Machine Learning and Generative AI

Machine Learning (ML) and Generative AI represent two distinct but related branches of artificial intelligence, each with unique applications and implications for economists and policymakers. ML  primarily focuses on prediction, causal, and classification tasks using structured data. It excels at analysing large datasets to uncover insights, make forecasts, and support decision-making processes. In economic contexts, ML is particularly useful for tasks such as assessing credit risk, detecting fraud, providing causal inference and optimizing resource allocation. Policymakers can leverage ML to evaluate policy impacts, target interventions more effectively, and improve the efficiency of public services.

Generative AI, on the other hand, is designed to create new content, whether text, images, or other forms of data. It builds on ML techniques but goes a step further by generating original outputs rather than just analyzing existing data. For economists and policymakers, Generative AI opens up new possibilities in areas such as natural language processing, scenario generation, and automated report writing. It can be used to simulate economic scenarios, generate policy briefs, or create synthetic datasets for research purposes. Large Language Models (LLMs) like GPT-4 are a prime example of Generative AI, capable of engaging in complex dialogues, answering questions, and even assisting in code writing or data analysis tasks.

While Machine Learning models typically require structured data and specific training for each task, Generative AI models like LLMs can work with unstructured data and adapt to a wide range of tasks with minimal additional training, demonstrated in the case of few-shot learning. This flexibility  makes Generative AI particularly powerful for dealing with the complex, multifaceted challenges often faced in economics and policy-making. However, it's important to note that Generative AI also brings new challenges, such as the potential for generating misinformation or biased content, which requires careful consideration in policy and research contexts.

As these technologies continue to evolve, economists and policymakers will need to understand the strengths, limitations, and ethical implications of both Machine Learning and Generative AI to harness their full potential while mitigating risks.

Additionally, several global factors are complicating the outlook for emerging markets. High oil prices, a slow recovery in China, and geopolitical instability in the Middle East have further strained these economies. While the Bloomberg Emerging Markets Capital Flow Proxy Index—tracking capital flows into and out of EMs—has not yet raised major concerns, the Citi Emerging Markets Macro Risk Index has shown an increase in investor perceptions of risk, which could further amplify capital outflows (Bloomberg, 2024). Historically, a higher perception of macroeconomic risk correlates strongly with outflows, suggesting that EMs may face increasing challenges if their economic conditions continue to deteriorate.

Dr Melvyn Weeks is a senior lecturer and fellow of Clare College, Cambridge University. Dr Weeks is an assistant editor of the Journal of Applied Econometrics, as well as an associate at Cambridge Econometrics. His work has been published in The Economic Journal, Journal of the American Statistical Association, Journal of Applied Econometrics, European Economic Review, Computational & Economics.