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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. Introduction

3 Waves - Overcoming Limitations of Human Performance

M2ML’s 3 Waves of Innovation

Wave 1 (W1) Discover an ‘Automatable’ Process

Which human-operated tasks can be enhanced by automation? Is it compelling and mechanically feasible to do?

Shifts from manual to machine-based cycles typically take place in processes that are 1) repeatable with precise specifications or 2) facilitate discovery by constraining the experimentation space.

Wave 2 (W2) Build out the Device Plumbing

What is the system design for minimum required performance? Do our projected unit economics make this a feasible option?

Design the system that includes hardware, networks, APIs, and architecture in order to prototype and test the feedback loop that completes an optimization process. Converge on the first use case’s minimum required performance and restrain further W2 experimentation until economics have also been validated.

Wave 3 (W3) Nail One Use Case, to then Build a Platform

How can you abstract the proven architecture into a platform? How do the greater number of use cases add exponential value?

Once the prototype and use case is validated in the market, think of a platform as a way to extend and scale infrastructure for higher throughput and increased autonomy, and for use across more domains and use cases.

Platform building depends upon constant improvement and new domain challenges, which constantly (but not always continuously) trigger a new wave cycle.

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Last updated 3 years ago

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