Understand.ai is a German startup founded in 2017 that provides AI solutions for automotive and other industries. They have 53 employees with expertise in AI and automotive. Their customers include major automotive companies. The presentation discusses the importance of data for building effective AI systems, including the need for large quantities of high quality, diverse training and validation data. It also emphasizes the challenges of testing and monitoring AI systems once deployed.

1. Software 2.0 | Fueling the AI Revolution
www.understand.ai

2. Founded: March 10th, 2017
Research dates back to 2014
HQ: Karlsruhe (Germany)
Heart of a key European Automotive & AI cluster
Investment: $3.5M seed
From Valley, UK & German entrepreneurial VCs
Team:
53 associates, combination of AI & Automotive talent
Customers:
Automotive OEMs and Tier 1/2s
e.g. VDA V&V-Methoden (Pegasus successor)
Understand.ai
A young fast-growing start-up

3. Agenda
1. Where is AI used?
2. What is required to build an AI
system?
3. Characteristics of Data
4. Training & Validation Data
5. Data Engine
6. Processes and People
7. Conclusion
Fueling the AI Revolution

4. Where is AI used?

5. ● speech to text
● night vision
● translation
● search & find
● ...
Personal Assistance
AI as...

6. ● Perception
● Planning
● Routing
● Behaviour Models
● ...
Automotive
AI in...

7. ● Diagnose Diseases
● Develop drugs faster
● Personalize treatment
● Enable gene editing
● Chatbot as a personal doctor (X2.ai)
● ...
Medicine
AI in...

8. What is required to build an AI system?

9. Infrastructure

10. Algorithms
generated model performs significantly
better than manually designed model
Source: https://ai.googleblog.com/2017/11/automl-for-large-scale-image.html

11. Data
Amount of sleep lost over...
Algorithms
Data Sets
PhD Tesla
Source: Andrej Karpathy

12. Academia
Academia versus Industry
fixed dataset for
comparability variable, high sophisticated
algorithms
variable results, often
hard to reproduce

13. Industry
Academia versus Industry
variable, ever
changing data set variable but less
relevant algorithms
fixed performance
results needs to be reliably
reproducible under a very wide
range of different circumstances

14. Fixed Model Performance
Industry

15. Characteristics of Data

16. Data Quality
Distorting VOC Bounding Boxes
ground-truth small noise (0.08) large noise (0.13)
Source: VOC dataset

17. Data Quality
Distorting VOC Bounding Boxes
at loU 0.5 at loU 0.8
industry standard
mAP VOC 0.6996 0.4626
mAP VOC 0.08 0.6794 0.2262
mAP VOC 0.13 0.6400 0.0562
-40,64 pts
Source: VOC dataset

18. Quantity of Data
The more the better!
Source: Revisiting Unreasonable Effectiveness of Data in Deep Learning Era (https://arxiv.org/pdf/1707.02968.pdf)
object
detection
performance
on
COCO
object
detection
performance
on
PASCAL
VOC
2007

19. Diversity of Data
When is performance acceptable?
Source: Tesla Autonomy Day

20. Diversity of Data
Weird Edge Cases :)

21. Training & Validation Data

22. Validation Data
Best Practice
a satisfying run on the test set is no
guarantee for happy customers...

23. Validation Data
Best Practice

24. Required Validation Effort
Validation effort might be insane...

25. Validate Autonomous Driving
Be as good as a human driver
96 fatalities within 8.8bil
miles +/- 20%
325mio hours of driving at 25mp/h
Source: https://www.rand.org/content/dam/rand/pubs/research_reports/RR1400/RR1478/RAND_RR1478.pdf

26. Will we all be labelers?
Tesla: 1.000 labelers
Google: 6.000 labelers
Baidu: 20.000 labelers

27. Tooling to Create Data Sets
Data Engine

28. Data Engine
Turbo Boost Data Collection

29. Data Selection
Data Engine
Data selection as a key
technique to keep the
data flow under control
Most data is valueless!
Build product around the
problem to be solved: initial data
and later data needs to be
sourced as efficiently as possible

30. Training & Validation Data
Data Engine
Aim for coverage, no
matter how big the data
set grows
Keep train set well
balanced when adding
new scenarios
Data sets need to cover real
world variance!

31. Uncovered Areas
Data Engine
Visualize output, implement
statistics, analyze outliers, run
new models in shadow mode
Initial data will not cover real
world variance + over time the
world is changing (model
staleness)
Design product to make end-users
naturally train your system!

32. Processes and People

33. Machine Learning Model Development Process

34. Split into 3 Teams
One team to collect,
condense and massage
the data as well as to
train and validate the
algorithm
One team to enrich data
by labeling it and build
annotation tools
One team to build the data
pipeline and surrounding
stack to host the model

35. Traditional System Testing and Monitoring

36. ML-Based System Testing and Monitoring
Behavior depends on dynamic qualities of data and model configuration choices

37. Conclusion

38. UAI is an awesome company
Quality, Quantity and Diversity of training
and validation data enable high performance
AI Systems
Validation requirements might have an
insane impact on effort
Build a data engine!
QA for production grade ML Systems is
highly complex
Conclusion
It’s all in the data!

39. Q&A
Daniel Rödler
daniel@understand.ai
Software 2.0
Fueling the AI Revolution!
Feedback, please. Thanks!
Philip Kessler
philip@understand.ai