There is a cool sandbox game on steam called “Plasma” that offers a graphical programming environment which includes a basic PID controller. I was really impressed with how easily I was able to make a cube float almost perfectly in 3D space within that game by only using 3 of these controllers (mainly the P and D portions) and some thrusters by monitoring the pitch and roll angles, and how high above the ground the cube was. I was even more impressed when adding some weight to one side of the cube and seeing the system compensate for it automatically.
The process of tuning the gains on the different inputs of the controllers, then seeing how the system responded made me think about how this is a similar yet much simpler process to training ML models.
I’ve always been a bit skeptical of how easily one would be able to make changes to something as complex as a large ML model, but looking at it like tuning a very complex PID controller kind of made it seem less like black magic to me for some reason
The process of tuning the gains on the different inputs of the controllers, then seeing how the system responded made me think about how this is a similar yet much simpler process to training ML models.
I’ve always been a bit skeptical of how easily one would be able to make changes to something as complex as a large ML model, but looking at it like tuning a very complex PID controller kind of made it seem less like black magic to me for some reason