Artifactual Intelligence

Abstract

Artificial intelligence has seen a resurgence in the public’s attention thanks to innovations in large language models (LLMs). While every technological innovation comes with its own well-studied lifecycle from hype to normalcy, LLMs particular flavor of narrow artificial intelligence seems different. To an extent that may well be unique among technologies, today’s artificial intelligence is not understood – even by those who create it. In this paper we provide a perspective on artificial intelligence rooted in the history of humanity’s efforts to understand the world around them, from economic calculation to probabilistic machine learning. This perspective allows us to see what AI really is – not artifical, but artifactual – and how it might enable an epistemological revolution.

Project Organization

• /code Scripts with prefixes (e.g., 01_import-data.py, 02_clean-data.py) and functions in /code/src.
• /data Simulated and real data, the latter not pushed.
• /figures PNG images and plots.
• /output Output from model runs, not pushed.
• /presentations Presentation slides.
• /private A catch-all folder for miscellaneous files, not pushed.
• /renv Project library, once initialized (see below).
• /writing Case studies and the paper.
• /.venv Hidden project library, not pushed.
• .gitignore Hidden Git instructions file.
• .python-version Hidden Python version for the reproducible environment.
• renv.lock Information on the reproducible environment.
• requirements.txt Information on the reproducible environment.

Reproducible Environment

Python

After cloning this repository, go to the project’s terminal in Positron and run python -m venv .venv to create the /.venv project library, followed by pip install -r requirements.txt to install the specified library versions.

Whenever you install new libraries or decide to update the versions of libraries you use, run pip freeze > requirements.txt to update requirements.txt.

R

Add a reproducible environment by creating a project library using the {renv} package.

• Initialize the project library once using renv::init().
• Once you’ve installed packages, add them to the project library using renv::snapshot().
• If a project library already exists, install the associated packages with renv::restore().

For more details on using GitHub, Quarto, etc. see ASC Training.