Abstraction is the golden path to information efficiency. But all kinds of hacks can get you data efficiency.
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There's a big difference between data-efficiency and information-efficiency. We could make considerable progress on the former without even starting to approach the latter -- and never realize our fundamental mistake.
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The difference between data & information: - Data is plentiful & noisy, the large majority of data is not information - Information is what makes data useful - We are bad at extracting info from data. We only manage to distill & use a small fraction of what is really available
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Working on "data efficiency" typically means finding new hacks to increase the amount of information we can extract from the same data. But "information efficiency" is an entirely unrelated concept: how to efficiently and effectively make use of information
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Is there a good reference as to what you mean by information-efficiency? I don't quite understand the distinction.
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data efficiency: compression, and all that jazz. information efficiency: realizing just a few facebook quiz questions, or Likes, can define how someone can be influenced to vote, rather than storing their whole online footprint in digital form
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this is also a common source of tension between engineers and data scientists - eg, engineers will prioritize data efficiency over information efficiency. For example, refusing to create abstractions in the data (derived tables, etc) because it adds more steps to a pipeline.
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Amount of data (or website traffic, on some cases...) is still too often prioritised over quality of it.
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The brain vs the gut. One is data efficient and the other is information efficient. Nice terms.
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Thanks. Twitter will use this to make your timeline better. UndoUndo
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Data is merely a byproduct of any measurement system. Data is useful as information in the context of the (prediction) task at hand. So improving information efficiency would require the consideration of the measurement system and the task.
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