snimu

Yeah, I was kind of rambling, sorry. 

My main point is twofold (I'll just write GPU when I mean GPU / AI accelerator):

1. Destroying all GPUs is a stalling tactic, not a winning strategy. While CPUs are clearly much worse for AI than GPUs, they, and AI algorithms, should keep improving over time. State-of-the-art models from less than ten years ago can be run on CPUs today, with little loss in accuracy. If this trend continues, GPUs vs CPUs only seems to be of short-term importance. Regarding your point about having to train a dense net on GPUs before sparsification, I'm not sure that that's the case. I'm in the process of reading... (read more)

I realize that destroying all GPUs (or all AI-Accelerators in general) as a solution to AGI Doom is not realisticly alignable, but I wonder whether it would be enough even if it were. It seems like the Lottery-Ticket Hypothesis would likely foil this plan: 

dense, randomly-initialized, feed-forward networks contain subnetworks ("winning tickets") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations.

Seeing how Neuralmagic successfully sparsifies models to run on CPUs with minimal loss of accuracy, this would imply to me that the minimal 'pivotal act' might be to destroy all compute instead of just GPUs / AI-accelerators. Moreover, it would actually... (read more)