One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the question understanding and answer extraction tasks. One of the promising tracks investigated in QA is to map new questions to formerly answered questions that are `similar'. In this paper, we propose a novel QA approach based on Recognizing Question Entailment (RQE) and we describe the QA system and resources that we built and evaluated on real medical questions. First, we compare machine learning and deep learning methods for RQE using different kinds of datasets, including textual inference, question similarity and entailment in both the open and clinical domains. Second, we combine IR models with the best RQE method to select entailed questions and rank the retrieved answers. To study the end-to-end QA approach, we built the MedQuAD collection of 47,457 question-answer pairs from trusted medical sources, that we introduce and share in the scope of this paper. Following the evaluation process used in TREC 2017 LiveQA, we find that our approach exceeds the best results of the medical task with a 29.8% increase over the best official score. The evaluation results also support the relevance of question entailment for QA and highlight the effectiveness of combining IR and RQE for future QA efforts. Our findings also show that relying on a restricted set of reliable answer sources can bring a substantial improvement in medical QA.

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. Unlike existing question-answering problems, answer generation in e-commerce confronts three main challenges: (1) Reviews are informal and noisy; (2) joint modeling of reviews and key-value product attributes is challenging; (3) traditional methods easily generate meaningless answers. To tackle above challenges, we propose an adversarial learning based model, named PAAG, which is composed of three components: a question-aware review representation module, a key-value memory network encoding attributes, and a recurrent neural network as a sequence generator. Specifically, we employ a convolutional discriminator to distinguish whether our generated answer matches the facts. To extract the salience part of reviews, an attention-based review reader is proposed to capture the most relevant words given the question. Conducted on a large-scale real-world e-commerce dataset, our extensive experiments verify the effectiveness of each module in our proposed model. Moreover, our experiments show that our model achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.

These transformations will open up new opportunities for individuals, the economy, and society, but they have the potential to disrupt the current livelihoods of millions of Americans. Whether AI leads to unemployment and increases in inequality over the long-run depends not only on the technology itself but also on the institutions and policies that are in place. This report examines the expected impact of AI-driven automation on the economy, and describes broad strategies that could increase the benefits of AI and mitigate its costs. Economics of AI-Driven Automation Technological progress is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. One of the main ways that technology increases productivity is by decreasing the number of labor hours needed to create a unit of output.

Japan's National Police Agency is looking into artificial intelligence and how it can be used to combat terrorism. Japan is looking towards advanced technology to aid in the fight against terroism. As reported in The Japan News, the Japanese National Police Agency (NPA) is planning to begin experimenting with the use of artificial intelligence in anti-terrorism and criminal investigations next fiscal year. The experiments would involve using the characteristics of past attacks in Europe to train AI to identify terrorists in crowded areas, and introducing AI systems to analyze surveillance videos in criminal investigations. The NPA plans to conduct experiments in the three areas: identifying suspicious people and objects targeting large events, determining the model of automobiles and analyzing suspicious financial transactions.

I have been introduced to the technologies that are being used in the development process of Self-driving cars which have been supported by the all new 5G technology that allow a real-time communication for this cars. Some of these cars has been already being made and where actually being deployed and tested in China by the government to ensure it safety and reliability and might get the chance to be approved and deployed soon. With the rise of 5G technology more and more fields will be nourished with a whole technologies making the job easier and more precise.

Google is pouring an additional $3.1 million into Wikipedia, bringing its total contribution to the free encyclopedia over the past decade to more than $7.5 million, the company announced at the World Economic Forum Tuesday. A little over a third of those funds will go toward sustaining current efforts at the Wikimedia Foundation, the nonprofit that runs Wikipedia, and the remaining $2 million will focus on long-term viability through the organization's endowment. Google will also begin allowing Wikipedia editors to use several of its machine learning tools for free, the tech giant said. And Wikimedia and Google will soon broaden Project Tiger, a joint initiative they launched in 2017 to increase the number of Wikipedia articles written in underrepresented languages in India, to include 10 new languages in a handful of countries and regions. It will now be called GLOW, Growing Local Language Content on Wikipedia.

The 2001 film, A Beautiful Mind, depicts how John Nash, a Nobel Laureate in Economics, battled paranoid schizophrenia, the most common type of schizophrenia. It is a chronic mental disorder that affects how a person thinks, feels or behaves, thus upending not only the life of those suffering from it but all those close to them. The cause of the disease is not yet well understood, and most treatments focus on treating the symptoms rather than the disease. In a recent study, researchers from the University of Alberta, Canada, and the National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, have proposed to use artificial intelligence to help predict schizophrenia cases. The symptoms of schizophrenia usually begin in young adulthood and express themselves gradually. Most often, this condition is diagnosed based on the individual's observed behavior, either from personal experiences or reports from those that are familiar with him/her.

Like bees to honey, tech trends generate hype. Merely appending the word "dotcom" to a company's name drove up stock prices in the Internet's salad days. Cloud computing, big data, and cryptocurrencies each have taken their turn in the hype cycle in recent years. Every trend brings genuinely promising technological developments, befuddling buzzwords, enthusiastic investors, and reassuring consultants offering enlightenment--for a fee, naturally. Now the catchall phrase of artificial intelligence is shaping up as the defining technological trend of the moment.

Gliding silently through Alibaba Group Holding Ltd's futuristic'FlyZoo' hotel, black disc-shaped robots about a metre in height deliver food and drop off fresh towels. The robots are part of a suite of high-tech tools that Alibaba says drastically cuts the hotel's cost of human labor and eliminates the need for guests to interact with other people. Formally opened to the public last month, the 290-room FlyZoo is an incubator for technology Alibaba wants to sell to the hotel industry in the future and an opportunity to showcase its prowess in artificial intelligence. It is also an experiment that tests consumer comfort levels with unmanned commerce in China - a country where intrusive data-sharing technology is readily tolerated and often met with enthusiasm. 'It's all about the efficiency of the service and the consistency of service, because the robots are not disturbed by human moods.

The winners in business have shifted markedly in the last decade. When the 2010s began, the world's top 10 public companies by market capitalization were based in five countries; only two of them were in the tech sector, and none was worth more than $400 billion. Today, all of the top 10 are in the US and China, the majority are tech companies, and some have at least temporarily exceeded $1 trillion in value. We expect the keys to success will be just as different in 10 years' time. Several developing trends are likely to fundamentally reshape the future competitive environment, including the rapid advancement of artificial intelligence, the changing global economic order, and increasing scrutiny of the broader contribution of business to society, to name just a few.