Sunday, June 24

AI&ML abstract: The AI economy in stats and labour policy options

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Researchers reviewed the evidence that artificial intelligence is having a large effect on the economy. Across a variety of statistics—including robotics shipments, AI startups, and patent counts—there is evidence of a large increase in AI-related activity.

Aggregate statistics provide ample evidence that the deployment and use of AI and other advanced technologies have increased over the past decade.

The AI Index, a non-profit project designed to track activity and progress in AI, provides a number of interesting facts designed to track the scientific progress in and impact of artificial intelligence and robotics.

For example, academic papers focused on AI have increased nine times since 1996; in comparison, computer science papers have increased six times since the same time.

The number of students enrolled in artificial intelligence and machine learning courses at Stanford has increased 11 times since 1996; similar
trends are observed at other universities including UC Berkeley, University of Illinois, Georgia Tech, and others.

The share of jobs requiring AI skills has increased almost five times since 2013 (and growth is especially rapid in Canada and the UK). There appears to be particularly high demand for workers with machine learning or deep learning skills.

By many measures, investment in AI, both by established firms and by venture capitalists and startups, has increased. The McKinsey Global Institute estimated in a 2017 report that established firms spent between $18 and $27 billion on internal corporate investment in AI-related projects in 2016.

Such firms also spend money on AI-related investments in the form of acquisitions. Facebook, Google, Amazon and Apple have bought up hundreds of innovative startups over the past decade, including ones that focus on AI or AI-related technologies.

MGI also notes that established firms spent $2 to $3 billion on AI-related M&A in 2016 alone.

While less in dollar value, investment in AI-related startups has also been increasing. An analysis of Crunchbase data by Himel and Seamans (2016) indicates an increase in venture capital funding that begins in 2012 and then accelerates sharply in 2014.

This observation corroborates findings reported in the MGI Report (2017) that venture capital investment in AI startups grew by 40 percent between 2013 and 2016.

Jason Furman from Harvard Kennedy School’s Peterson Institute for International Economics and Robert Seamans from New York University’s Leonard N. Stern School of Business, also reviewed recent research in this area which suggests that AI and robotics have the potential to increase productivity growth but may have mixed effects on labour, particularly in the short run.

In particular, some occupations and industries may do well while others experience labour market upheaval.

They then consider current and potential policies around AI that may help to boost productivity growth while also mitigating any labour market downsides including evaluating the pros and cons of an AI specific regulator, expanded antitrust enforcement, and alternative strategies for dealing with the labour-market impacts of AI, including universal basic income and guaranteed employment.

Read the full academic paper


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