Presentation of the NATF report on Artificial Intelligence and Machine Learning.

1. Artificial Intelligence and Machine Learning – July 2018 1/14
Yves Caseau
National Academy of Technologies
Michelin CIO
Taking Advantage of AI
July 11th
, 2018
V0.4

2. Artificial Intelligence and Machine Learning – July 2018 2/14
OutlineOutline
Artificial Intelligence and
Machine Learning « revolution »
A glance at the « toolbox »:
methods, protocols and assembly
A « how to guide » for corporation
How to grow « emergence » ?
1:AI“renewal”1:AI“renewal”2:The« Toolbox »2:The« Toolbox »3:Calltoaction3:Calltoaction

3. Artificial Intelligence and Machine Learning – July 2018 3/14
AI « renewal » /AI « renewal » /
Technology academy workgroupTechnology academy workgroup
Spectacular Investment Acceleration
 Major players and venture capital
 Belief that major benefits are yet to
come
Spectacular Performance Acceleration
 Image, speech recognition,
translation, ….
 Alpha Go, etc. 
 Moore’s law does not explain
everything
Workgroup questions
 Revolution or evolution ?
 AI Algorithms = commodity ?
 « Exponential Organization » ?
1:AI“renewal”1:AI“renewal”

4. Artificial Intelligence and Machine Learning – July 2018 4/14
Taking advantage of AI availabilityTaking advantage of AI availability
Vaccine Manufacturing at Merck
 5 Terabytes in a “Datalake”
 Batch yield optimization
IHG Continental (hotels)
 ultra-fine customer segmentation client
 Similar approach at Amadeus
Ejection fraction analysis (cardiology)
 Contest prepared by doctors and cardiologists
 DNN to compute a volume through image analysis
FAA
 Long term delay forecast through a Bayesian
network
 Taking “avalanche effect” into accounts
 5 years of data, 52 millions flights – noisy data
1:AI“renewal”1:AI“renewal”

5. Artificial Intelligence and Machine Learning – July 2018 5/14
1:AI“renewal”1:AI“renewal”
Most AI application are built on top of a feedback loopMost AI application are built on top of a feedback loop
Iterative
Developement
of AI Practice
Speed of learning
depends on
computing power
Smart
Algorithms
Smart
Engineering Smart
Services
Service
Usage
Growing
Large
Datasets
Distributed Software
Engineering Practices
Distributed Software
Engineering Practices
Management Vision
& Grit
Management Vision
& Grit
Ease to collectEase to collect Trust &
Acceptability
Trust &
Acceptability
A
Scientists
Open source
Startups
B-
Lack of SW
medium
sized
players
C+
Risk-adverse
Lack of SW
culture
B+
Market
Size /
language
B-
GDPR
CNIL
B+
Competitive
access to
GPU/TPU

6. Artificial Intelligence and Machine Learning – July 2018 6/14
Today’s Artificial Intelligence (& ML) makes an extended toolboxToday’s Artificial Intelligence (& ML) makes an extended toolbox
Open
question
Question
précise
Few data Lots of data
classical
« Data Science »
methods
Rules
OR / NLP
Agents
Evolutionary Game Theory
Deep Learning
(CNN)
Semantics
(e.g., Watson)
• Rule-based and
constraint-
based (e.g.,
configuration)
• Fuzzy
boundary with
operations
research
• Most companies
“AI use cases”
• Einstein /
TellMePlus /
Da Vinci Labs
• Moore’s Law &
Big Data
• Key role of
simulation
• Well suited to
complex
systems
• Continuous
but slow
progress
• News articles
written by
robots
• Pattern/situation
recognition
• This decade’s
inflexion point
Intelligence Artificielle et Apprentissage Automatique –May 2018 8/16
Quelques éléments clés de la boîte-à-outils
l Regression linéaire
/ logistique
l Réseau Bayésien
l Régularisation
l (K-mean) clustering
l Random Forest
l Gradient Boosting
l Support-Vector
Machines
l Réseaux
Neuronaux
l Ontologies
l Lexicographie
l ARMA, ARIMA, etc.
2:La“boïte-à-outils
2:The« Toolbox »2:The« Toolbox »

7. Artificial Intelligence and Machine Learning – July 2018 7/14
Some key pieces in the toolboxSome key pieces in the toolbox
l Linear / logistic
regression
l Bayesian networks
l Regularization
l (K-mean) clustering
l Random Forest
l Gradient Boosting
l Support-Vector
Machines
l Neural Networks
l Ontologies
l Lexicographic tools
l Rule-bases scripting
l ARMA, ARIMA, etc.
2:The« Toolbox »2:The« Toolbox »

8. Artificial Intelligence and Machine Learning – July 2018 8/14
Meta-Heuristics to mix these componentsMeta-Heuristics to mix these components
l Reinforcement learning
l Transfer learning
l Natural language processing toolbox
l Large-scale Intelligent agents
communities
l Game theory to reason about
competition and cooperation
• Hybrid AI: to combine
different tools and
meta-heuristics
• Generative approaches
2:The« Toolbox »2:The« Toolbox »

9. Artificial Intelligence and Machine Learning – July 2018 9/14
Cognitive Systems: Mixing various AI andCognitive Systems: Mixing various AI and
Machine Learning TechniquesMachine Learning Techniques
 Smart System
Components:
 Perception / environment
 Self-consciousness of
goals
 Forecast and adjust
 Growth through usage
 Biomimicry
 Develop through
reinforcement
 Add layered capabilities
for resilience
 Cognitive computing
 “reason from a purpose” –
IBM
 “systems grow by
machine learning, not by
programmatic design”
EDA
Objects &
sensors
user
CEP
Reflexes
ACT
command
center
state
react
history
THINK
Decisions
(AI)
PLAN
Execution
Logic
goals
REFLECT
Evolutionary
ML
ANALYZE
Machine
Learning
ADAPT
Reactive
LEARN
Representation
VALUATION
emotions
services Other systems
Systems of
Systems
FORECAST
Anticipation
decide
insights
2:The« Toolbox »2:The« Toolbox »

10. Artificial Intelligence and Machine Learning – July 2018 10/14
AI strategy starts with data collectionAI strategy starts with data collection
Data collection process
Do not forget meta-data !
Build qualified training sets
“System thinking” (loop) :
collect tomorrow’s data as well as past
data
Prepare « machine vision » revolution
(& perception) through collecting images
and video … as well as customer digital
traces.
3:Calltoaction3:Calltoaction
Data
Lots of them, tagged
Algorithms
most often, open-source
Integration & meta-
heuristics
Training Protocols
Time & resourcesSkills / experience

11. Artificial Intelligence and Machine Learning – July 2018 11/14
Grow the success conditions for your teamsGrow the success conditions for your teams
Leverage the
« technology wave »
Beware of « false positives »
 Overfitting, Spurious correlations, ..
… and of biases in training data
Mindset: distributed and emergent innovationMindset: distributed and emergent innovation
Data collection/ training setsData collection/ training sets
AI-friendly software environmentsAI-friendly software environments
Lab Culture (Data Science)Lab Culture (Data Science)
PerseverancePerseverance
Constant
flow of
software
It takes
time to
build skills
3:Calltoaction3:Calltoaction

12. Artificial Intelligence and Machine Learning – July 2018 12/14
Time to act is nowTime to act is now
 Start right now with tools
that are easily available
 Simple methods work
 Take advantage of
« integrated/Automated »
toolboxes
 Einstein, Holmes,
TellMePlus, etc.
Secure access to large-scale computing
power to increase the speed of learning
(GPU & TPU)
3:Calltoaction3:Calltoaction
Research &
Development
Digital
Manufacturing
Deliver
Product
Supply Chain
Assist
Customer
Pattern detectionPattern detection
Customer Interaction (e.g. Chatbots / Smart Assistants)Customer Interaction (e.g. Chatbots / Smart Assistants)
Operations Support / Information Systems
Digital Traces - IOT
Operations Support / Information Systems
Digital Traces - IOT
FraudFraud
Predictive maintenance
Quality Assurance
Automation Forecast / Optimization
Robotic Process AutomationRobotic Process Automation
Knowledge EngineeringKnowledge Engineering SearchSearch

13. Artificial Intelligence and Machine Learning – July 2018 13/14
To develop one’s situation potential (emergence)To develop one’s situation potential (emergence)
Artificial Intelligence is not a service that you buy,
it is a practical skill that one must grow.
 It takes time …
Learning curve
 To develop the kind of AI that is suited to one’s business
 To work within a small team with outside experts (e.g., from academia)
 To organize contests with business training sets
 To build a continuous improvement process
Think Platform
 Large scope vision
(upstream & downstream value chain)
 « Win/win » : learn to share data
 Example: Today’s “stupid” chatbots
collect data that will be used to train
tomorrow’s smart assistants
3:Calltoaction3:Calltoaction

14. Artificial Intelligence and Machine Learning – July 2018 14/14
Main take-awayMain take-away
These are the five domains that anyone
should start investigating without delays:
1.Smart Automation: RPA scripting tools,
Rule engines
2.Natural Language Processing:
Bots & ontologies
Sentiment analysis API
3.Pattern recognition :
Random Forests, Neural Nets
4.Forecasting : Machine
Learning Toolboxes /
Prediction API / Bayesian Networks
5.Machine Vision : play with CNN
(TensorFlow)
ConclusionConclusion