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Journal Article
New from the Richmond Fed’s Regional Matters blog
Journal Article
New disruption from artificial intelligence exposes high-skilled workers
With workers still grappling with the consequences of automation, the lightning-speed pace of artificial intelligence (AI) development poses fresh concerns of a new wave of worker displacement.
Journal Article
Opinion: Artificial Intelligence: Potentials and Prospects
We are at the dawn of a new technological revolution. The recent development of artificial intelligence (AI), especially the emergence of generative AI, has offered a plausible future in which machines will eventually free humans from a wide range of cognitive tasks, unleashing vast creativity and productivity gains.
Discussion Paper
Automation and AI: What Does Adoption Look Like for Fifth District Businesses?
Technological developments shift the kinds of skills needed in the labor force. From innovations in agriculture to electricity to the personal computer to the internet, technology has shaped the way we work and the types of workers we need to produce the goods and provide the services that consumers demand. The tight labor market of the last few years has provided employers with further incentive to find ways to use automation to increase the productivity of existing workers and even reduce the need to hire more. The opportunities of artificial intelligence (AI), particularly generative AI, ...
Journal Article
Has AI Improved Productivity?
Research Spotlight on "Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform." Erik Brynjolfsson, Xiang Hui, and Meng Liu. National Bureau of Economic Research Working Paper No. 24917, August 2018.
Working Paper
Artificial Intelligence and Inflation Forecasts
We explore the ability of Large Language Models (LLMs) to produce in-sample conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor.
Discussion Paper
AI and the Labor Market: Will Firms Hire, Fire, or Retrain?
The rapid rise in Artificial Intelligence (AI) has the potential to dramatically change the labor market, and indeed possibly even the nature of work itself. However, how firms are adjusting their workforces to accommodate this emerging technology is not yet clear. Our August regional business surveys asked manufacturing and service firms special topical questions about their use of AI, and how it is changing their workforces. Most firms that report expected AI use in the next six months plan to retrain their workforces, with far fewer reporting adjustments to planned headcounts.
Working Paper
Artificial Intelligence and Inflation Forecasts
We explore the ability of Large Language Models (LLMs) to produce in-sample conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor.
AI and Productivity Growth: Evidence from Historical Developments in Other Technologies
An analysis of the diffusion of PCs, smart devices, cloud computing and 3D printing suggests that AI may spread in a pattern similar to those of PCs and cloud computing.
Working Paper
Artificial Intelligence and Inflation Forecasts
We explore the ability of Large Language Models (LLMs) to produce conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor. We argue that this method of generating forecasts is inexpensive and can be ...