This notion that the knowledge required to make progress in Deep Learning originates from an entirely different area of research than what one is taught in university threatens the status quo. Nobody in the DL community will admit this because it acknowledges a disadvantage.
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That said, the number of researchers working in complex adaptive systems (see:
@sfiscience) is extremely small. This is likely because it's mostly populated by researchers who come from a mix of interdisciplinary sciences. Present-day academic organizations discourage this kind.Prikaži ovu nit -
This is indeed unfortunate considering that the biggest benefits of science will originate from our increased understanding of complex adaptive systems using methods that cross-pollinate from deep learning research.
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David Krakauer says "So how we integrate complexity science with machine learning, I consider by the way, the challenge for thinking people in the 21st century." in this interview by
@jim_rutthttps://www.jimruttshow.com/david-krakauer/Prikaži ovu nit
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OK but there is also the engineering part. What about that? I try to understand how this understanding will affect his actions.
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You mean to use biology as inspiration for taking from that space the ideas.
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Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Čini se da učitavanje traje već neko vrijeme.
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