Image

Information Has Value – Computer Science edition

After yesterday’s post I had a fascinating discussion with someone who codes for a living about whether patents were a viable research resource in CS. First off, they’re extremely hard to understand. And yes, I definitely agree, and it’s a good reminder that when I talk about this with students I also talk explicitly about what I expect they’ll be able to learn from the exercise.

Hensel, Otto A. 1900. Rocking or oscillating bath-tub. United States US643094A, filed January 6, 1899, and issued February 6, 1900.
  1. If you find a patent that you think is related to your topic, look at other similarly classified patents to see what problems people are tackling in the field and who is tackling them.
  2. As you look through similarly classified patents, collect vocabulary that you can use in future searches. After all, most search systems simply match letters in a row rather than semantics, so if people are talking about the same thing but using different words to do so, you won’t find that whole side of the conversation.

While reading in order to understand the patented process is probably not feasible for most people, reading instrumentally has been super useful for me when exploring CS topics.

So far so good, but what really set me thinking was this industry coder’s take on the disadvantages of reading patents. Apparently he’s told not to read patents because knowingly infringing on someone else’s IP brings worse penalties than unknowingly infringing. In order to mitigate penalties, they don’t look at patents. So now I’m wondering how to guide students as they prepare for a world in which, at least some of the time, lack of information has value. And how do I square that with the idea of the very real costs involved in having a bunch of people reinventing wheels and falling into the same pitfalls, all so that if they get sued it won’t be quite so bad? And how do I square that with how this upends the progress narrative of the sciences in general, a set of disciplines which so carefully finds gaps in knowledge and then fills them, or finds the limits of current knowledge and then pushes those limits back bit by bit?

I wonder if it matters what sector you’re in, or even what specific companies you’re working for. And I wonder how liberal arts students might engage with this conundrum in a way that prepares them for life after graduation, whether that life involves CS careers or not.

Leave a Reply