits that self-driving cars bring and the challenges developing them, what developments are causing energy to become cheaper and cleaner, and what becomes possible with quantum computing. InfoQ: What made you decide to write this book?