Bidirectional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform local and global information exchange between words simultaneously, rather than incremental reading of a sequence of words. Results on various classification and sequence labelling benchmarks show that the proposed model has strong representation power, giving highly competitive performances compared to stacked BiLSTM models with similar parameter numbers. 1 Introduction Neural models have become the dominant approach in the NLP literature. Compared to handcrafted indicator features, neural sentence representations are less sparse, and more flexible in encoding intricate syntactic and semantic information. Among various neural networks for encoding sentences, bidirectional LSTMs (BiLSTM) (Hochreiter and Schmidhuber, 1997) have been a dominant method, giving state-of-the-art results in language modelling (Sundermeyer et al., 2012), machine translation (Bahdanau et al., 2015), syntactic parsing (Dozat and Manning, 2017) and question answering (Tan et al., 2015). Despite their success, BiLSTMs have been shown to suffer several limitations.

We present an approach to interactive-predictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations. Secondly, human effort is further reduced by using the entropy of word predictions as uncertainty criterion to trigger feedback requests. Lastly, online updates of the model parameters after every interaction allow the model to adapt quickly. We show in simulation experiments that reward signals on partial translations significantly improve character F-score and BLEU compared to feedback on full translations only, while human effort can be reduced to an average number of $5$ feedback requests for every input.

NEW DELHI: Punjab National Bank on Sunday said it plans to rely on artificial intelligence (AI) for reconciliation of accounts and incorporate analytics for improving audit systems as it seeks to clean up the process after the massive fraud involving Nirav Modi and Mehul Choksi. The bank has already initiated a number of steps and now intends to firmly put behind memories of the over Rs 20,000 crore swindle by the diamond merchants by targeting an 11% growth in business during the current financial year. The bank discussed the road map, where the target is to increase total business to Rs 12 lakh crore, including a Rs 1 lakh crore or 13.7% increase in low-cost current and savings account deposits, it said in a statement. PNB managing director Sunil Mehta has maintained that the bank's business was not impacted by the scam, which was limited to the Brady House branch in Mumbai. "The'business remodelling' brought alive by changes at PNB is essential to ensure that the bank continues to grow and compete with its peers better," he said and elaborated on several steps that are in the pipeline or have already been initiated to reduce human intervention.

Scientists have developed a new way to improve how computers "see" and "understand" objects in the real world by training the computers' vision systems in a virtual environment. The research team published their findings in IEEE/CAA Journal of Autmatica Sinica, a joint publication of the IEEE and the Chinese Association of Automation. For computers to learn and accurately recognize objects, such as a building, a street, or humans, the machines must rely on processing huge amount of labeled data, in this case, images of objects with accurate annotations. A self-driving car, for instance, needs thousands of images of roads and cars to learn from. Datasets therefore play a crucial role in the training and testing of the computer vision systems.

During the first industrial revolution, workers flocked in the cities in masses because of the factories that were coming up. Individual craftsmen were kicked out of business because of manufacturing, and this allowed consumers to access cheaper products much faster. Although some workers were replaced, new jobs were created and as time passed by, employment levels rose up to all-time highs. It is with certainty that technology will eliminate very many jobs, from a few million to more than a billion. According to Andrew Charlton, "This huge variance is due to the fact that we are not yet sure about the number of jobs that robots will take over in the next couple of years; we are not sure where technology will be headed."

The burgeoning online lending segment in India is also giving rise to a new kind of challenge on sourcing credit score data. To solve this problem, several fintech companies are using Artificial Intelligence (AI) and Machine Learning (ML) to create alternate lending data score for more than 80 per cent of the Indian population who have no credit scores. From the place where people live to the restaurants they visit to their digital footprints on social media, ML captures it all. Mohan Yadav, a 25-year-old software professional in Mumbai, was denied a ₹25,000 personal loan by his bank since he had no credit history. After a quick search online, he applied for a loan from Cashe, a Mumbai-based fintech company that offers personal loans to salaried professionals who have just entered the workforce.

PARIS – A rare series of sketches featuring "Astro Boy," the robot character created by late Japanese manga artist Osamu Tezuka, fetched a record 269,400 euros ($322,300) for works by the artist at an auction Saturday in Paris. Auction house Artcurial said the successful bid was nearly five times the estimated bid price of between 40,000 to 60,000 euros. Artcurial said it is very rare for the original drawings of Tezuka (1928-1989) to be auctioned, and that the successful bid was likely the largest ever for the cartoonist's work. The drawings comprise six panels of a page of a comic and depict Astro Boy's fight with an enemy. They were published in a manga magazine in Japan around 1956 or 1957.

The age of Artificial Intelligence (AI) is almost upon us. Rapid developments in machine learning have allowed us to build better, smarter machines that are capable of making decisions and handling tasks similar to humans. Some of these developments are also being implemented in mobiles to create the next generation of smarter phones. I attended the recent Huawei Global Analyst Summit in Shenzhen to speak with the heads of Huawei's development teams and find out more about the future of AI in mobiles. Huawei is a leading brand in mobile phone technology.

Shenzhen-based AI and humanoid robotic company Ubtech has secured $820 million in Series C funding round, our sister site TechNode Chinese is reporting (in Chinese), setting a new financing record for the largest investment raised in a single round by an AI company. The new investment brings Ubtech's valuation to approximately $5 billion. The new funding was led by Tencent, with participation from a long list of investors including Industrial and Commercial Bank of China, Haier, Telstra, China Minsheng Bank, Juran Zhijia, CreditEase, and Green Pine Capital, with additional investment from CDH Investments who led Ubtech's 100 million series B funding in 2016. Ubtech founder and CEO Zhou Jian said the new round of funding has brought in invaluable investors and the investment will be dedicated to facilitating Ubtech's future commercialization plans. Zhou said the investment will be used in three main areas including R&D, market expansion/branding, and recruitment.

With the creation of Smart Cities high on the UAE government's agenda, a new course from the Center for Executive and Professional Education (CEPE) at American University of Sharjah (AUS) will help executives apply the benefits of Artificial Intelligence (AI) to their Smart City projects. The Certificate in AI for Smart Cities is being delivered by CEPE in conjunction with the AUS College of Engineering's Department of Computer Science and Engineering. Topics to be covered include Big Data, Machine Learning, Cyber-Physical Systems, Internet of Things, Cyber Security and Blockchain, and Cloud Computing. Participants require no prior knowledge of AI, as the course has been designed for mid- to high-level executives from across the Middle East tasked with developing Smart City solutions. It is an introductory course intended for those who want to better understand how AI can transform their operations, with a focus on learning from global best practice and case studies. AI holds enormous value for the UAE and wider GCC.