Hydrogen-soaked crystal lets neural networks expand to match a problem
Training AIs remains very processor-intensive, in part because traditional processing architectures are poor matches for the sorts of neural networks that are widely used. This has led to the development of what has been termed neuromorphic computing hardware, which attempts to model the behavior of biological neurons in hardware.
https://arstechnica.com/science/2022/02/hydrogen-soaked-crystal-lets-neural-networks-expand-to-match-a-problem
