Is there a #DataScience counterpart to this principle?
A hallmark of great design is the affordance-to-complexity ratio: being able to do lots of things via a very simple interface. A world of possibilities, that even a kid can learn to access in a minute. The Minecraft interface is a good example of that. As was the original iPhone
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In this case, competition grows rapidly. In the end, the complexity will appear by itself. No matter how simple the interfaces are, to do important things, we need to face the complexity.
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It goes beyond simplicity. A compiler lets you do a lot of things with a very simple interface.
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The interface of a compiler (which includes its programming language and surrounding tooling) is very complex: a kid cannot master it in a few minutes.
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Key: powerful combinations of few simple primitiveshttps://twitter.com/trylks/status/1007685022843891713 …
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Abstracting the complexity with techniques like convention over configuration (COC) is helpful too Both are complementary: - Combine simple primitives: simple model (low Kolmogorov complexity) - COC: complexity becomes optionalhttps://twitter.com/fchollet/status/1231285340335267840 …
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What do you mean by the original iPhone? What changed in the current one in terms of complexity?
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Norton Commander on the 8086 systems was a beautiful example of this. Once you moved from DOS to it, it was better than anything that come after until windows 95
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Not to be a fan boy but keras is a good example too. I compare pure Tf1 solutions or pytorch and I get frustrated in comparison. It's like saying to a linux C guy, use win32api ( infuriating )
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Ironic how http://keras.io is 5D chess explained in Klingon tho, needs to start at eli5 teachable machine
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