Solving Problems & Saving Time through Software and Crushing Entropy
Powered by Cecil & Hyde.
If you've read a handful of my posts I don't think you would be surprised if I told you I am an academically-minded person. There are a fair number of causes for this (which I am learning about more and more) and there are plenty of reasons why that would be a surprising development.
As a kid, I was "good at math". The truth is more that it came very easily. However, it never engaged me. It was easy and boring. I still have not done a lot of math in my day, though I wish I did more. I wish they taught math differently because when I see the kind of work that Grant Sanderson does at 3 Blue 1 Brown I am positively riveted. Even though I am entirely incapable of actually doing that math—it would take a lot of work now to even attempt getting there.
The link between high-level math that looks so interesting, my "academic-ness" nature, and my career in software revolves around the question of "How do we know what we know?" In my mind this boils down to two famous sayings: "All models are wrong, but some are useful" and "Don't confuse the map for the territory."
Grant Sanderson wields his understanding of high-level maths by fitting a solution to a real-world problem. He answers "How do we know how to solve this problem?" by reframing the problem in terms of a system of math that can generate an accurate and useful answer. For those who don't enjoy math, but have seen "Hidden Figures", this is precisely what Dorothy Vaughn did to solve their trajectory problems by picking a different mathematical system that would give them an answer they can use. In the view of people without this kind of mathematical mastery, it looks like they are wizards, or they are merely cheating—making up answers as they go along.
We know that computers only do what they are told. Decades ago you had the ability to see and understand everything the computer was doing. Not today, there are too many layers and too many authors of code in every machine running today. Computers are still doing exactly what they are told. There are just too many authors yelling at the computer.
This is the root of bugs. The computer is doing what it is told. The problem is us. We got an unexpected response. It is our understanding that is flawed. The "territory" is what it is. Our "map" is wrong.
We are wrong constantly. This means that we should always be learning. Many people are afraid of being wrong. I've never understood that perspective. Learning oftentimes means you were wrong. After all, it's rare that we go out of our way to truly learn something from zero knowledge. More often we are adding to the knowledge, and more importantly, changing the knowledge we have. Updating our maps, not treating them as dogmatic truth to rely on.