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You could say that modern deep learning is the product of a co-evolution process between hardware, software, and algorithms. It originally arose thanks to the availability of GPUs and CUDA. Then we started optimizing our hardware and software for these specific algos...
...which led to the consolidation of the machine learning community behind these highly-effective "winners". Nowadays, the entire deep learning software and hardware ecosystem is micro-optimized for a small set of techniques.
At this point, figuring out a different path would require a multi-year re-engineering of the entire ecosystem.
So the current state of deep learning is at a local optimum but possibly not a global one? That sounds very fitting.
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