6 Accelerating productivity: Machine-augmented work

 

This chapter covers

  • Using LLMs in professional and personal settings
  • Discussing the use and misuse of generative AI tools in education
  • Exploring methods to detect machine-generated content
  • Examining the overall economic effect of generative AI tools

Everyone has, at some point in their life, experienced what in positive psychology is known as the concept of flow: you’re deeply absorbed in what you’re working on and perhaps lose track of time because you’re so focused. And, most likely, you also experienced sudden interruptions, maybe the need to look something up or attend to something else, that break the flow. This frustration was top of mind for then GitHub CEO Nat Friedman when he announced the release of GitHub’s coding assistant, Copilot. “It helps you quickly discover alternative ways to solve problems, write tests, and explore new APIs without having to tediously tailor a search for answers on the internet,” Friedman wrote [1]. Integrating into Microsoft’s code editor, Visual Studio Code, was a crucial component: Copilot would plug directly into coders’ existing workflows.

Using LLMs in the professional space

LLMs assisting doctors with administrative tasks

LLMs for legal research, discovery, and documentation

LLMs augmenting financial investing and bank customer service

LLMs as collaborators in creativity

LLMs as a programming sidekick

LLMs in daily life

Generative AI’s footprint on education

Detecting AI-generated text

How LLMs affect jobs and the economy

Summary