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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12 years ago
It is a Mistake to…. Extrapolate

 (Part 8 (of 11) of the Top 10 Data Mining Mistakes, drawn from the Handbook of Statistical Analysis and Data Mining Applications) Modeling “connects the dots” between known cases to build up a plausible estimate of what will happen in related, but unseen, locations in data space. Obviously, models – and especially nonlinear ones — are very unreliable outside the bounds of any known data. (Boundary checks are the very minimum protection against “over-answering”, as discussed in the next installment.) But, there are other types of extrapolations that are equally dangerous. We tend to learn too much from

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