Sundar Pichai, the chief executive of Google, has been put in charge of its parent company Alphabet, after co-founders Larry Page and Sergey Brin announced they were stepping down. The 47-year-old said the pair had set up a "strong foundation" on which he would "continue to build". Pichai's life story is remarkable, and his rise to the top of Google is an endorsement of India's standing in the global technology industry - and equally, a reassuring reminder of the so-called "American Dream". Pichai was born and schooled in Chennai, India. He captained his school's cricket team, leading it to win regional competitions.

The major objective of the Cognitive Systems & Artificial Intelligence In BFSI market report is to help the user understand the market in terms of its definition, segmentation, market potential, influential trends, and the challenges that the market is facing. This research is conducted to understand the current landscape of the market, especially in 2019 up-to the forecast year 2025. The readers will find this report very helpful in understanding the Cognitive Systems & Artificial Intelligence In BFSI market in depth. The data and the information regarding the market are taken from reliable sources such as websites, annual reports of the companies, journals, and others and were checked and validated by the industry experts. The facts and data are represented in the report using diagrams, graphs, pie charts, and other pictorial representations.

Keyword extraction has received an increasing attention as an important research topic which can lead to have advancements in diverse applications such as document context categorization, text indexing and document classification. In this paper we propose STF-IDF, a novel semantic method based on TF-IDF, for scoring word importance of informal documents in a corpus. A set of nearly four million documents from health-care social media was collected and was trained in order to draw semantic model and to find the word embeddings. Then, the features of semantic space were utilized to rearrange the original TF-IDF scores through an iterative solution so as to improve the moderate performance of this algorithm on informal texts. After testing the proposed method with 200 randomly chosen documents, our method managed to decrease the TF-IDF mean error rate by a factor of 50% and reaching the mean error of 13.7%, as opposed to 27.2% of the original TF-IDF.

Transportation occupies one-third of the amount in the logistics costs, and accordingly transportation systems largely influence the performance of the logistics system. This work presents an adaptive data-driven innovative modular approach for solving the real-world Vehicle Routing Problems (VRP) in the field of logistics. The work consists of two basic units: (i) an innovative multi-step algorithm for successful and entirely feasible solving of the VRP problems in logistics, (ii) an adaptive approach for adjusting and setting up parameters and constants of the proposed algorithm. The proposed algorithm combines several data transformation approaches, heuristics and Tabu search. Moreover, as the performance of the algorithm depends on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constants according to historical data is proposed. A comparison of the acquired results has been made using the Decision Support System with predictive models: Generalized Linear Models (GLM) and Support Vector Machine (SVM). The algorithm, along with the control parameters, which using the prediction method were acquired, was incorporated into a web-based enterprise system, which is in use in several big distribution companies in Bosnia and Herzegovina. The results of the proposed algorithm were compared with a set of benchmark instances and validated over real benchmark instances as well. The successful feasibility of the given routes, in a real environment, is also presented.

In September last year, a video made the rounds of the Internet showing an astronaut taking giant slow-motion leaps on what appeared similar to the surface of the Moon. However, the parody was highlighted soon when an auto rickshaw was seen passing nearby tumbling across the unstructured road filled with potholes. While the video taken by a Bengaluru artist left many netizens in splits, the artist's unique way of shedding light into the city's perennial pothole problem was lauded heavily. These deaths were out of 9423 accidents that year, in which 8792 people suffered grievous injuries such as bone fractures and slip discs. Adding insult to injury, the number of road accidents due to potholes was unfortunately more than the fatalities caused by the terrorist attacks, noted the Supreme Court.

The Artificial Intelligence market has witnessed growth from USD XX million to USD XX million from 2014 to 2019. With the CAGR of X.X%, this market is estimated to reach USD XX million in 2026. The report mainly studies the size, recent trends and development status of the Artificial Intelligence market, as well as investment opportunities, government policy, market dynamics (drivers, restraints, opportunities), supply chain and competitive landscape. Technological innovation and advancement will further optimize the performance of the product, making it more widely used in downstream applications. Moreover, Porter's Five Forces Analysis (potential entrants, suppliers, substitutes, buyers, industry competitors) provides crucial information for knowing the Artificial Intelligence market.

A deep neural network (DNN) with piecewise linear activatio ns can partition the input space into numerous small linear regions, where diffe rent linear functions are fitted. It is believed that the number of these regions rep resents the expressivity of the DNN. This paper provides a novel and meticulous perspe ctive to look into DNNs: Instead of just counting the number of the linear regio ns, we study their local properties, such as the inspheres, the directions of t he corresponding hyper-planes, the decision boundaries, and the relevance of the su rrounding regions. W e empirically observed that different optimization techniq ues lead to completely different linear regions, even though they result in similar cl assification accuracies. W e hope our study can inspire the design of novel optimizatio n techniques, and help discover and analyze the behaviors of DNNs. In the past few decades, deep neural networks (DNNs) have ach ieved remarkable success in various difficult tasks of machine learning (Krizhevsky et al., 2012; Graves et al., 2013; Goodfellow et al., 2014; He et al., 2016; Silver et al., 2017; Devlin et al., 2019). Albeit the great progress DNNs have made, there are still many problems which have not been thoro ughly studied, such as the expressivity and optimization of DNNs. High expressivity is believed to be one of the most important reasons for the success of DNNs. It is well known that a standard deep feedforward network with pie cewise linear activations can partition the input space into many linear regions, where different li near functions are fitted (Pascanu et al., 2014; Montufar et al., 2014). More specifically, the activat ion states are in one-to-one correspondence with the linear regions, i.e., all points in the same li near region activate the same nodes of the DNN, and hence the hidden layers serve as a series of affine transformations of these points.

As we enter our third decade in the 21st century, it seems appropriate to reflect on the ways technology developed and note the breakthroughs that were achieved in the last 10 years. The 2010s saw IBM's Watson win a game of Jeopardy, ushering in mainstream awareness of machine learning, along with DeepMind's AlphaGO becoming the world's Go champion. It was the decade that industrial tools like drones, 3D printers, genetic sequencing, and virtual reality (VR) all became consumer products. And it was a decade in which some alarming trends related to surveillance, targeted misinformation, and deepfakes came online. For better or worse, the past decade was a breathtaking era in human history in which the idea of exponential growth in information technologies powered by computation became a mainstream concept.

Artificial intelligence (AI) is now receiving unprecedented global attention as it finds widespread practical application in multiple spheres of activity. But what are the human rights, social justice and development implications of AI when used in areas such as health, education and social services, or in building "smart cities"? How does algorithmic decision making impact on marginalised people and the poor? This edition of Global Information Society Watch (GISWatch) provides a perspective from the global South on the application of AI to our everyday lives. It includes 40 country reports from countries as diverse as Benin, Argentina, India, Russia and Ukraine, as well as three regional reports.

Tourism information is scattered around nowadays. To search for the information, it is usually time consuming to browse through the results from search engine, select and view the details of each accommodation. In this paper, we present a methodology to extract particular information from full text returned from the search engine to facilitate the users. Then, the users can specifically look to the desired relevant information. The approach can be used for the same task in other domains. The main steps are 1) building training data and 2) building recognition model. First, the tourism data is gathered and the vocabularies are built. The raw corpus is used to train for creating vocabulary embedding. Also, it is used for creating annotated data. The process of creating named entity annotation is presented. Then, the recognition model of a given entity type can be built. From the experiments, given hotel description, the model can extract the desired entity,i.e, name, location, facility. The extracted data can further be stored as a structured information, e.g., in the ontology format, for future querying and inference. The model for automatic named entity identification, based on machine learning, yields the error ranging 8%-25%.