...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.
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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...
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At this point, figuring out a different path would require a multi-year re-engineering of the entire ecosystem.
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Just curious, with keras and a decent laptop in 2020, would you be able to match state-of-the-art in 2010? 2000? 1990?
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I guess I mean tensorflow or any other modern toolkit. If I have to pick a subdomain I’ll say natural language
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We need open source version of CUDA.
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It's called ROCM
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Thanks to gaming
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My guess would be, that without gaming, particulalrly GPUs in consoles the market would be very small, technological progress much smaller. Is that correct? (In other words driven by consumer-markets like the overall miniaturization of computers by smartphones.)
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The hardware is less dynamic of the trio. Algorithms will always evolve no matter what. But the software bit presents biggest challenges. I guess people will always explore and try new things in open source space. Kind of like Windows vs Linux.
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It's still statistics for developers.
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