What I did last week: aesthetics, economics, Rorschach analogy for AI, default images, and “leveling”

I did some satisfying work last week. Here’s a quick rundown. I’m listing the posts in the order I wrote them.

Visual Aesthetics

A case of visual aesthetics: Why is the monochrome image superior to the color image?

The issue, black & white vs. color, has been and I suppose remains central to photography, and I deal with it there, a bit. But that’s not what I’m doing here. This is about the conversion of a particular ChatGPT image from color to black & white. It was a fun post to assemble and to think about. I like the suite of images.

Rank 5 Economics?

Beyond Marginalism: What’s Next? [MR #12]

This is my last word – save for an introduction I’ll write in a week or three, who knows? – on the fourth and final chapter of Cowen’s monograph on marginalism. This is where he tosses up some examples of leading edge work in economics, noting that it’s drifting away from marginalism into complex high-dimensional models created through machine learning. His examples come from finance. The new models yield better predictions.

I focus on one model that has 360,000 parameters and end up making (speculative) sense out of what’s going on. I suggest that those parameters are picking up the effects of Keynes’ “animal spirits” as expressed in the gossip and stories of Schiller’s narrative economics. I further suggest that we can test this by comparing the output of a classical model with that from a high-parameter machine learning model. The divergence should be highest with those stocks otherwise identified as meme stocks.

The prospect of empirical investigation into animal spirits in asset pricing [the fate of marginalism]

Here I take my speculations about how to test these high parameter models and present them to Marge, the AI associated with Cowen’s book. Marge approves.

Rorschach test for AI

More on how I’m approaching The God Test – Rorschach! [GT-2]

I came up with the Rorschach blot as analogy for the kind of challenge AI presents to us, to our understanding of AI and of the future. The idea is that the blot does have a form, albeit a complex one that’s not very legible. Hence our commentary on it (that is, on AI) tells as much about us as about AI. I’ll be developing this further in a later post.

Prototype Image in ChatGPT

This is a new working paper that opens up a whole new line of investigation. This was a fun piece of work. Writing it up took way longer than actually generating the images.

A prototypical image in ChatGPT 5.6: An informal pilot study

Abstract: Previous work has found strong default preferences in stories generated by large language models from minimally specified prompts. To determine whether a similar effect appears in image generation, I asked ChatGPT 5.6 to create 17 images in separate chats using four prompts that specified either no subject matter or only a rendering medium: “Create an image,” “Create a drawing,” “Create a painting,” and “Create a water color painting.” Sixteen of the 17 outputs depicted closely related landscapes containing mountains, trees, sky, and water; the remaining image depicted a lighthouse. The images also shared a calm, picturesque mood and contained no human figures, although several included signs of human habitation. Because the internal prompt passed to the image generator was not available, the study cannot determine whether these defaults arise primarily in the language model, the image generator, or their interaction. The results are exploratory but suggest that severely underspecified image prompts may reveal stable default preferences in the integrated ChatGPT image-generation system.

The “leveling” of knowledge in the compressed form of LLMs

NYTimes: AI needs human supervision in order to complete an entire job.

This is something I’ve been thinking about off and on for a while, but this is my first explicit framing of the issue. The idea is that once ideas or set of ideas has been expressed in writing and those documents then consumed into an LLM, all ideas function the same within/through/for the model. In that post I’m comparing a study of using AI to perform routine office processes (from NYTimes) with the use of AI to perform a complex set of tasks in drug development, in effect, high-school level capability with Ph.D. level capability. They’re the same to the LLM.

I need to think about this some more. It seems to me what’s nowhere present in the LLM is the kind of procedural knowledge necessary to learn tasks at whatever level. That simply isn’t presented in the written products of that knowledge (not even in written procedures).