New age, new control points?
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New control points are emerging in an era characterised by complex coordination challenges and machine learning. How can telcos and their partners help to maintain a balance of power in the Coordination Age?
Format: PDF filePages: 43 pages Charts: 09 Author: David Pringle Publication Date: August 2019
Table of Contents
- Executive Summary
- 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