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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5 years ago
“Doing AI” Is a Mistake that Detracts from Real Problem-Solving

  A note from Executive Editor Eric Siegel: Richard Heimann’s forthcoming book, Doing AI, takes on the problems with “AI” as a brand with a style so crisp, clear, and unique, it just pops off the page. He surveys the litany of troublemakers who’ve misguided the world with AI mythology, but then greets this mishap with the ultimate business-savvy antidote: how to effectively identify and solve real-world problems. His book will repeatedly make you go “hmm!” as it overhauls your thinking about AI, machine learning, and problem-solving in general. And by the way, if you’d like to hear

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