The pace of technological change is rendering many job activities -- and the skills they require -- obsolete. Research by McKinsey suggests that globally more than 50% of the workforce is at risk of losing their jobs to automation, and a survey by the World Economic Forum suggests that 42% of the core job skills required today will change substantially by 2022. In this landscape of constant disruption, individuals, companies, and governments are fighting to ensure they have the skills to remain competitive. To shed light on the global skills landscape, Coursera recently released the first edition of our Global Skills Index (GSI) report. As the world's largest platform for higher education, Coursera brings together 40 million learners around the world with over 3,000 courses from leading universities and companies.

Machine Learning (ML) provides methods, techniques, and tools that can help solving diagnostic and prognostic problems in a variety of medical domains. The rising technology in Machine Learning in Medicine market is also depicted in this research report. Factors that are boosting the growth of the market, and giving a positive push to thrive in the global market is explained in detail. The report delivers a comprehensive overview of the crucial elements of the market and elements such as drivers, restraints, current trends of the past and present times, supervisory scenario, and technological growth. A thorough analysis of these elements has been accepted for defining the future growth prospects of the global Machine Learning in Medicine market.

National Workshops to Explore Long-range Strategies 7. Collect suggestions from the national planning workshops, distilled in to 93 actions, assess all via five (5) Real-Time Delphi's 8. Final Report for Public Discussion 10.

In today's world it's easy to get swept up in the cool side of Artificial Intelligence. Whether it be a new Amazon Echo, autonomous cars or perhaps the new Boston Dynamics Robot, which now has the amazing ability to open a door (just watch the video on YouTube, you'll see what I mean). However, whilst the cool stuff is put right in front of our eyes, for us to fascinate over it's easy to forget about what goes on behind the scenes of Artificial Intelligence. Now I'm not saying that the tech, gadgets and robots aren't cool, they're very cool. In fact, I'm sure I'll be the first in line to buy an autonomous flying car, when the time comes.

Unemployed Artificial Intelligence (AI) ex perts are as elusive as the abominable snowman of the Himalayas. In fact, in my AI career, which spans more than two decades, I have never met one. However, in the past year, ever since the media woke up to the amazing world of AI, I have been receiving queries like: since AI is a new thing, will I find a job once I graduate? I've never seen an advert for an AI expert, etc. In the past decades, and especially in the past year in my role as a member of the Malta.AI taskforce, I have met thousands of people who might have heard about AI, are probably using it extensively, but have no clue that they're using it or even what it is.

In January-March 2019 The Economist Intelligence Unit, on behalf of Temenos, surveyed 405 global banking executives on the changes they see taking place in their industry to 2020 and 2025, their organisational response, and the longer-term impact on their strategic development. This, the sixth iteration of the retail banking survey, focuses on how these retail banks are incorporating and advancing technology delivery for their current and future customers. The survey is part of a global research programme on retail banking, which includes in-depth interviews with retail banks, fintechs and regulators from North America, Europe, Africa and the Middle East, Asia-Pacific, and Latin America. The survey respondents were geographically diverse: 25% were drawn from Europe, 25% from Asia-Pacific, 18% from North America, 16% from Africa and the Middle East, and 16% from Latin America. Respondents came from a variety of job functions: marketing and sales (18%), IT (15%), and customer service and finance each accounted for about one in ten respondents (9% and 10% respectively).

AI in Oil & Gas market is expected to grow from an estimated $1.57 billion in 2017 to $2.85 billion by 2022, at a CAGR of 12.66%, from 2017 to 2022. The growth of AI in Oil & Gas market will be mainly driven by the rise in adoption of the big data technology in the Oil & Gas industry to augment E&P capabilities, a significant increase in venture capital investments, and growing need for automation in the Oil & Gas industry, and tremendous pressure to reduce production costs. Software in AI in Oil & Gas market is applicable in upstream Oil & Gas exploration and production activities. The hardware segment in AI in Oil & Gas market is expected to grow swiftly during the forecast period (2017 to 2022), mainly due to the increasing requirement for sophisticated hardware system configurations and components capable of handling massive data, including, but not limited to Tensor Processor Unit (TPU), Graphic Processing Unit (GPU), Resistive Processing Unit (RPU), Field Programmable Gate Array (FPGA), and Visual Processing Unit (VPU) to install software-based AI capabilities. The upstream AI in Oil and Gas Market is set to grow in the next five years.

The human brain is a funny thing. Certain memories can stick with us forever: the birth of a child, a car crash, an election day. But we only store some details--the color of the hospital delivery room or the smell of the polling station--while others fade, such as the face of the nurse when that child was born, or what we were wearing during that accident. For Google CEO Sundar Pichai, the day he watched AI rise out of a lab is one he'll remember forever. "This was 2012, in a room with a small team, and there were just a few of us," he tells me. An engineer named Jeff Dean, a legendary programmer at Google who helped build its search engine, had been working on a new project and wanted Pichai to have a look. "Anytime Jeff wants to update you on something, you just get excited by it," he says. Pichai doesn't recall exactly which building he was in when Dean presented his work, though odd details of that day have stuck with him. He remembers standing, rather than sitting, and someone joking about an HR snafu that had designated the newly hired Geoffrey Hinton--the "Father of Deep Learning," an AI researcher for four decades, and, later, a Turing Award winner--as an intern. The future CEO of Google was an SVP at the time, running Chrome and Apps, and he hadn't been thinking about AI.

As one of fundamental properties to characterize inconsistency measures for knowledge bases, the property of free formula independence well captures the intuition that free formulas are independent of the amount of inconsistency in a knowledge base for cases where inconsistency is characterized in terms of minimal inconsistent subsets. But it has been argued that not all the free formulas are independent of inconsistency in some other contexts of inconsistency characterization. In this paper, we propose a characterization of formulas independent of inconsistency in the framework of Priest's minimally inconsistent LP. Based on an atom-based counterpart of the notion of free formula, we propose a notion of Bi-free formula to describe formulas that are free from inconsistency in both syntax and paraconsistent models in this logic. Then we propose the property of Bi-free formula independence, which is more suitable for characterizing the role of formulas free from inconsistency in measuring inconsistency from both syntactic and semantic perspectives.

Large textual corpora are often represented by the document-term frequency matrix whose elements are the frequency of terms; however, this matrix has two problems: sparsity and high dimensionality. Four dimension reduction strategies are used to address these problems. Of the four strategies, unsupervised feature transformation (UFT) is a popular and efficient strategy to map the terms to a new basis in the document-term frequency matrix. Although several UFT-based methods have been developed, fuzzy clustering has not been considered for dimensionality reduction. This research explores fuzzy clustering as a new UFT-based approach to create a lower-dimensional representation of documents. Performance of fuzzy clustering with and without using global term weighting methods is shown to exceed principal component analysis and singular value decomposition. This study also explores the effect of applying different fuzzifier values on fuzzy clustering for dimensionality reduction purpose.