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Risk missing the long tail, algorithmic discrimination, stereotyping
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Neglect of novelty
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The cold start problem, managing serendipity and filter bubble effects.
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Finding the value proposition which goes beyond the simple “you purchased this, you’ll like that”
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Slow progress: curation needs human labor to insure high accuracy, it does not scale the way a computerized process would.
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Must maintain continuity: missing a single year or month hurts the value of the overall dataset.
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Scaling up / right incentives for the workforce: the workforce doing the digital labor of curation should be paid fairly, which is not the case yet.
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Quality control
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Finding emergent, implicit attributes (imagine: if you rank things based on just one public feature: not interesting nor valuable)
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Insuring consistency of the ranking (many rankings are less straightforward than they appear)
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Avoid gaming of the system by the users (for instance, companies try to play Google’s ranking of search results at their advantage)
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Intelligent BI with Aiden
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wit.ai, the chatbot by FB
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Close-to-real-life speech synthesis
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Generating realistic car models from a few parameters by Autodesk
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Generating summaries and comments from financial reports Yseop
A video on the generation of car models by Autodesk:
Find references for this lesson, and other lessons, here.
Discover my other courses in data / tech for business: https://www.clementlevallois.net
Or get in touch via Twitter: @seinecle