This is one of those links where just seeing the title sets you off, thinking about the implications.
I'm going to have to spend more time digesting the article, but one thing that jumps out at me, and maybe it's answered in the article and I don't understand it, is the role of time. Generally in physics, you're talking about a quantity being conserved over time, and I'm not sure what plays the role of time when you're talking about conserved quantities in machine learning -- is it conserved over training iterations or over inference layers, or what?
edit: now that i've read it again, I just saw that they described in the second paragraph.
I'm now wondering if in something like Sora that can do a kind of physical modeling, if there's some conserved quantity in the neural network that is _directly analagous_ to conserved quantities in physics -- if there is, for example, something that represents momentum, that operates exactly as momentum as it progresses through the layers.
I'm going to have to spend more time digesting the article, but one thing that jumps out at me, and maybe it's answered in the article and I don't understand it, is the role of time. Generally in physics, you're talking about a quantity being conserved over time, and I'm not sure what plays the role of time when you're talking about conserved quantities in machine learning -- is it conserved over training iterations or over inference layers, or what?
edit: now that i've read it again, I just saw that they described in the second paragraph.
I'm now wondering if in something like Sora that can do a kind of physical modeling, if there's some conserved quantity in the neural network that is _directly analagous_ to conserved quantities in physics -- if there is, for example, something that represents momentum, that operates exactly as momentum as it progresses through the layers.