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For more than 50 years, Davos, the annual meeting of the World Economic Forum, has been an important barometer of economic, political, social, and environmental issues affecting the future of the world. So, what topics are driving the agenda for Davos 2022? The global supply chain crisis has taken on a new meaning. As the pandemic spread rapidly in 2020 and lingered in 2021, the general consensus was that disruptions to the global supply chain would be temporary albeit costly. But in 2022, it is clear that fragile supply chain may exist in a perpetual state of disruption for quite some time. In fact, the global supply chain was always in a fragile state; the pandemic laid bare just how vulnerable it was all along.
In the late nineteen-forties, Delmar Harder, a vice-president at Ford, popularized the term "automation"--a "nickname," he said, for the increased mechanization at the company's Detroit factory. Harder was mostly talking about the automatic transfer of car parts between machines, but the concept soon grew legs--and sometimes a robotic arm--to encompass a range of practices and possibilities. From the immediate postwar years to the late nineteen-sixties, America underwent what we might call an automation boom, not only in the automotive sector but in most heavy-manufacturing industries. As new technology made factory work more efficient, it also rendered factory workers redundant, often displacing them into a growing service sector. Automation looks a little different these days, but the rhetoric around it remains basically the same.
FinTech as we know it now is highly specialized and centralized. Blockchain and AI can be catalysts for FinTech 2.0 focusing on holistic solutions with increased transaction speeds, transparency, and security. Furthermore, DeFi may mean a larger pool of investors as more and more people gain access to financial markets. The more investors there are, the more data there will be that would be impossible to process without AI. Blockchain provides the foundation for smart contracts to improve transparency and data management, while AI may be leveraged to scale processes, accelerate transactions, and extract insights from large volumes of data.
There is no disputing the privacy trend. And it is one of the few issues in American life that crosses party lines. Data shows that 86% of people care about privacy for themselves and others -- with 79% willing to act on it by spending time and money to protect their data. And to those cynics who say people moan about privacy and do nothing, the same study found that 47% have taken action because of a company's data policies. What does this mean for the trillions of dollars that flow through the U.S. economy as a result of the very same privacy violations that are enraging consumers?
Gatestone Institute senior fellow Gordon Chang weighs in on Shanghai residents protesting COVID lockdowns on'Fox News Live.' In the spring of 2021, China was reporting only a few dozen COVID cases each day and celebrating a return to steady economic growth. The United States, meanwhile, reeled from its worst death wave of the pandemic. Media outlets around the world, from the Chinese Ministry of Propaganda to the New York Times, were quick to declare that China had "won" the pandemic, having decisively defeated the virus and demonstrated the virtues of unbridled autocracy. Xi Jinping was set to use China's apparent COVID success as a central argument for enshrining himself, at the upcoming Communist Party Congress in October 2022, as emperor-for-life.
The number of American workers who quit their jobs during the pandemic--over a fifth of the workforce--may constitute one of the largest American labor movements in recent history. Workers demanded higher pay and better conditions, spurred by rising inflation and the pandemic realization that employers expected them to risk their lives for low wages, mediocre benefits, and few protections from abusive customers--often while corporate stock prices soared. At the same time, automation has become cheaper and smarter than ever. Robot adoption hit record highs in 2021. This wasn't a surprise, given prior trends in robotics, but it was likely accelerated by pandemic-related worker shortages and Covid-19 safety requirements.
It is no secret that everybody wants to predict recessions. Many economists and finance firms have attempted this with limited success, but by and large there are several well known leading indicators for recessions in the US economy. However, when presented to the general public these indicators are typically taken alone, and are not framed in a way that can give probability statements associated with an upcoming recession. In this project, I have taken several of those economic indicators and built a classification model to generate probabilistic statements. Here, the actual classification ('recession' or'no recession') is not as important as the probability of a recession, since this probability will be used to determine a basic portfolio scheme which I will describe later on.
Artificial intelligence (AI) can be a powerful tool to help build a more inclusive economy.ljubaphoto It's no secret that the pandemic resulted in women and marginalized communities being ousted from the work force in record numbers. Though many demographic sectors have since bounced back, the gains remain unequal among traditionally under-represented groups. For example, employment in the accommodation and food service industries, which are traditionally staffed primarily by women, are still 17 per cent below pre-pandemic levels. And while the unemployment rate for racialized workers has returned to pre-pandemic levels, it's still higher than that of non-racialized workers.
Anxiety about automation is prevalent in this era of rapid technological advances, especially in artificial intelligence (AI), machine learning (ML), and robotics. Accordingly, how human labor competes, or cooperates, with machines in performing a range of tasks (what we term "the race between human labor and machines") has attracted a great deal of attention among the public, policymakers, and researchers.14,15,18 While there have been persistent concerns about new technology and automation replacing human tasks at least since the Industrial Revolution,8 recent technological advances in executing sophisticated and complex tasks--enabled by a combinatorial innovation of new techniques and algorithms, advances in computational power, and exponential increases in data--differentiate the 21st century from previous ones.14 For instance, recent advances in autonomous self-driving cars demonstrate the way a wide range of human tasks that have been considered least susceptible to automation may no longer be safe from automation and computerization. Another case in point is human competition against machines, such as IBM's Watson on the TV game show "Jeopardy!" Both cases imply that some tasks, such as pattern recognition and information processing, are being rapidly computerized. Furthermore, recent studies suggest that robotics also plays a role in automating manual tasks and decreasing employment of low-wage workers.3,22
There are several technology and business forces in-play that are going to derive and drive new sources of customer, product and operational value. As a set up for this blog on the Economic Value of Data Science, let's review some of those driving forces. "Due to its ability to substantially improve productivity and boost economic output, Artificial Intelligence (AI) has the potential to increase economic growth rates by a weighted average of 1.7% and profitability rates by 38% across a variety of industries by 2035. Source: NorthBridge Consultants "The Artificial Intelligence Revolution: New Challenges & Opport…" Figure 1: Source: "The Artificial Intelligence Revolution: New Challenges & Opportunities" Data Science (Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning) holds the potential to exploit Big Data and IoT to create new sources of economic value (wealth). But what is the source of this economic value when the AI tools that are driving this economic growth (TensorFlow, Spark ML, Caffee2, Keras) are open source and equally available to all players?