## November 23, 2021

### Compositional Thermostatics (Part 1)

#### Posted by John Baez

At the Topos Institute this summer, a group of folks started talking about thermodynamics and category theory. It probably started because Spencer Breiner and my former student Joe Moeller, both working at the National Institute of Standards and Technology, were talking about thermodynamics with some people there. But I’ve been interested in thermodynamics for quite a while now — and Owen Lynch, a grad student visiting from the University of Utrecht, wanted to do his master’s thesis on the subject. He’s now working with me. Sophie Libkind, David Spivak and David Jaz Myers also joined in: they’re especially interested in open systems and how they interact.

Prompted by these conversations, a subset of us eventually wrote a paper on the foundations of equilibrium thermodynamics:

- John Baez, Owen Lynch and Joe Moeller, Compositional thermostatics.

## November 17, 2021

### Large Sets: The Movie

#### Posted by Tom Leinster

Earlier this year, I wrote a series of blog posts on large sets — or large cardinals, if you prefer — in categorical set theory. Thinking about large sets in Glasgow’s beautiful green spaces, writing those posts, and chatting about them with people here at the Café was one of the highlights of my summer.

Juan Orendain at the Universidad Nacional Autónoma de México was kind enough to invite me to give a talk in their category theory seminar, which I did today. I chose to speak about large sets, first giving a short introduction to categorical set theory, and then explaining some of the key points from this summer’s blog posts.

You can watch the video or read the slides.

## November 8, 2021

### Causality in Machine Learning

#### Posted by David Corfield

Back when we started the Café in 2006, I was working as a philosopher embedded with a machine learning group in the Max Planck Institute in Tübingen. Here I am reporting on my contribution to a NIPS workshop, held amongst the mountains of Whistler, on how one may still be able to learn when the distributions from which data is drawn for training and testing purposes differ. My proposal was that background knowledge, much of it causal, had to be deployed. It turns out that a video of the talk is still available – links to this and the resulting book chapter, *Projection and Projectability*, are here.

I was reminded of this work recently after seeing the strides taken by the machine learning community to integrate causal graphical models with their statistical techniques in Towards Causal Representation Learning and Causality for Machine Learning. Who knows? Perhaps my talk, which was after all addressed to some of these people, sowed a seed.