A month of milliblogging
It took me twenty-five years, but I'm now convinced that blogging is good.
I started this substack experiment annoyed about how a lunatic billionaire had nuked my social network. I wasn’t sure what I was going to do.
I’m going to try it out and see how it goes. I will milliblog here. I’ll post Twitter threads but with better grammar. Maybe I’ll add some longer explanations and context. Maybe I’ll add some links to what I’m reading and listening to. (In case it wasn’t clear from the title, right now I’m listening to King Gizzard & The Lizard Wizard.) But I am going to try to write here without obsessing about every tiny detail of the presentation.
It went way better than I had expected. Invigorated by the process of writing, I wrote a week later:
What if I can get at that book I had initially envisioned by laying out a diary of half-baked thoughts here? I’m up for experiments. I’m going to try to post every day of July. I’ll revisit that schedule on August 1.
And I managed to keep writing something every day in July. I know July has 31 days, but Mondays are for content and Sundays are for navel gazing. I promise content will come tomorrow. But let me revisit my schedule today, and try to pin down some plans for the future.
I’m not sure I can keep up the Gelman-esque momentum of daily posting. But this writing practice is invaluable. And it’s more productive than Twitter. And I really don’t want to use Threads. This is the first writing exercise I’ve made for myself where I’ve been able to commit every day. And, just like when I was 14 and forced myself to practice three-finger patterns on the fretboard, I’m developing some new reflexes for runs in writing. I can now consistently write about 1000 words an hour. That’s crazy to me.
I only hit a wall one day so far. I couldn’t get a piece to come together how I wanted, and it was beyond frustrating. But one of the most important things I’ve learned from my barbell training odysseys is that some days are bad, and not every bad day needs an over-analysis. One of the best things you can do is put it behind you and come back tomorrow. This attitude works for so many things beyond the gym.
Here’s a rough sketch of what I hope to do here in the coming months. Next week I’ll continue my daily pace, further unpacking my thoughts on what exercise science can teach us about inference. The week after, I’m traveling and expect to be unable to post every day. But I’m going to do my best to see what’s possible on the road.
My trip is to the Machine Learning for Healthcare meeting in New York. I’m participating in two sessions and will do my best to share my experiences here. The first is a workshop on how observational causal inference can help machine learning for health care. Guess what my position is! This workshop is forcing me to tighten up my arguments, and I look forward to hearing the counterarguments from the other participants. In the second session, I’m going to talk about the limits of the optimization mindset in individual and population health, drawing on several historical examples. I am excited to share thoughts on that here as well.
Looking even further ahead, I’m teaching our graduate introduction to machine learning again this fall. For whatever reason, I can’t get myself to teach this course the same way for too long. I rewrote the syllabus last week and think I have a newly compelling story this year. I’m going to be teaching from the book Moritz and I wrote, but I want to teach the course with a critical eye toward the 2nd edition. Which parts of the book do I still agree with? On which parts have I changed my mind? I will blog lecture summaries every Tuesday and Thursday, and I hope to continue the discussion here.
And I guess we’ll revisit where this substack is at around winter break. I appreciate all the feedback you’ve sent me on my writing thus far. Please keep me honest as this milliblogging moves forward. Let’s see where this goes.
Gelman pre-posts his posts a week, even weeks, in advance! I would link to his post on this but I can't find it. Good luck! This is definitely better than Twitter and I'm learning a lot.
This blog is packed.sorry for my terse response: Eagerly awaiting your blogs on the workshop. In two weeks, I’ll begin teaching, for the fourth time, my (large) introductory course on stat ML here at ASU. Thanks for the book plug; I’ll be checking it out (which I am sorry but I wasn’t aware of!) and may request a copy.