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Global Big Data Conference

#artificialintelligence

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

ZDNet

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

arXiv.org 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

#artificialintelligence

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

#artificialintelligence

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.


5 Key Challenges In Today's Era of Big Data

#artificialintelligence

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.


5 Key Challenges In Today's Era of Big Data

#artificialintelligence

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.


Global Big Data Conference

#artificialintelligence

According to predictions by PwC, artificial intelligence (AI) will add a staggering US$16 trillion to the global economy by 2030. To put things into perspective: The gross domestic product (GDP) of Singapore and Hong Kong-based on 2018 figures are just below US$400 billion each. Even China's GDP of US$13 trillion in 2018 is lower. For all the rosy promises, however, a couple of reports this week on the state of AI may put a dampener on the next AI-touting startup. In a report on The Economist, Tom Gauld calls out what he sees as the technology hitting a wall.


6 AI Trends That Will Drive Economic Recovery As States Reopen

#artificialintelligence

"The Golden Age of Biology is upon us," said Mayfield Fund's Navin Chaddha commenting on CRISPR gene ... [ ] editing With economic indicators not seen since the Great Depression (U.S. unemployment at 18%, GDP Q1 contraction at 4.8%, forecasted to surpass 30% for Q2) and the U.S. stock market propped up by trillions of dollars in government stimulus and soaring toward record highs, this is either the worst of times, as Fed Chair Powell declared in his April 28 monetary policy address, or the best of times to come. As more than half of U.S. states begin to reopen from the COVID-19 shutdown, one thing remains clear for now: The sharing economy is dead. Long live the distancing economy. To get an understanding of the role that AI will play in driving growth amid the global coronavirus crisis, I had a chance to talk with Navin Chaddha, managing partner of Mayfield Fund and Forbes Midas List investor, on the post-pandemic outlook for private equity investment. He shared Mayfield's thesis for their newest early stage funds which closed at $750 million on March 25, just as 90% of the world was locking down.


Will There Be An AI Productivity Boom?

#artificialintelligence

Can artificial intelligence ever boost productivity of firms and industries the way the PC and ... [ ] networking did in the '80s and '90s? A big pastime of economists in the 1980s and 1990s was trying to gauge how much corporate and industrial productivity would benefit from the then-novel phenomena of personal computers, workgroup servers, and computer networking. At first it was hard to see, but in time, economists did indeed find evidence that information technology contributed to boosting economic productivity. It's too soon to expect to see data showing a similar boom from artificial intelligence, today's big IT revolution. The technology is just becoming industrialized, and many companies have yet to even try to use things such as machine learning in any significant way.