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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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On this page
  • Improving Model Performance through Efficient Use of Data
  • Accelerating the Amassing of Foundational Datasets
  • Federated Learning
  • Bio-Manufacturing the First Version of a Superior Bio-Product
  1. Wave 2
  2. Wave 2 – Opportunity Areas

Accelerating Serendipitous Discovery

New process definition to supersede human limitations

PreviousAutomating & Roboticizing Stodgy IndustriesNextWave 2 - The North Star: Building for the J-Curve

Last updated 3 years ago

Improving Model Performance through Efficient Use of Data

Data efficiency plays include using less data to train algorithms, differentiating clean data from garble, and algorithms that interpolate behavior through synthetic data.

  • To mitigate model drift and longtail data challenges, Bee portfolio company builds dynamic asset databases (CGI enhanced digital twins based upon 3d scans), and can produce 10K+ variants of pixel-perfect labeled images overnight.

Accelerating the Amassing of Foundational Datasets

Trends like Siri for the biology lab, automated pipetting, labs-on-a-chip, high throughput experimentation, virtual CROs, CV, AI, and ML, are streamlining critical but tedious benchwork and generating rich datasets. Using this data to efficiently traverse experimentation helps cut down discovery time to generate breakthroughs consistently.

  • Bee portfolio company is powering industry-wide scale up via continuous fermentation at bench-scale which informs their ML algorithm, enabling high-throughput (10x faster and cheaper) bio-production.

Federated Learning

Data privacy is a customer and enterprise problem. Infrastructure that allows data to be shared reliably and enables ML infrastructure to update without compromising details of provenance is an exciting area of exploration.

  • ML companies in the healthcare domain (e.g. ) rely on federated learning to get access to more diverse datasets to speed up drug discovery.

Bio-Manufacturing the First Version of a Superior Bio-Product

In W2, startups typically aspire to achieve just 1 reproducible pilot-scale fermentation run across 2 years, with a special emphasis on downstream processing (DSP - recovering the desired output, e.g. protein). DSP is custom across outputs and therefore the most difficult to scale.

  • Bee portfolio company is producing cow cheese without the cow by leveraging a proprietary production method of the milk protein, Casein Micelles. The company raised a Series A before launching product in market, an industry norm.

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