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 and Founder, Elder Research, Inc

 (Part 2 of 11 of the Top 10 Data Mining Mistakes, drawn largely from Chapter 20 of the Handbook of Statistical Analysis and Data Mining Applications) Only out-of-sample results matter; otherwise, a lookup table would always be the best model.  Researchers at the MD Anderson medical center in Houston (almost two decades ago) used neural networks to detect cancer.  Their out-of-sample results were reasonably good, though worse than training, which is typical.  They supposed that longer training of the network would improve it – after all, that’s the way it works with doctors – and were astonished to

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