Machine Learning/AI: Hard Facts, Conclusions and Actions

Start: May 15, 2017

We are in the early stages of a 10-year cycle which machine learning is morphing from a lab curiosity to a rich, pervasive technology value-add.

Grandiose visions and deep seated cynicism are fighting each other in the marketplace. We break down fact versus fiction and various alternative paths to success.

Discussion Topics:

  • How safe it is to dive into the deep end – what’s real and what isn’t.
  • Which platforms and applications to invest in first.
  • Where innovation in open source and with citizen data scientist enablement is having an impact.

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