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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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4 years ago
Models Are Rarely Deployed – an Industrywide Failure in ML Leadership (Poll Results)

  Originally published in KDnuggets. The latest KDnuggets poll reconfirms today’s dire industry buzz: Very few machine learning models actually get deployed. In this article, I’ll summarize the poll results and argue that this pervasive failure of ML projects comes from a lack of prudent leadership. I’ll also argue that MLops is not the fundamental missing ingredient – instead, an effective ML leadership practice must be the dog that wags the model-integration tail. Considering the growing chatter about ML’s failure to launch, there’s been relatively little concrete industry research – especially when it comes to surveys on model

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