NEW DELHI: India may have to forgo as much as $1.97 trillion in gross domestic product (GDP) growth promised by investment in intelligent technologies over the next decade if the country fails to bridge the skill gap, a new report from Accenture said on Monday. Advanced technologies such as artificial intelligence (AI), augmented/virtual reality (AR/VR) and Blockchain can enable rapid reskilling and upskilling at scale, said the report titled "Fueling India's Skill Revolution". These technologies can help people learn new skills quickly, efficiently and cost effectively, Accenture said. "We must offer more experiential on-the-job training and help people adopt life-long learning as their jobs are transformed. Digital tools and applications -- like artificial itelligence, analytics and blockchain -- will be essential in delivering these new learning approaches," said Rekha M Menon, Chairman and Senior Managing Director at Accenture in India.

The study of register in computational language research has historically been divided into register analysis, seeking to determine the registerial character of a text or corpus, and register synthesis, seeking to generate a text in a desired register. This article surveys the different approaches to these disparate tasks. Register synthesis has tended to use more theoretically articulated notions of register and genre than analysis work, which often seeks to categorize on the basis of intuitive and somewhat incoherent notions of prelabeled 'text types'. I argue that an integration of computational register analysis and synthesis will benefit register studies as a whole, by enabling a new large-scale research program in register studies. It will enable comprehensive global mapping of functional language varieties in multiple languages, including the relationships between them. Furthermore, computational methods together with high coverage systematically collected and analyzed data will thus enable rigorous empirical validation and refinement of different theories of register, which will have also implications for our understanding of linguistic variation in general.

Matrix product states (MPS), a tensor network designed for one-dimensional quantum systems, has been recently proposed for generative modeling of natural data (such as images) in terms of `Born machine'. However, the exponential decay of correlation in MPS restricts its representation power heavily for modeling complex data such as natural images. In this work, we push forward the effort of applying tensor networks to machine learning by employing the Tree Tensor Network (TTN) which exhibits balanced performance in expressibility and efficient training and sampling. We design the tree tensor network to utilize the 2-dimensional prior of the natural images and develop sweeping learning and sampling algorithms which can be efficiently implemented utilizing Graphical Processing Units (GPU). We apply our model to random binary patterns and the binary MNIST datasets of handwritten digits. We show that TTN is superior to MPS for generative modeling in keeping correlation of pixels in natural images, as well as giving better log-likelihood scores in standard datasets of handwritten digits. We also compare its performance with state-of-the-art generative models such as the Variational AutoEncoders, Restricted Boltzmann machines, and PixelCNN. Finally, we discuss the future development of Tensor Network States in machine learning problems.

During the twentieth century, discoveries in mathematical logic revolutionized our understanding of the very foundations of mathematics. In 1931, the logician Kurt Gödel showed that, in any system of axioms that is expressive enough to model arithmetic, some true statements will be unprovable1. And in the following decades, it was demonstrated that the continuum hypothesis -- which states that no set of distinct objects has a size larger than that of the integers but smaller than that of the real numbers -- can be neither proved nor refuted using the standard axioms of mathematics2–4. They identify a machine-learning problem whose fate depends on the continuum hypothesis, leaving its resolution forever beyond reach. Machine learning is concerned with the design and analysis of algorithms that can learn and improve their performance as they are exposed to data.

Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex techniques. In large data sets, however, machine learning methods shine. New analytic approaches are needed to make the most of Big Data in economics. Researchers and policymakers should thus pay close attention to recent developments in machine learning techniques if they want to fully take advantage of these new sources of Big Data. Complex data are now available, characterized by large volume, fast velocity, diverse varieties, and the ability to link many data sets together. Powerful new analytic techniques derived from machine learning are increasingly part of the mainstream econometric toolbox. Big Data allows for better prediction of economic phenomena and improves causal inference. Machine learning techniques allow researchers to create simple models that describe very large, complex data sets.

Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex techniques. In large data sets, however, machine learning methods shine. New analytic approaches are needed to make the most of Big Data in economics. Researchers and policymakers should thus pay close attention to recent developments in machine learning techniques if they want to fully take advantage of these new sources of Big Data. Complex data are now available, characterized by large volume, fast velocity, diverse varieties, and the ability to link many data sets together. Powerful new analytic techniques derived from machine learning are increasingly part of the mainstream econometric toolbox. Big Data allows for better prediction of economic phenomena and improves causal inference. Machine learning techniques allow researchers to create simple models that describe very large, complex data sets.

How smart is the form of artificial intelligence known as deep learning computer networks, and how closely do these machines mimic the human brain? They have improved greatly in recent years, but still have a long way to go, a team of UCLA cognitive psychologists reports in the journal PLOS Computational Biology. Supporters have expressed enthusiasm for the use of these networks to do many individual tasks, and even jobs, traditionally performed by people. However, results of the five experiments in this study showed that it's easy to fool the networks, and the networks' method of identifying objects using computer vision differs substantially from human vision. "The machines have severe limitations that we need to understand," said Philip Kellman, a UCLA distinguished professor of psychology and a senior author of the study.

In an interview with HR Technologist, I explain how employers can better the employee experience and help employees complete their jobs more efficiently using artificial intelligence (AI). Embedding AI into a company's unique Employee Value Proposition is necessary to deliver on the talent brand and to drive engagement. Employers can leverage AI to create differentiated employee experiences in a variety of ways, which will only continue to evolve. We already see employers using AI to develop personalized compensation packages based on employee's role, preferences and productivity; uncover insights on workplace trends and challenges, create stronger rewards programs and eliminate menial tasks. Millennials and Gen Z are digital natives, accustomed to convenience and desire by meaningful experiences.

When Lockport City School District in New York announced it was planning on using the technology, the New York Civil Liberties Union came out against the move, calling the cameras "invasive and error-prone," and raising concerns about children's privacy. The American Civil Liberties Union of Arkansas similarly condemned a move by an Arkansas school district to install cameras equipped with facial recognition, nothing they could be vulnerable to hacking.

An organization's success is dependent on the high performance of its employees who behave as catalysts for business disruption and transformation. Today's workforce is increasingly globally dispersed, multi-generational, and multicultural. Effective management of any dynamic workforce requires an integrated and consistent approach to help drive innovation, collaboration, continuous learning, efficiency, retention, quality and cost savings. Managing a multi-gen workforce is a global phenomenon across industries. Artificial Intelligence (AI) is ubiquitously present and promises to transform both the HR function and the future of work.