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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8 years ago
Why Your Analytics Must Ask the Data “Good” Questions — Ones that Reduce Data

 The problem of monetizing Big Data, or improving its usefulness in decision-making, stands out in every forward-looking organization today. To solve it, as everyone knows by now, it is not enough to store or manage it. We must analyze.  Hence, the term—what else? —“Big Data Analytics” (BDA). Yet in spite of the overwhelming need for BDA, without which Big Data is just more data, “Computer Science-IT Data Science” (CSIT DS) focuses the great majority of its effort on data storage and management. Most of its work could more properly be described as Big Data Management (BDM). BDA is

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