Machine Learning Times
EXCLUSIVE HIGHLIGHTS
A Brief History of Why Machine Learning Projects Stall
  EDITOR’S NOTE: This is the preface to The...
Hybrid AI Emerges To Tame LLMs – And Not A Moment Too Soon
 Originally published in Forbes The great potential of LLMs is...
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...
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By: Dr. John Elder, CEO & Founder, Elder Research, Inc

 (Part 3 of 11 of the Top 10 Data Mining Mistakes, drawn from the Handbook of Statistical Analysis and Data Mining Applications) -John Elder (elder [at] datamininglab [dot] com) “To a little boy with a hammer, all the world’s a nail.” All of us have had colleagues (at least) for whom the best solution for a problem happens to be the type of analysis in which they are most skilled! For many reasons, most researchers and practitioners focus too narrowly on one type of modeling technique. But, for best results, one needs a whole toolkit. At the very

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