Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Persian. In order to target this gap for low resource languages, we propose a "Large Scale Colloquial Persian Dataset" (LSCP). LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. We believe that further investigations and processing, as well as the application of novel algorithms and methods, can strengthen enriching computerized understanding and processing of low resource languages. The proposed corpus consists of 120M sentences resulted from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translation in five different languages.

Artificial intelligence (AI) will change the way we learn and work in the near future. Nearly 400 million workers globally will change their occupations in the next 10 years, and business schools are uniquely situated to respond to the shifts coming to the future of work. However, a recent study, "Implications of Artificial Intelligence on Business Schools and Lifelong Learning," shows that business schools remain cautious in adapting management education to address the changing needs of students, workers and organizations, writes Anne Trumbore in this opinion piece. Trumbore, one of the study's coauthors, is senior director of Wharton Online, a strategic digital learning initiative at the Wharton School of the University of Pennsylvania. In the past few weeks, COVID 19 has moved hundreds of millions of students around the globe from physical to online classes.

AI is by far the biggest hype I have seen in my career, and for good reason. It could revolutionise customer experience at a time when experience and service are the final frontier in differentiation. In the entire digital transformation of the world, we are spending over $1.2 trillion next year, and $6 trillion over the next four years. Much of this spend will focus on technologies that allow the enterprise to differentiate in the market and deliver exceptional customer experience. An emerging Gen Z employee and customer base is changing engagement models with companies, using more channels and more self-service options.

Smartphone owners are being given new rights to have their device repaired under laws introduced by the EU that could put an end to'throwaway culture'. Manufacturers will made to fix broken electronic devices under the EU's new Circular Economy Action Plan (CEAP), which will also cover the UK despite Brexit. The plan, unveiled on Wednesday by the European Commission, will give Europeans'the right to repair' by making devices easier to fix. The laws, which will also apply to tablets, laptops and printers, focus on a more circular economy – where electronic resources are kept in use as long as possible. Major tech companies making devices hard to fix, including Apple, Samsung and Huawei, is creating an electronic and electrical rubbish mountain – wasting resources and blighting the environment, say green campaigners.

Computers and robots are now learning to make decisions! Of course, "deciding" is a big word for machines that have no consciousness and whose level of "reasoning" is not even as evolved as that of a frog. But the latest developments in artificial intelligence (AI) are enough to frighten some and to arouse the fantasies of others. Between myth and reality, where exactly does the current research stand in this technology that threatens to disrupt all others? In its Wide Angle section, the Courier attempts to untangle the various paths of inquiry and offers some terminological signposts to help uninitiated readers to find their way through the fascinating but scary world of AI.

FinTech breaks down some of the key technologies behind the digital transformation of the insurance industry, and explores the startups behind their rapid adoption. As the technological transformation of the global business landscape continues, more and more industries are feeling the pressure to embrace the potential of digital solutions, or be consigned to the scrapheap. In the unending quest for legacy industries to stay agile and avoid disruption, leading edge technologies are fusing with existing business models to create new, tech-driven disciplines. Fintech has been one of the great success stories of this trend, as financial institutions harness the power of artificial intelligence (AI), big data and multi-platform customer experiences (CX) to meet the expectations of their customers. One subset of the fintech space that's proven to be ripe for innovation and disruption in the past few years is the insurance industry. As one of the oldest financial businesses, insurance has traditionally been a market that's slow to adopt, cautious and favours those with deep pockets who already have a seat at the table.

To accommodate the diversity of infrastructures used for AI solutions, IBM has expanded support of its award-winning software beyond POWER architectures to include Intel platforms. To avoid confusion in the marketplace, IBM PowerAI Vision has been renamed IBM Visual Insights. Contact your IBM representative for the list of selected services available in your country, either as standard or customized offerings for the efficient installation, implementation, or integration of this product. IBM Support is your gateway to technical support tools and resources that are designed to help you save time and simplify support. IBM Support can help you find answers to questions, download fixes, troubleshoot, submit and track problem cases, and build skills. Learn and stay informed about the transformation of IBM Support, including new tools, new processes, and new capabilities, by going to the IBM Support Insider.

This study considers the expressiveness (that is the expressive power or expressivity) of different types of Knowledge Organization Systems (KOS) and discusses its potential to be machine-processable in the context of the Semantic Web. For this purpose, the theoretical foundations of KOS are reviewed based on conceptualizations introduced by the Functional Requirements for Subject Authority Data (FRSAD) and the Simple Knowledge Organization System (SKOS); natural language processing techniques are also implemented. Applying a comparative analysis, the dataset comprises a thesaurus (Eurovoc), a subject headings system (LCSH) and a classification scheme (DDC). These are compared with an ontology (CIDOC-CRM) by focusing on how they define and handle concepts and relations. It was observed that LCSH and DDC focus on the formalism of character strings (nomens) rather than on the modelling of semantics; their definition of what constitutes a concept is quite fuzzy, and they comprise a large number of complex concepts. By contrast, thesauri have a coherent definition of what constitutes a concept, and apply a systematic approach to the modelling of relations. Ontologies explicitly define diverse types of relations, and are by their nature machine-processable. The paper concludes that the potential of both the expressiveness and machine processability of each KOS is extensively regulated by its structural rules. It is harder to represent subject headings and classification schemes as semantic networks with nodes and arcs, while thesauri are more suitable for such a representation. In addition, a paradigm shift is revealed which focuses on the modelling of relations between concepts, rather than the concepts themselves.

How to Find Your Scents & Sensuality in the Digital Age - Duration: 33 minutes. How to Find Your Scents & Sensuality in the Digital Age - Duration: 33 minutes. How to Find Your Scents & Sensuality in the Digital Age - Duration: 33 minutes. How to Find Your Scents & Sensuality in the Digital Age - Duration: 33 minutes. How to Find Your Scents & Sensuality in the Digital Age - Duration: 33 minutes.

Machine Learning is evidently a vast field and its study is one of the most enlightening tasks one could ever undertake. Today most of the business operations and innovations are done around ML and its innovative applications. A number of professionals are up-skilling themselves with advanced ML knowledge to thrive ahead in their respective fields. They are more keen on learning the offerings, advancements, experts' opinion and various nuances in context to machine learning or artificial intelligence (AI) as a whole. If you are tech-enthusiast and looking forward to learning some new ideas and innovations about machine learning, you can find plenty of comprehensive books that demonstrate and offer various skills, advice and learning opportunities.