I’ve been trying to understand the black box problem in AI, and I came across an idea that I found interesting. Some people use concepts from physics, like energy landscapes or stable states, to explain how neural networks learn. From what I understand, the idea is that instead of looking at every single parameter, you look at the model as a complex system that slowly moves toward more stable patterns during training. That explanation makes sense to me at a basic level, but I’m not sure how far it actually goes with modern large models. Is this a useful way to think about neural networks, or is it too simplified? I’d like to hear from people who understand this better. submitted by /u/Marketingdoctors
Originally posted by u/Marketingdoctors on r/ArtificialInteligence