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Machine Learning for the Automotive World

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Machine Learning for the Automotive World

  1. 1. Machine Learning for the Automotive World February 25th, 2020 Jeff Rehm
  2. 2. Topics Behavioral Learning The Vision Challenges and Pitfalls Implications for UX Design Predictions are hard – particularly when they’re about the future. --Yogi Bera (?)
  3. 3. First, a Misconception Having a vision is important-- there should always be a goal to strive for. But… we should take a sober, rational approach. “If it’s built in Python, it’s Machine Learning. If it’s built in PowerPoint, it’s Artificial Intelligence” -- CloudMade Data Scientist The idea of “Artificial Intelligence” in an automobile. It is still a long, long way in the future.
  4. 4. Making predictions based on the behavioral patterns and preferences of an individual Making predictions based on large-scale aggregation of behavior (speed, feature use, etc.) Making predictions based on similarities to others Cohort Learning Personal LearningFleet Learning Behavioral Learning
  5. 5. The Vision, the Promise JLR Video https://www.youtube.com/watch?v=F923EuB06CI
  6. 6. Applications Today Led by Luxury, but moving mainstream JLR Smart Settings Audi MIB2+ BMW Connected+ Hyundai Smart Cruise Ford Mach-E Mercedes MBUX
  7. 7. Typical Use Cases Focused on the Journey • Destination • Route • Departure time • Arrival time What they do on the way • Media preferences • Climate preferences Built around the driver’s profile
  8. 8. Why has it taken so long? Connectivity In-vehicle hardware Backend systems at carmakers Readiness of users But mostly: Because it’s hard.
  9. 9. Configuration Data Collection Feature Extraction ML Data Verification Machine Resource Management Analysis Tools Process Management Tools Serving Infrastructure Monitoring “Only a small fraction of real-world ML systems is composed of the ML code, as shown by the small black box in the middle. The required surrounding infrastructure is vast and complex. “ Hidden Technical Debt in Machine Learning Systems Google, Inc. Why it’s so Hard
  10. 10. Challenges and Pitfalls Going Rogue Dangers: Feedback Loops, Non- homogenous data, non-declared consumers, etc. à Technical debt. Potentially massive amounts. Privacy • Trust – avoiding creepiness • Data protection regulations (GDPR, CCPA) • Consent management Disillusionment • Expectations management • Some people have predictable behaviors. Others do not. • UX Design is critical here Coherency ML must be managed across the entire ecosystem (car, cloud, companion app) • Consistency of predictions • Holistic approach.
  11. 11. Big Challenge: The Business Case What’s the ROI? If there is a significant investment to properly deploy ML, what’s the payback? What is a prediction worth? Valuing ML Solutions is not easy. What did we learn? • There are very few standalone “killer use- cases”. • High level “elegance” features are difficult to value or monetize • Instead it’s the accumulation of many features that add up to provide synergy Solve real problems • Look further down Maslow’s Automaker’s hierarchy of needs à Focus on Safety, Emissions, Efficiency, Cost of ownership, Retention, Costs of development, manufacture, etc.
  12. 12. Deployment Considerations Architecture • Where should the learning be done? • Storage, memory, CPU resources, transmission, cloud costs • Where should the predictions be done? • Latency? Context? Managing data • Validation • Extraction • Normalization across entire fleet • Privacy
  13. 13. UX Design Paradigm Shift Answer the ‘W’ questions: • Where are they going? Who will go? When will they leave? How will they get there? What will they do on the way? Confidence? Predictions are probabilities, not absolutes. Integration: Non-deterministic predictions must be integrated into your deterministic UX logic. This is where your skill enters in. CONTEXT CONTEXT + INTENT
  14. 14. Example: 2 Variable Integration Do Nothing Confidence Level – Departure Time ConfidenceLevel-Destination 100%0% 100% Proactive push notification Pre-populated suggestions Proactive destination input
  15. 15. Future Applications Adaptive UX • Familiarity with area • Cognitive workload Feature on-boarding • Cohort, Fleet, Personal Preconditioning • Climate • EV batteries Maintenance functions • Performed at ideal time based on predicted route, speed, etc. Powertrain Efficiency • Lean burn, ICE/BEV, Transmission optimization And on…
  16. 16. Take-Aways Machine Learning is real. Adds real value to the automaker and their customers. Building a proper foundation is a must. Maturity level is about to make a big jump. It is truly a new frontier.
  17. 17. “To a man with a hammer, everything looks like a nail” -- Mark Twain (?) “AI is not magic pixie dust. Predictions can provide value, but there is no substitute for good well-thought design.” --Jeff
  18. 18. References Yogi Berra quote https://quoteinvestigator.com/2013/10/20/no-predict/ Land Rover's Self-Learning Intelligent Vehicle Video https://www.youtube.com/watch?v=F923EuB06CI Mercedes MBUX https://www.mercedes- benz.com/en/innovation/connected/mbux-mercedes- benz-user-experience-revolution-in-the-cockpit/ Audi MIB2+ Infotainment https://www.audi-mediacenter.com/en/the-new-audi-rs- q8-the-most-sporty-q-12422/infotainment-and-audi- connect-12432 Hyundai Smart Cruise (SCC-ML) https://www.hyundainews.com/en-us/releases/2887 Ford Mustang Mach-E Launch https://youtu.be/o0F9Uktpgtk?t=1309 Hidden Technical Debt in Machine Learning Systems D. Sculley, Gary Holt, Daniel Golovin, et al , Google Inc. https://papers.nips.cc/paper/5656-hidden-technical-debt- in-machine-learning-systems.pdf Mark Twain Quote https://quoteinvestigator.com/2014/05/08/hammer-nail/

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