Machine Learning Times
EXCLUSIVE HIGHLIGHTS
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...
How To Overcome The Confidence-Killer That Destroys Most Predictive AI Projects
  Originally published in Forbes When Henry Castellanos first presented...
You Must Address These 4 Concerns To Deploy Predictive AI
 Originally published in Forbes Most predictive AI projects fail to launch into production. The...
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13 years ago
Top 10 Analytic Mistakes–Today #0: Lacking Relevant Data

 Mining data to extract useful and enduring patterns remains a skill arguably more art than science. Pressure enhances the appeal of early apparent results, but it is all too easy to fool oneself. How can one resist the siren songs of the data and maintain an analysis discipline that will lead to robust results? It is essential to not: lack (proper) data, focus on training, rely on one technique, ask the wrong question, listen (only) to the data, accept leaks from the future, discount pesky cases, extrapolate (practically and theoretically), answer every inquiry, sample casually, or believe the

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