Geographically, the global artificial intelligence in retail market is segmented into five major regions, namely, North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. The global AI in retail market is analyzed methodically with respect to major countries in each of the regions with the help of bottom-up approach to arrive at the most precise market estimation. At present, North America holds a dominating position in the global AI in retail market. The region has high technology adoption rate, presence of key players & start-ups, and high penetration of internet. Consequently, North America is expected to retain its dominance throughout the forecast period.

For the second year in a row, Canada has refused visas to dozens of researchers - most of them from Africa - who were hoping to attend an artificial intelligence (AI) conference in Vancouver. The hassles have caused at least one other AI conference to choose a different country for their next event. The Neural Information Processing Systems conference (NeurIPS), which brings together thousands of experts and researchers from all over the world, will be held in Vancouver next month. Last week, NeurIPS began hearing that several attendees had had their visas denied. It was the second year in a row the conference has had visa troubles.

"AI is the broader concept of machines being able to carry out tasks in a way that is considered smart. The smart processes include machines being able to function automatically, reason and learn by themselves," explains Claudia Ayin, an independent ICT consultant. Machine learning is the aspect of AI that allows computers to learn by themselves. "Machine learning is therefore a branch of AI that is able to process large data sets and let machines learn for themselves without having been explicitly programmed," she adds. According to MarketsandMarkets, an Indian research company, in 2018 the worldwide AI in agriculture market was valued at €545 million and, by 2025, is expected to reach €2.4 billion as more and more smallholder farmers adopt new, data-driven technologies.

By 1433, the Chinese admiral Zheng He had already sailed from China to India, Indonesia, and even Africa on caravels twice as large as those Christopher Columbus used 59 years later for his fateful journey. China could have been the country to discover America. Instead, its government surprisingly decided to put an end to its naval activities and burn its entire fleet of ships, indirectly allowing Spain to conquer America and bring prosperity to Europe. It took more than 5 centuries for China to recover from this political decision. What could make such an advanced country deliberately turn away from its future?

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality. Identifying potential DDIs during the drug design process is critical for patients and society. Although several computational models have been proposed for DDI prediction, there are still limitations: (1) specialized design of drug representation for DDI predictions is lacking; (2) predictions are based on limited labelled data and do not generalize well to unseen drugs or DDIs; and (3) models are characterized by a large number of parameters, thus are hard to interpret. In this work, we develop a C hemicA l S ubstrucT urE R epresentation ( CASTER) framework that predicts DDIs given chemical structures of drugs. CASTER aims to mitigate these limitations via (1) a sequential pattern mining module rooted in the DDI mechanism to efficiently characterize functional substructures of drugs; (2) an auto-encoding module that leverages both labelled and unlabelled chemical structure data to improve predictive accuracy and generalizability; and (3) a dictionary learning module that explains the prediction via a small set of coefficients which measure the relevance of each input substructures to the DDI outcome. We evaluated CASTER on two real-world DDI datasets and showed that it performed better than state-of-the-art baselines and provided interpretable predictions. 1 Introduction Adverse drug-drug interactions (DDIs) are caused by pharmacological interactions of drugs. They result in a large number of morbidity and mortality, and incur huge medical costs (Giacomini et al. 2007; Onakpoya, Heneghan, and Aronson 2016).

A BSTRACT Recent advances in deep learning theory have evoked the study of generalizabil-ity across different local minima of deep neural networks (DNNs). While current work focused on either discovering properties of good local minima or developing regularization techniques to induce good local minima, no approach exists that can tackle both problems. We achieve these two goals successfully in a unified manner. Specifically, based on the Fisher information we propose a metric both strongly indicative of generalizability of local minima and effectively applied as a practical regularizer. We provide theoretical analysis including a generalization bound and empirically demonstrate the success of our approach in both capturing and improving the generalizability of DNNs. Experiments are performed on CIFAR-10 and CIFAR-100 for various network architectures. 1 I NTRODUCTION Recently, there has been a surge in the interest of acquiring a theoretical understanding over deep neural network's behavior. Breakthroughs have been made in characterizing the optimization process, showing that learning algorithms such as stochastic gradient descent (SGD) tend to end up in one of the many local minima which have close-to-zero training loss (Choromanska et al., 2015; Dauphin et al., 2014; Kawaguchi, 2016; Nguyen & Hein, 2018; Du et al., 2018). It is, therefore, natural to ask two closely related questions: (a) What kind of local minima can generalize better? To our knowledge, existing work focused only on one of the two questions. For the "what" question, various definitions of "flatness/sharpness" have been introduced and analyzed (Keskar et al., 2017; Neyshabur et al., 2018; 2017; Wu et al., 2017; Liang et al., 2017).

--Wide spread of satirical news in online communities is an ongoing trend. The nature of satires are so inherently ambiguous that sometimes it's too hard even for humans to understand whether it's actually satire or not. So, research interest has grown in this field. The purpose of this research is to detect Bangla satirical news spread in online news portals as well as social media. In this paper we propose a hybrid technique for extracting feature from text documents combining Word2V ecand TF-IDF. Using our proposed feature extraction technique, with standard CNN architecture we could detect whether a Bangla text document is satire or not with an accuracy of more than 96%. Satires can be considered as a literary form which involves a delicate balance between criticism and humor.

Gartner has unveiled its biggest predictions for IT organisations and users for 2020 and beyond. These predictions analyse how technology is changing society and the expectations of users. "Technology is changing the notion of what it means to be human," said Daryl Plummer, VP and Fellow at Gartner. "CIOs in end-user organizations must understand the effects of the change and reset expectations for what technology means." "This year's predictions help us move beyond thinking about mere notions of technology adoption and draw us more deeply into issues surrounding what it means to be human in the digital world," said Plummer.

Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. In fact, Mathematics is behind everything around us, from shapes, patterns, and colors, to the count of petals in a flower. Mathematics is embedded in each and every aspect of our lives. Although having a good understanding of programming languages, Machine Learning algorithms and following a data-driven approach is necessary to become a Data Scientist, Data Science isn't all about these fields. In this blog post, you will understand the importance of Math and Statistics for Data Science and how they can be used to build Machine Learning models.

The statistical study, the report outlines the Global Artificial Intelligence in Education System Industry including production, cost/profit, supply-demand, and import-export. The total market is further bifurcated into a company, by country, and by various segmentation for the competitive landscape study.