Category: Artificial Intelligence

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X Reasons for policy professionals to get into programming in 202X

In 2019 I wrote a post with seven reasons policy professionals should learn to code. Now that AI can write reasonable code, I’ve had a number of people ask me if it’s still worth learning to program. Based on the evidence I’ve seen, AI appears to be a an amplifier, rather than a substitute for human intelligence in the policy space: meaning the people that get the most out of AI are likely to be those that know how to instruct AI to tackle a problem and tell good output from bad. So my answer is yes, learning to code still makes sense. Not necessarily so you can write the code yourself, but to expand the quantity, scope and quality of what you're capable of.

From code to conversation: Localized AI fun with LM Studio and the ellmer package

Summary: in this post I demonstrate how you can interact with locally hosted LLM models in R using the ellmer package and LM Studio.

How I ChatGPT: Utilizing ChatGPT as a Public Policy Professional

In this post I provide some of my insights into using AI tools, such as ChatGPT, as a public policy professional and applied economist.