The TWIML AI Podcast · Monday, July 27, 2026
The initial research in weight space learning, starting around 2020-2021, focused on the idea of 'fingerprinting' neural networks. The goal was to determine if neural network weights could be analyzed similarly to software code to identify differences and changes.
“Can we fingerprint a neural network or version of neural network like we can do with software, right?”
“Problem with neural networks is if you do one update of weights during training, every weight is a little bit different. So there's not much you can extract from this. Right. A fairly unstable locally. If everything is different, nothing is different, right?”