Machine Learning Times
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2 More Ways To Hybridize Predictive AI And Generative AI
  Originally published in Forbes Predictive AI and generative AI...
How To Overcome Predictive AI’s Everyday Failure
  Originally published in Forbes Executives know the importance of predictive...
Our Last Hope Before The AI Bubble Detonates: Taming LLMs
  Originally published in Forbes To know that we’re in...
The Agentic AI Hype Cycle Is Out Of Control — Yet Widely Normalized
  Originally published in Forbes I recently wrote about how...
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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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