I thought that the part about models needing to keep track of a more complicated mix-state presentation as opposed to just the world model is one of those technical insights that’s blindingly obvious once someone points it out to you (i.e., the best type of insight :)). I love how the post starts out by describing the simple ZIR example to help us get a sense of what these mixed state presentations are like. Bravo!
Thanks! In my experience Computational Mechanics has many of those types of technical insights. My background is in neuroscience and in that context it really helped me think about computation in brains, and design experiments. Now I’m excited to use Comp Mech in a
more concrete and deeper way to understand how artificial neural network internal structures relate to their behavior. Hopefully this is just the start!
I thought that the part about models needing to keep track of a more complicated mix-state presentation as opposed to just the world model is one of those technical insights that’s blindingly obvious once someone points it out to you (i.e., the best type of insight :)). I love how the post starts out by describing the simple ZIR example to help us get a sense of what these mixed state presentations are like. Bravo!
Thanks! In my experience Computational Mechanics has many of those types of technical insights. My background is in neuroscience and in that context it really helped me think about computation in brains, and design experiments. Now I’m excited to use Comp Mech in a more concrete and deeper way to understand how artificial neural network internal structures relate to their behavior. Hopefully this is just the start!