[HN Gopher] Deep Learning in Business Analytics: A Clash of Expe...
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       Deep Learning in Business Analytics: A Clash of Expectations and
       Reality
        
       Author : belter
       Score  : 8 points
       Date   : 2022-05-22 21:11 UTC (1 hours ago)
        
 (HTM) web link (arxiv.org)
 (TXT) w3m dump (arxiv.org)
        
       | stanfordkid wrote:
       | I'd be curious to understand why this is the case. It seems that
       | businesses generally have highly network style data -- think of
       | your average system for a "widget" business... it ranges from
       | orders to suppliers to manufacturing processes, advertising, all
       | the way to orders from customers. All stored in different systems
       | with disparate join keys.
       | 
       | Most of the hard part of BI I have seen is joining these pieces
       | of information together that are otherwise uncorrelated -- e.g
       | which clicks corresponded to conversion is a tracking problem,
       | then correlating them with actual business actions (e.g running a
       | marketing campaign or improving a process)
       | 
       | It's more about data joining and data aggregation than "ML"
        
       | mountainriver wrote:
       | This is an incredibly shallow paper. GNNs are mostly used on
       | structured data today and are a part of the Deep Learning family,
       | as are transformers.
       | 
       | The authors seem to be unaware of that and are pegging deep
       | learning as a very narrow implementation.
        
       | belter wrote:
       | PDF: https://arxiv.org/ftp/arxiv/papers/2205/2205.09337.pdf
       | 
       | "...This paper explains why DL - despite its popularity - has
       | difficulties speeding up its adoption within business analytics.
       | It is shown - by a mixture of content analysis and empirical
       | study - that the adoption of deep learning is not only affected
       | by computational complexity, lacking big data architecture, lack
       | of transparency (black-box), and skill shortage, but also by the
       | fact that DL does not outperform traditional ML models in the
       | case of structured datasets with fixed-length feature vectors.
       | Deep learning should be regarded as a powerful addition to the
       | existing body of ML models instead of a one size fits all
       | solution..."
        
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       (page generated 2022-05-22 23:01 UTC)