Machine Learning for the Automotive World
- 1. Machine Learning for the Automotive World February 25th, 2020 Jeff Rehm
- 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. 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.
- 5. 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
- 6. The Vision, the Promise JLR Video https://www.youtube.com/watch?v=F923EuB06CI
- 7. Applications Today Led by Luxury, but moving mainstream JLR Smart Settings Audi MIB2+ BMW Connected+ Hyundai Smart Cruise Ford Mach-E Mercedes MBUX
- 8. 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
- 9. Why has it taken so long? Connectivity In-vehicle hardware Backend systems at carmakers Readiness of users But mostly: Because it’s hard.
- 10. 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
- 11. 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.
- 12. 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.
- 13. 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
- 14. 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
- 15. Example: 2 Variable Integration Do Nothing Confidence Level – Departure Time ConfidenceLevel-Destination 100%0% 100% Proactive push notification Pre-populated suggestions Proactive destination input
- 16. 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…
- 17. 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.
- 18. “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
- 19. 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/