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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8 years ago
Explaining Deep Learning by Making AI Transparent

 For today’s leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World Las Vegas, June 3-7, 2018.   Before discussing this above topic, it is important to describe my definition of AI within the context of this article. AI is the use of neural net technology with its more recent developments occurring in the area of deep learning. Now let’s begin the discussion. Experienced data scientists with many years of applying various solutions for specific business problems do not merely rely   on the “performance” of the solution itself as the final deliverable. An equal

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