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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.
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.
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 ...
Journal Article
Federal Reserve: Artificial Intelligence and Bank Supervision
Artificial intelligence has come a long way since English mathematician, logician, and cryptographer Alan Turing's seminal 1950 essay, "Computing Machinery and Intelligence," which explored the idea of building computers capable of imitating human thought. In 1997, almost 50 years after Turing's essay, AI posted a historic breakthrough when the IBM supercomputer Deep Blue won a chess match against reigning world champion Garry Kasparov. Since then, AI's capabilities have improved rapidly, largely through advances in machine learning (ML), especially in ML models that use digital neural ...
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 ...