Professor of economics Anton Korinek at the University of Virginia recommends his students start learning a burgeoning technology that is predicted to revolutionize the study of economics. Generative artificial intelligence, or “GenAI” for short, is that technology.
Machine learning, a subset of AI, is already used by economists to analyze data and create economic forecasts. However, genAI is a distinct technology. It serves as the foundation for ChatGPT and technologies that are similar to it, and it has recently made incredible strides.
According to an article he produced that was accepted for publication by the Journal of Economic Literature, Korinek anticipates it will “revolutionize research.”
GenAI can assist with more than simply research. In a recent publication, two economists at George Mason University demonstrated how genAI can be used to teach economics by building tests and solving specific models in the classroom. The Federal Reserve Bank of St. Louis researchers also demonstrated in a separate study that an AI model they created “understands the request for inflation forecasts” and “provides an affordable and accurate alternative to traditional forecasts.”
Generic artificial intelligence (genAI) is more likely to empower economists than take their employment, at least for the time being, by helping with research projects and forecasting inflation.
In his study, Korinek makes the case that massive language models, a particular kind of genAI, may assist economists in six different ways when conducting research: “ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations.”
According to Korinek, the most popular GenAI programs include Microsoft’s New Bing, Google’s Bard, Anthropic’s Claude 2, and Meta’s LlaMA 2. By simply asking the chatbot to produce a list of ideas, any of those chatbots can assist economists in coming up with research topics. It can even assess research programs by outlining advantages and disadvantages.
GenAI excels at copyediting and polishing writing, including identifying errors, making title suggestions, and even creating content expressly for social media to advertise a paper. According to Korinek’s report, the new technology can make a researcher’s writing considerably more precise, clear, and fluid.
However, the study also demonstrated that GenAI is very helpful in the classroom.
According to a new working paper, GenAI is even more skilled at projecting inflation than the economists who do it now.
The research, written by two policy advisers at the St. Louis Fed, contrasted the Survey of Professional Forecasters, which happens to be one of the most reliable sources of macroeconomic forecasts.
So how might the development of genAI affect jobs in the field of economics? It will probably only have a little impact at first, mainly improving the productivity and efficiency of economists, but it may eventually result in some job losses.
The duties that economists execute with the help of a lot of technology are also tasks that genAI could accomplish, especially as it develops. The “human touch,” as Gudell puts it, will always be necessary in economics. That applies to working with GenAI itself as well as while instructing students and giving live lectures in front of an audience.
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