The problem with education is that it can’t be measured effectively, so instead we turn to credentials, which can.

Here is a long list of sources I consulted at various points while doing research for this project. As it might have become clear through reading the previous installments of this series, this was more a talk about the densest subgraph problem than it was about theory being useful in computer science. There are a few reasons for that, which I might explain in more detail at some point in the future. This means that I presented a lot of information that I borrowed from other people! Here’s an annotated bibliography of sorts, in case you were interested in going deeper, for whatever reason.

In Part 3, we discussed the densest subgraph problem (DSP) and some algorithms for solving it. In this section, we’ll be looking at how we can generalize this problem and those algorithms to solve some other problems. This is the part of the talk where I will introduce some fancy new math I had to learn in order to understand how one might generalize iterative peeling to solve adjacent problems.

Today it occurred to me that inherent in experiencing any artwork is the idea that I could someday meet the artist and ask them questions about it or at the very least have a conversation with them.

If the whole artwork is generated then there are no real questions that could be asked, because prompt engineering is not a real process.

I’ve been thinking a lot about how one’s background influences one’s experiences in education (and, in turn, what advice one gives to the next generation). Two people can have wildly different experiences in the same course, with the same instructor, at the same institution, and if you look deep enough the culprit is often socioeconomic differences, which tend to also line up with differences in academic achievement (but not always… it’s complicated).