Machine Learning Times
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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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By: Dean Abbott, President, Abbott Analytics 

 The Cross Industry Standard Process for Data Mining (CRISP-DM) is the leading published methodology for Data Mining (DM), and by extension, Predictive Analytics (PA). I use it routinely as I lead PA projects and when I teach appiled DM and PA courses. It was the subject of three KD-Nuggets polls, in 2002, 2004, and 2007) and nearly half of the responders stated they used it as the main methodology for DM (http://www.kdnuggets.com/polls/2007/data_mining_methodology.htm). There are six stages in the CRISP-DM process, including Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. There are many excellent books describing five

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