Cultural Evolution in Deep Learning, Some Notes, 2

A couple of days ago (5.24.22) I blogged about this article:

Artem Kaznatcheev, Konrad Paul Kording, Nothing makes sense in deep learning, except in the light of evolution, arXiv:2205.10320

Now I want to say a little about why I think it is important. This is from a note I sent to Tyler Cowen at Marginal Revolution:

Why is the paper important? Well, it is about deep learning, which is a very important technology that interests both of us. That makes the paper interesting, but it’s not why I think it is important. The study of cultural evolution has been going on in one way or another for some decades going back into the 1950s (and ignoring 19th century interest in the subject). A couple years ago a professional society was formed, which is dominated by people with a biological background, people like Peter Richerson and Robert Boyd, who trained Joseph Henrich and others. These investigators regard cultural evolution as simply another mechanism for doing the job done by biological evolution, a mechanism that works more quickly and flexibly. In this tradition, if we may call it that, the benefits of cultural evolution accrue to physical human beings, just like the benefits of biological evolution. That’s fine and well, and has produced interesting and important research.

But it has nothing to say about things like music, and literature, and not much to say about technology either. Those things move too rapidly and are moreover very complex and difficult to describe. Consequently orthodox cultural evolution, if I may, misses many cultural phenomena of interest.

As you know, back in 1976 Richard Dawkins proposed the idea of memes in a final chapter of The Selfish Gene. He proposed that, just as genes are the targets of biological evolution (the argument he made in the book), so memes are the targets of cultural evolution. Unfortunately he was unable to say much of anything very interesting about these memes, nor has anyone else, though Dan Dennett has tried (and, in my estimation, failed). Consequently, meme mostly means LOLcats and allied phenomena on the web, but also a lot of pop-culture speculation about, well, culture.

This deep learning paper is important precisely because Kaznatcheev and Kording take deep learning tech as the target of cultural evolutionary change. One thing about deep learning is that so very much of the technology is on public view. We’ve got the papers, but also the GitHub code repositories and so forth. So there’s lots to look at and analyze. This paper is just a beginning for them. They’ll be collecting data and analyzing it.

https://new-savanna.blogspot.com/search/label/progress

This work is important for progress studies, not simply because it is about important technology, but for its method. Pointillistic studies of the history of technology are interesting and important. But, from my point of view, they are most important as starting points for evolutionary investigations, which are going to require a lot more digging in the archives and computational investigation of libraries, document collections, and so forth.

These are the good old days, the best days are yet to come.