CoCalc Exhibiting at AISTATS 2024

Our View from AISTATS: The Two-Toolbox Problem

We recently spent time at AISTATS, and what makes that conference so unique is that it’s where two distinct worlds collide: the rigor of classical statistics and the scale of modern AI.

One talk might be a deep dive into causal inference or Bayesian methods, and the very next could be about the statistical interpretability of a massive deep learning model. The whole event is focused on answering fundamental questions about AI through a statistical lens, tackling big issues like uncertainty quantification and building more transparent models.

In the hallways, this led to a lot of conversations about a common workflow challenge. Many researchers we spoke with feel like they're working with two separate toolboxes. They might do their heavy statistical lifting in R or with Python’s statsmodels, but then have to apply those insights to a neural network built in PyTorch or TensorFlow.

It’s a subtle but constant friction—moving data, translating code, and trying to get these two distinct environments to talk to each other seamlessly. As a team that thinks a lot about integrated scientific workflows, it was fascinating to see. It’s a classic problem: how do you make two toolboxes feel like one?

A huge thank you to the AISTATS organizers for creating a space where these two critical fields can meet and push each other forward.

— The CoCalc Team

cocalc.com