1. Forget about AI and do
Mathematical Modelling instead!
Florian Wilhelm @ inovex
Artificial Intelligence

2. Dein
Foto
hier
Mathematical Modelling
Data Science to Production
Causal Inference
Uncertainty Quantification
Python Data Stack
Creator of PyScaffold
@FlorianWilhelm
FlorianWilhelm
FlorianWilhelm.info
2
Dr. Florian Wilhelm
Head of Data Science @ inovex

3. inovex is an IT project house
with focus on digital transformation
› Product Discovery · Product Ownership
› Web · UI/UX · Replatforming · Microservices
› Mobile · Apps · Smart Devices · Robotics
› Big Data & Business Intelligence Platforms
› Data Science · Data Products · Search · Deep Learning
› Data Center Automation · DevOps · Cloud · Hosting
› Agile Training · Technology Training · Coaching
Karlsruhe · Pforzheim · Stuttgart · München · Köln · Hamburg
www.inovex.de/en
Using technology to inspire our clients.
And ourselves.

4. Why this talk?
4

5. The wonderful World of AI
5
headlines taken from nature.com

6. … but is it actually?
6
https://spectrum.ieee.org/view-from-the-valley/artificial-intelligence/machine-learning/andrew-ng-xrays-the-ai-hype

7. Hype Cycle
7
AI

8. What is meant by AI?
AI often means machine learning,
especially deep learning, i.e. deep
neural networks.
8
Expectation: Give it some data and
it will magically do anything.

9. Some Experiences
That Made Me Think 🤔
9

10. Predicting the Efficiency of Nuclear Power Plants
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https://www.nuclear-power.com/nuclear-engineering/thermodynamics/laws-of-thermodynamics/second-law-of-thermodynamics/carnot-efficiency-efficiency-of-carnot-heat-engine/

11. Size Prediction for Packaging
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L
M
S
Erdbeere

12. Size Prediction for Packaging
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Erdbeere
Linear Programming
Source: https://en.wikipedia.org/wiki/Bin_packing_problem

13. Covid-19: Inference vs Prediction
Some use-cases require no prediction but rather an inference
of latent factors, e.g. reproduction number and change points.
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Source: J. Dehning et al. (2020). Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions (v2020.05.01).
Zenodo. https://doi.org/10.5281/zenodo.3780722

14. Downsides of
AI / Deep Learning
14

15. Need for Interpretable Models
› Crucial for high-stake decision making, e.g. medicine,
law enforcement, banking, etc.
› Transparency about assumptions and (inductive) biases,
relevant for society, diversity, etc.
› Needed to build trust in the users of the models. Is it
secure, compliant and robust?
15
Source: “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead”, Cynthia
Rudin, Nature Machine Intelligence, 2019

16. Explainable AI (XAI) vs. Interpretability
Explaining black box models is not enough!
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Explaining the model vs. explaining the world!
Source: “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead”, Cynthia
Rudin, Nature Machine Intelligence, 2019

17. Do we actually have Big Data? …
… or is it just
an illusion?
17
Source: https://en.wikipedia.org/wiki/Ames_room

18. Interpolation vs Extrapolation
18
Source: http://www.statistics4u.com/fundstat_eng/cc_ann_extrapolation.html
Extrapolation is only possible through explicit modelling or generalization.
Extremely hard for Neural Networks and ML methods, also humans 😀 .

19. Problems with AI/Deep Learning
1. data-inefficient i.e. lots of data needed
2. hard to interpret and explain
3. lacks robustness thus trustworthiness
4. reflects desired and unwanted biases from the data
5. hard to reason about and gain further insights
19

20. 20
Just think of “AI” as a database
with a fuzzy index!

21. Toy Data Challenge
21

22. Just some Data…
22
Feature
Target

23. … that we split into Train & Test…
23
Feature
Target

24. … and fit several ML Algorithms.
24
Feature
Target

25. Extrapolation 😱
25
Feature
Target

26. What is this data anyway?
26

27. Let me think!...
27

28. Free Fall!
28

29. Our InoCube
29
https://www.inovex.de/de/leistungen/internet-of-things/projekt-inocube/

30. Predicting Throwing Height with InoCube
30
https://www.inovex.de/de/leistungen/internet-of-things/projekt-inocube/

31. What is
Mathematical Modelling?
31

32. What is actually a Model?
32
“Model is an algorithm trained on data”
train Model
Algorithm
Data

33. Two Perspectives
33
Algorithm Mathematical World
Algorithms are designed to
describe the actual problem
at hand.
AI/ML-World
Algorithms, e.g. neural
networks, are tailored to
approximate any kind of
data.

34. Mathematical Modelling
“Mathematical modelling is the art of translating problems
from an application area into tractable mathematical
formulations whose theoretical and numerical analysis
provides insight, answers, and guidance useful for the
originating application.” - Arnold Neumaier
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https://www.mat.univie.ac.at/~neum/model.html

35. High Relevance in Real-Life Projects
› Understanding the
problem & domain is key
› Simple trumps complex
most of the time
35

36. There is no Black & White, it’s a Spectrum
36
Deep Learning
CC BY-SA 4.0: https://en.wikipedia.org/wiki/Convolutional_neural_network#/media/File:Typical_cnn.png
Machine Learning
Mathematical Modelling

37. Simplified Mathematical Modelling Cycle
37
Greefrath, Gilbert & Vorhölter, Katrin. (2016). Teaching and Learning Mathematical Modelling: Approaches and Developments from
German Speaking Countries. 10.1007/978-3-319-45004-9_1
Start

38. When to use Mathematical Modelling?
› gain insights in your data
(e.g. latent factors)
› integrating available
domain knowledge
› structured data like tabular
data
› sparse or lack of much data
› feedback loops
› high-stake decision-making
(interpretability,
uncertainty )
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› unstructured data (text,
images, …)
› no domain-knowledge
available
› short-term results are
required

39. Mathematical Modelling is about
Understanding the Problem.
39

40. Thank You!
Florian Wilhelm
Head of Data Science
inovex GmbH
Schanzenstraße 6-20
Kupferhütte 1.13
51063 Köln
florian.wilhelm@inovex.de