I haven't blogged yet this year. As always, not having a consistent schedule I feel obligated to fulfill tends to lead to me not doing things. But I figured I'd follow up on my end-of-year post, check in on what's been going on and where I'm at on the goals I set at the end of 2022, and maybe set myself up for a few follow-ups.

So Far This Year

January

It's been a wild few months! I forced myself not to start any new projects or another hundred days during January, despite getting bored out of my skull, because

Intentional and Unintentional Machine Learning Failures

For the past few months, I've been working with machine learning and intentionally overfitting/breaking models. This is very much a work in progress, but is an attempt to explain why I think these phenomena are so interesting. I welcome your feedback as I continue to work through these ideas.

Overfitting is a well-documented phenomenon in machine learning, and one widely considered to be undesirable. It's what occurs when your model works too well, when it can perform perfectly on a training set but can't respond to new data. This can often result from

Code lives here.

Reading about detournement this week has me thinking of this meme by Trevor Allen.

If, for Debord, detournement is about creating new webs of meaning, and robbing existing signs of their own meaning, then erasure seems like a powerful method of reframing recognizable corpuses (corpi?). I'm especially interested in his point that a detournement is less effective the more it approaches a rational reply. Rather than trying to "embrace debate" or critique our political enemies directly, the goal