An intro into AI and how business leaders should use it

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Angela Merkel is a boy!

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AI & Business
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Is Angela Merkel A Boy?
Campaign Image 2017

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Let’s see...
https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/

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A Boy!

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AI started in 1956
Source: Wikipedia
https://www.futuretimeline.net/forum/topic/21039-broken-promises-empty-threats-the-evolution-of-ai-in-the-usa-1956-1996/

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1st. Wave: ML
Source: Wikipedia
BEFORE:
● Program Each Step
NOW:
● Symbolic Artificial
Intelligence > Rule
Based
Learn how to Code with Swift

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2nd. Wave: More Rules
Source: Wikipedia
BEFORE:
● AI limited to small
applications like
Checkers
NOW:
● Aiming to build
systems to augment
the knowledge of
doctors
“We know more than we can tell”
Michael Polany 1958

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3rd. Wave: Computer Designs Rules
Find likelihood for a SERIOUS DELINQUENCY
(2y past due for mortgage)
AI Optimizes to find the best Decision.
AI writes the if-then Rules.

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Angela Merkel is 65 y old

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RULE 1:
AI is a Tool
Not
A Business Model

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AI Can
Create New Products
https://eng.uber.com/atg-dataviz/
https://www.symrise.com/newsroom/article/breaking-new-fragra
nce-ground-with-artificial-intelligence-ai-ibm-research-and-symr
ise-are-workin/
New Products Can
Create New Business

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Success is based on your
Business Model

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Digitalization
Has Changed Business Levers
From: https://stratechery.com/2015/aggregation-theory/
Example: Media Industry
Media Publication 1
Media Publication 2
Media Publication 3
Many
Suppliers
Few
Suppliers

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The Levers
• Direct Relationship with Users
• They Own the Relationship
• No One is between Demand and them
• Zero Marginal Costs For Serving Incremental Users
• No COGS
• No Distribution Cost
• No Transaction Cost
• Abundant Supply
• With either zero marginal acquisition cost
• OR acquisition cost that scales
Those are attractive terms for the business. Data improves over time
the service and creates a moat for competition. But data does not
create the business model.
Further reading: https://stratechery.com/

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Business Imperative
• Amazon One Vendor
• Tesla Over The Air Software Update
• Netflix Originals
• Disney New Streaming Service
• Uber city-per-city investment in Supply
• Facebook Instant Articles
Who Is Pushing Into Your Space?

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Tools Create New Product & New Business
Tools Help To Optimize Your Core Biz.
https://eng.uber.com/atg-dataviz/
https://www.symrise.com/newsroom/article/breaking-new-fragra
nce-ground-with-artificial-intelligence-ai-ibm-research-and-symr
ise-are-workin/
● Banking: Card Fraud Detection
● Banking: Credit Scoring
● Media: Content Recommendation
● Health Care: Fraud Detection
● Medicine: Image Processing
● Medicine: Outliers Detection
● Retail: Likelihood to Buy
● Books: Marketing Planning
● Manufacturing: Failure Prediction
● Manufacturing: Optimization
● Insurance: Likelihood & Pricing
● Transportation: Route Planning
● Energy: Grid Utilization

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Insurance
● Damage Estimations: Using Deep Learning &
Satellite Images to understand damage of the
Camp Fire in 2018
● Insurance Premiums:
Praedicat mines
literature to extract
metadata about bodily
harm that can be
attributed to certain
materials.
https://www.space.com/41403-california-wildfires-2018-photos
-from-space.html
● Claim Management & Indemnity Prediction: Digital
workflows improve workflows and predictions (AI
Insurance)

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Oil & Gas
● Clean up data from pressure transient analysis (PTA)
using Singular Spectrum Analysis (SSA) and a
supervised learning
● Methode can replace the manual task of identifying the
flow regime.
https://doi.org/10.1145/3292500.3330661
https://www.kdd.org/kdd2019/accepted-papers/view/structured-noise-detection-application-on-well-test-pressure-derivative-dat

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Retail Credit Loan Applications
(RNN over debit or a credit card transactions)
1) More accurate than
traditional
2) Less human overhead (raw
transaction data >> no
feature needed)
3) Fast as no additional
customer input is needed
(transactional data)
4) Hard to forge signals
(transactional data)
5) Works for customers without
credit history https://doi.org/10.1145/3292500.3330693

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Customer Care
3
Disclaimer: I am an angel investor in ultimate and i2x.
https://medium.com/sap-io/how-an-ai-powered-contact-center-solution-ca
n-save-62-billion-in-lost-sales-b53e3c906cb6

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Fitbit for Cows
Helping to manage health from cows to improve milk
production.
The only difference between cows that produce 30
liters of milk a day and those that produce 10 liters
was the animal’s health.
https://itunes.apple.com/app/ida-by-connecterra/id1246795438
https://www.connecterra.io/ida/registration-for-ida-event-at-neu-hope-dairy-usa/ida-dashboard-en/

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RULE 2:
Not More Data
But Right & Protected Data

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AI needs Data
from Marketing Material of Ursa Space Systems RSMetrics
https://medium.com/@RSMetrics/metalsignals-tracking-the-worlds-shadow-sup
ply-with-satellite-imagery-f4f1c67717c8

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You Need to Protect
Your Data

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Data Can Be Shared - Central

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… and Decentral > Federated Learning
APPLIED FEDERATED LEARNING: arXiv:1812.02903v1
Step 1: Train Model in Cloud
Initial Data
Model

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Federated Learning
Step 2: Distribute the Model &
Improve Model Local
Company X
Company Y
Company Z
Initial Data
Model

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Federated Learning
Company X
Company Y
Company Y
Step 3: Upload Model not the
Data & Merge Models
Initial Data
Model
Improve Model Local

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Federated Learning
Company X
Company Y
Company Y
Step 3: Upload Model not the
Data & Merge Models
Improve Model Local
Repeat

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Data has also
Diminishing Returns
by Claudia Perlich et. al., “Tree Induction vs. Logistic Regression: A Learning-Curve Analysis”, Journal of
Machine Learning Research 4 (2003) 211-255

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There is a Way to
Jumpstart Your Data Needs

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Transfer Learning
Transfer Learning is a
strategy where a model
used for one tasks (e.i.
recognition of animals)
used as starting point for
the second task (e.i.
recognition of faces)
Step 1: Peel Back
Layer
Step 2: Add Layer &
Re-Train with Your
Data

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Filter Layers
Your Model Knows Animals
See: https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html?m=1

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Transfer Learning at Work
Combining historical mining and exploration data with NASA data and use pre-trained deep
learning networks to identify several target zones with the highest potential for gold
mineralization in Nevada’s Jerritt Canyon district.

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RULE #3:
Regulation & Ethics Concerns
Should Guide You
Not Stop You

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The Latecomer Argument
“AI is the most
serious threat to the
survival of the
human race”
Elon Musk

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Regulation Considerations
Ability to Explanation
“[...] the controller shall provide [...]
the following information: the
existence of the automated
decision-making and [...] meaningful
information about the logic involved
and the envisaged consequences of
such processing for the data
subject.”
Art. 13-15 & 22 Regulation (EU)
2016/679

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One solutions: LIME
( local interpretable model-agnostic explanations)
https://github.com/marcotcr/lime AND https://homes.cs.washington.edu/~marcotcr/blog/lime/
Predict on PermutationTake Data Point(s) = Local Permute Data Points (+ Noise)
Use distance and other similarity
metrics to understand the most
important (not permuted) features.

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Biased Data Concerns
Trainings Data
Label “dogs” Label “cats”
Predictive Model
Input Data
Class “cats”

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Solutions?
Compare counterfactuals
https://pair-code.github.io/what-if-tool/
https://pair-code.github.io/what-if-tool/ai-fairness.html
https://arxiv.org/abs/1610.02413
What is Fairness?

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RULE #4:
Think Holistically

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AI needs the whole company
● Business Model
● Data
● People
● Infrastructure
● Organizational Set Up
And… Leadership

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Example: China
People
Leadership
Data
Infrastructure
doi: 10.1038/d41586-019-02360-7 AND https://flia.org/notice-state-council-issuing-new-generation-artificial-intelligence-development-plan/
https://macropolo.org/chinas-ai-talent-base-is-growing-and-then-leaving/
https://www.wired.com/story/china-catching-up-us-in-ai-research/

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Example: Deutsche Bahn

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You are not alone

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Your Cookbook
1. AI is a Tool Not A Business Model
a. Not everything is a unicorn
b. Biz improvements are a good start
2. Not More Data But “Right” & “Protected” Data
a. Own / protect the access
b. Use federated learning to share models
c. There are diminishing returns in data
3. Regulation Should Guide You Not Stop You
a. Use LIME to understand the model
b. Test for Biases
4. Think Holistically
a. Hire data scientists and engineers
b. Set a AI minded culture

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Lutz Finger
Disclaimer
This content is solemnly my own opinion and not
necessarily the opinion of any of my employer or
former employers.

An intro into AI and how business leaders should use it

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An intro into AI and how business leaders should use it