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Big Data Engineer vs AI Engineer: Which Career is Better?


'A domain for the nerds,' this is what technology was called in the late 1900s. However, a lot of things changed in the 21st century. Owing to the drastic surge in the implementation of artificial intelligence, the technology market has opened its door to more jobs. On the other hand, big data is also bringing many organizational changes to companies. Big data was previously seen as useless content occupying most of the memory in data centers.

Advantages and Disadvantages of Artificial Intelligence and What Does the Future Hold? - Big Data Analytics News


Artificial intelligence aims at stimulating human reasoning in machines. There are advantages and disadvantages associated with artificial intelligence that will be listed in this context. AI technology has made it possible to solve complex problems. For instance, AI technology can aid medical practitioners in detecting ailments such as cancer. Also, AI technology can ensure you have access to insider trade news.

Is FinTech Reliable? The Future of AI in Financial Sectors


The financial industry is revolutionized with the integration of artificial intelligence. It not only enhances the precision level but also speeds up the query resolution period. With the help of enhanced efficiency and accuracy, human problems are solved with the help of AI. A broad range of advanced technology, including Artificial Intelligence (AI), Machine Learning and Neural Networks, Evolutionary Algorithms, and Big Data Analytics, has allowed computers to cruise diverse, and profound data sets. But one question should be a subject of discussion: is this man-made technology actually reliable or not?

How to Upskill Your Staff for AI and Machine Learning - InformationWeek


Organizations are discovering how artificial intelligence and machine learning can transform their business. AI's contribution to global GDP is expected to grow from $2 trillion in 2019 to $15 trillion in 2030 according to PwC. Every organization needs professionals to digest data and translate it into action, but the labor market is woefully unprepared to meet the exponential growth in demand. How do we start the AI revolution without any revolutionaries? Sometimes the answer lies within.

DataOps for Societal Intelligence: a Data Pipeline for Labor Market Skills Extraction and Matching Artificial Intelligence

Big Data analytics supported by AI algorithms can support skills localization and retrieval in the context of a labor market intelligence problem. We formulate and solve this problem through specific DataOps models, blending data sources from administrative and technical partners in several countries into cooperation, creating shared knowledge to support policy and decision-making. We then focus on the critical task of skills extraction from resumes and vacancies featuring state-of-the-art machine learning models. We showcase preliminary results with applied machine learning on real data from the employment agencies of the Netherlands and the Flemish region in Belgium. The final goal is to match these skills to standard ontologies of skills, jobs and occupations.

Global Big Data Conference


Well, is artificial intelligence a job-killer or not? We keep hearing both sides, from projections of doom for many professions that will necessitate things such as universal basic income to help sidelined workers, to projections of countless unfilled jobs needed to build and manage AI-powered enterprises. For a worker losing his or her job to automation, knowing that an AI programming job is being created elsewhere is of little solace. Perhaps the reality will be somewhere in between. An MIT report released at the end of last year states recent fears about AI leading to mass unemployment are unlikely to be realized.

Technology is about to destroy millions of jobs. But, if we're lucky, it will create even more


The next five years might see 85 million jobs displaced by new technologies, according to a new report from the World Economic Forum (WEF), although the trend could be balanced out by the creation of 97 million new roles – subject, however, to businesses and governments putting in extra efforts to upskill and retrain the workforce. While the adoption of technologies that automate human labor has been long-anticipated by analysts, who have predicted the start of the "Fourth Industrial Revolution" for years now, 2020 has come with its share of unexpected events, and they have greatly accelerated changes that could threaten the stability of the labor market sooner than expected. The COVID-19 pandemic has fast-tracked most businesses' digital transformation, bringing remote work into the mainstream but also sparking CIOs' interest in new technologies. Surveying 300 of the world's biggest companies, which together employ eight million people around the world, the WEF found that an overwhelming 80% of decision makers are planning on accelerating the automation of their work processes, while half are set to increase the automation of jobs in their company. Industries like finance, healthcare and transportation are showing renewed interest in artificial intelligence, while the public sector is keen to increase the use of big data, IoT and robotics.

The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data Artificial Intelligence

The current knowledge system of macroeconomics is built on interactions among a small number of variables, since traditional macroeconomic models can mostly handle a handful of inputs. Recent work using big data suggests that a much larger number of variables are active in driving the dynamics of the aggregate economy. In this paper, we introduce a knowledge graph (KG) that consists of not only linkages between traditional economic variables but also new alternative big data variables. We extract these new variables and the linkages by applying advanced natural language processing (NLP) tools on the massive textual data of academic literature and research reports. As one example of the potential applications, we use it as the prior knowledge to select variables for economic forecasting models in macroeconomics. Compared to statistical variable selection methods, KG-based methods achieve significantly higher forecasting accuracy, especially for long run forecasts.

Global Big Data Conference


Automation has been gradually transforming the workplace for years (think Excel spreadsheets or chatbots). As artificial intelligence (AI), machine learning and deep learning systems that can learn from each other become more prevalent and smarter (think Alexa or IBM Watson), they continue to replace more manual, repetitive job tasks. Consequently, automation and robots are changing more jobs globally at breakneck speed. A McKinsey Global Institute report suggests that between 400 million to 800 million jobs worldwide will be lost due to automation by 2030. The report claims that the U.S. could lose between 16 to 54 million jobs by 2030.

5 Key Challenges In Today's Era of Big Data


Digital transformation will create trillions of dollars of value. While estimates vary, the World Economic Forum in 2016 estimated an increase in $100 trillion in global business and social value by 2030. Due to AI, PwC has estimated an increase of $15.7 trillion and McKinsey has estimated an increase of $13 trillion in annual global GDP by 2030. We are currently in the middle of an AI renaissance, driven by big data and breakthroughs in machine learning and deep learning. These breakthroughs offer opportunities and challenges to companies depending on the speed at which they adapt to these changes.