Machine Learning Times
EXCLUSIVE HIGHLIGHTS
AGI Is Infeasible. Instead, Pursue Superhuman Adaptable Intelligence
  Originally published in Forbes On a recent episode of the...
Artifact-Driven Development: Making It Possible to Query Large Analytics and AI Projects
 A practical introduction to making complex project structure explicit...
Incoherent AGI Hype Spurs An Industrywide Pivot To Hybrid AI
  Originally published in Forbes Recently on The Dr. Data Show,...
The AI Paradox: More Humanlike Means Less Autonomous
  Originally published in Forbes The AI executives are at...
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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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