New age, new control points?

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Description

Format: PDF file
Pages: 43 pages
Charts: 09
Author: David Pringle
Publication Date: August 2019

Table of Contents

• Executive Summary
• Introduction
• What constitutes a control point?
  • Control points evolve and shift
  • New kinds of control points
• The big data dividend
  • Can incumbents’ big data advantage be overcome?
  • Data has drawbacks – dangers of distraction
  • How does machine learning change the data game?
• The power of network effects
  • The importance of the ecosystem
  • Cloud computing capacity and capabilities
  • Digital identity and digital payments
• The value of vertical integration
  • The machine learning super cycle
  • The machine learning cycle in action – image recognition
• Tesla’s journey towards self-driving vehicles
  • Custom-made computing architecture
  • Training the self-driving software
  • But does Tesla have a sustainable advantage?
• Regulatory checks and balances
• Conclusions and recommendations

Table of Figures

• Figure 1: The key elements of an effective machine learning solution
• Figure 2: Big data doesn’t always deliver more accurate machine learning
• Figure 3: Assessing the effectiveness of a machine learning solution
• Figure 4: The key elements of an all-cloud or edge-based machine learning solution
• Figure 5: Sample images returned by Google Photos in a search for bikes
• Figure 6: Google has tight control over much of the image recognition learning cycle
• Figure 7: Tesla’s in-house chip to process 21X more video frames than its predecessor
• Figure 8: Tesla’s process for correcting mistakes by its Autopilot system
• Figure 9: Tesla’s machine learning cycle isn’t as robust as it would like

Technologies and industry terms referenced include: AI, Amazon, apple, big data, control points, EcoSystem, facebook, google, Machine Learning, regulation, Tesla