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Bharath Ramsundar wrote that deep learning is being coupled with "[i]mprovements in custom architecture". For example, machine learning systems have been implemented in the blockchain world, where Bitmain assaulted "many cryptocurrencies by designing custom mining ASICs (application specific integrated circuits)" which had been envisioned as undoable. "Nvidia’s grand achievement however is in making the case that these improvement in architectures are not merely isolated victories for specific applications but perhaps broadly applicable to all of computer science." They have suggested that broad harnessing of GPUs and the GPU stack (cf., CPU stack) can deliver "dramatic growth in deep learning architecture." "The magic" of Huang’s law promise is that as nascent deep learning powered software becomes more availed, the improvements from GPU scaling and more generally from architectural improvements" will concretely improve "performance and behavior of modern software stacks."[1]
He says it is a self published unreliable source. His edit summary said: "Github is a platform for self-published sources, not a publication with editorial standards." Notwithstanding, given the level of analysis and extensive citations within the article, and the author's credentials, I disagree WP:BRD Input from other editors would be appreciated. 7&6=thirteen (☎) 10:59, 25 September 2020 (UTC)[reply]
Comment there a discussion about similar sources. My own opinion is that editor's should evaluate the credentials of expert in field. This does appear to be an expert. But I will wait to hear other opinions. Lightburst (talk) 13:53, 25 September 2020 (UTC)[reply]
