Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
How Predictive AI Will Solve GenAI’s Deadly Reliability Problem
  Originally published in Forbes Generative AI too unreliable to...
5 Ways To Hybridize Predictive AI And Generative AI
  Originally published in Forbes AI is in trouble. Both...
This Simple Arithmetic Can Optimize Your Main Business Operations
 Originally published in Forbes Deep down, we all know that...
Predictive AI Usually Fails Because It’s Not Usually Valuated
 Originally published in Forbes Why in the world would the...
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Eric Siegel, scholar, consultant and event organizer, explains how, where and why predictive analytics can be used to inform more proactive, empirically-based decision making. As part of his time at Cognizant Confluence 2011, Siegel brings a lot of good points to the table here, offering insights into why predictive analytics are useful and which business practices they can be most helpful to. The idea of predictive analytics is pulled from a lot of unstructured data, AKA Big Data. It is this unstructured data that offers valuable information and learning opportunities. And as Siegel says, “There’s never enough data” when it comes to analytics.

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