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  • Introduction
    • Machine-to-Machine Learning White Paper Introduction
    • What is M2ML?
    • 3 Waves - Overcoming Limitations of Human Performance
  • Wave 1
    • Wave 1 – Vision & Identification
    • Wave 1 – Archetypes
    • Wave 1 – Opportunity Areas
      • Automating & Roboticizing Stodgy Industries
      • Accelerating Serendipitous Discovery
  • Wave 2
    • Wave 2 – Building an MVP
    • Wave 2 – Archetypes
    • Wave 2 – Opportunity Areas
      • Automating & Roboticizing Stodgy Industries
      • Accelerating Serendipitous Discovery
    • Wave 2 - The North Star: Building for the J-Curve
  • Wave 3
    • Wave 3 – Building a Platform
    • Wave 3 – Archetypes
    • Wave 3 – Opportunity Areas
      • Automating & Roboticizing Stodgy Industries
      • Accelerating Serendipitous Discovery
  • Insights
    • Identifying Winners Across Waves
    • Case Study: Rapid Robotics
    • Go-to-Market Playbook
      • Product
      • Sales
      • Platform
    • Waves in Motion
  • Conclusion
    • Why Now?
    • Get in Touch
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  1. Wave 2

Wave 2 – Opportunity Areas

By embarking on Wave 1 to operationalize processes, a firm aims to identify a set of performance metrics, an input state of parameters to optimize, a process to run and analyze results of repeated experiments and an algorithm to optimally alter input parameters for the next iteration based on the output of the previous state. This is reinforcement learning 101 - by achieving this virtuous cycle on a lab bench scale, a firm immediately moves to Wave 2 - ​scaling the device plumbing to implement this learning cycle and deploy into production via an MVP.

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Last updated 3 years ago

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