This document provides an overview of an AI project called XMANAI. It discusses: 1) Key AI concepts like definitions, policies, trends and explainable AI. 2) The challenges XMANAI aims to address like increasing trust and transparency in AI for manufacturing. 3) XMANAI's vision to develop explainable hybrid and graph AI models coupled with complex data and model management to solve manufacturing problems.

1. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957362
Technical Project Overview
2020-11-10 Kick-off Meeting
Dr. Fenareti Lampathaki (Suite5) – Technical Coordinator

2. TABLE OF
CONTENTS
01
02
03
04
AI Basics
Definition, Policies, Trends,
Explainable AI, Technologies
Rationale & Motivation
Key Challenges
Vision
Approach, Concept
XMANAI in Perspective
Methodology, Roadmap
2020-11-01 Kick-off Meeting 2

3. AI Basics
01
Definition, Policies, Trends, Explainable AI, Technologies
3
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4. • Artificial intelligence (AI) refers to systems that display
intelligent behaviour by analysing their environment and
taking actions – with some degree of autonomy – to
achieve specific goals.
• AI-based systems can be:
• Software-based, acting in the virtual world (e.g. voice
assistants, image analysis software, search engines, speech
and face recognition systems)
• Embedded in hardware devices (e.g. advanced robots,
autonomous cars, drones or Internet of Things applications)
Artificial Intelligence
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Sources: EC (2018) Communication from the Commission: Artificial Intelligence for Europe. SWD(2018) 137 final &
https://qbi.uq.edu.au/brain/intelligent-machines/history-artificial-intelligence

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AI at EU Policy Level
A Common European industrial (manufacturing) data space, to support the
competitiveness and performance of the EU’s industry, allowing to capture the potential
value of use of non-personal data in manufacturing (estimated at € 1,5 trillion by 2027).

6. 6
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AI Taxonomies
Thematic subdomains
Natural Language Processing
(NLP) & Generation(NLG)
Computer vision
AI Applications, Infrastructure,
Platforms, Software as a Service
(AIaaS, IaaS, PaaS, SaaS)
Machine learning (ML) methods
Robotics & Automation Processes
Connected & Automated Vehicles
(CAVs)
AI thematic subdomains and top-10 terms by relevance to the topic, of industrial and R&D
activities, 2009-2018 – Source: https://doi.org/10.1016/j.telpol.2020.101943
+ Based on breadth of intelligence, learning ability, type of application, learning paradigm, etc.

7. 7
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Trends in AI, Data Science & ML 2020
• Highlights from Hype Cycle for Data Science and Machine
Learning, 2020:
• On the Rise: Self-Supervised Learning - Federated
Machine Learning – Kubeflow - Transfer Learning
• At the Peak: Data Labeling and Annotation Services -
Explainable AI – MLOps - Augmented DSML - AutoML -
Deep Neural Networks (Deep Learning) - Prescriptive
Analytics
• Sliding Into the Trough: Graph Analytics - Advanced
Video/Image Analytics - Event Stream Processing
• Climbing the Slope: Predictive Analytics - Text Analytics
• Entering the Plateau: Apache Spark - Notebooks

8. • Explainability that reflects the ability to understand and explain in
human terms what is happening with an AI model and how it
works under the hood, promoting inspection and traceability of
actions undertaken.
• Interpretability that refers to the degree to which a human can
observe cause-and-effect situations, understand the cause of a
decision and anticipate how changes in the data or the model will
alter the results.
• Trustability that embraces the ability of AI models to provide
accurate, trustworthy and performant predictions.
Explainable AI (XAI)
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GDPR
AI: It’s a machine
failure or not?
XAI: Why it’s a
machine failure?
Source: https://www.cc.gatech.edu/~alanwags/DLAI2016/(Gunning)%20IJCAI-16%20DLAI%20WS.pdf

9. Explainability Aspects
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10. 10
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AI Technologies Landscape 2020

11. 11
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AI in Manufacturing
AI potential across the breadth and depth of manufacturing operations
Sources: https://www.capgemini.com/wp-content/uploads/2019/12/AI-in-manufacturing-
operations.pdf & https://www.pwc.com/gx/en/industrial-manufacturing/pdf/intro-implementing-ai-
manufacturing.pdf

12. Rationale & Motivation
02
Key Challenges
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13. 13
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Key Business Challenges
 “Why did the AI system make a specific prediction or decision?”
 “Why didn’t the AI system decide something else?“
 “When did the AI system succeed and when did it fail and what was the impact?”
 How to avoid undetected bias, mistakes, and miscomprehensions creeping into decision-making?
 How to facilitate robustness, accuracy and performance?
 How to ensure fair decision making?
 How to provide really actionable insights?
I. How to Increase Human Trust in AI
II. How to Increase Transparency and Reliability of AI
“The AI made us take this decision, not sure why…”

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Just some of the key “Data/AI” Challenges
I. Efficient and Secure Data
Management II. Traceable AI Model
Lifecycle Management III. Trusted Data and AI Model
Sharing
 Inconsistent, incomplete or missing
data with low dimensionality ->
overfitting or underfitting AI models
 Properly preparing and manipulating
the data (even 80% of time in AI
projects!)
 Packaging, deploying and scaling AI
models/pipelines in different
execution environments in an
interoperable manner
 Keeping track of AI experiments and
reproducing code and results
 Transfer learning
 Collaboration between data scientists,
engineers and business experts
 IPR-compliant “assets” exchange
 Equilibrium point for AI-related assets
handling
 AI’s “transparency paradox” & Ethics

15. Vision
03
Approach, Concept
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16. 16
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Vision at a glance…
• WHY? For optimizing performance and manufacturing products’ and processes’ quality… For accurately forecasting
product demand… For production optimization and predictive maintenance… For enabling agile planning processes… For
understanding and cultivating different skills that are required in manufacturing in order to transition to the AI era
“XMANAI aims at placing the indisputable power of Explainable AI at the service of manufacturing and human progress, carving
out a “human-centric”, trustful approach that is respectful of European values and principles, and adopting the mentality that
“our AI is only as good as we are”. XMANAI will help the manufacturing value chain to shift towards the amplifying AI era by
coupling (hybrid and graph) AI "glass box" models that are explainable to a "human-in-the-loop" and produce value-based
explanations, with complex AI assets (data and models) management-sharing-security technologies to multiply the latent data
value in a trusted manner, and targeted manufacturing apps to solve concrete manufacturing problems with high impact. ”
Target Users: Data Scientists & Data Engineers; Business Users in Manufacturing

17. Explainable AI Circles in XMANAI
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AI Axis I. Basic Analytics
AI Axis II.
Machine Learning
AI Axis III. Deep Learning
Graph AI Algorithms
- Understand AI Models -
Hybrid AI Algorithms
- Understand AI Results -
Traditional AI Algorithms –
Understand Data -
Multi-Party Asset Contracts for acquiring AI Models, Explanations, Results and / or (Missing) Industrial Data
Trust Level 1 - Emerging XAI Circle
Trust Level 2 - Developing XAI Circle
Trust Level 3 - Established XAI Circle
Sample Data
Exploration
Features
Visualizations
Elicited
Explanations
Features
Surrogate
Models
Directly
Interpretable
Models
Explanation
Interfaces
Knowledge Graphs ->
Graph Feature
Engineering -> Graph
Native Learning

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XAI Models Portfolio in XMANAI
OBJ.1: >30 hybrid AI baseline models. >15 graph AI baseline models. >16 hybrid trained AI models. >8 trained graph AI models. >4
surrogate models to increase explainability for hybrid AI models. >4 manufacturing problems addressed
ΧΜΑΝΑΙ Catalogue of
Explainable AI models
Hybrid ML/DL
Algorithms
Graph ML/DL
Algorithms
Baseline Models
Hybrid ML/DL
Algorithms
Graph ML/DL
Algorithms
Trained Models
Surrogate Models:
 SHapley Additive exPlanations (SHAP)
 Local Interpretable Model-agnostic Explanations
(LIME)
 Causal Models to Explain Learning (CAMEL)
 …

19. XMANAI Journey though the “Business Expert”
Perspective
2020-11-01 Kick-off Meeting 19
Business Expert Perspective
Extract Data
Data
At rest
Data
In motion
 Data Ingestion
 Data Mapping and
Semantic Annotation
 Data Cleaning
 Data Provenance
 Data Update
Management
XMANAI Data/Model
Management Bundle
Safeguard Data
 Data Security and
Privacy Assurance
 Data Access Policies
Management
 Data Licensing
 Secure Data Storage
in multiple
modalities
XMANAI Data/Model
Management Bundle
XMANAI Secure
Asset Sharing Bundle
Share / Acquire
(Missing) Data
Bilateral Asset Contract…
 Creation
 Approval
 Negotiation
 Signature
 Lifecycle Management
XMANAI Core AI
Bundle
Understand Data
 Exploratory Data
Analysis
 Data Modelling
 Data Curation
 Knowledge Graph
Generation
Manufacturing
Systems
&
XMANAI
Demonstrators
Applications
 Interactive Results’
Visualizations
 Results and
Explanations
Extraction
 Notifications
XMANAI AI Insights
Bundle
Derive
Intelligence
AI Results

20. 20
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XMANAI Journey though the “Data
Scientist” Perspective
Data Scientist Perspective
XMANAI Core AI
Bundle
Understand Data
 Exploratory Data
Analysis
 Data Modelling
 Data Curation
 Knowledge Graph
Generation
XMANAI Core AI
Bundle
Manipulate Data
 Data Transformation
 Data Linking / Merging
 Graph Embeddings
 Dimensionality
Reduction
 Feature Storage
XMANAI Secure
Asset Sharing Bundle
Acquire (Missing)
Data
Bilateral Asset Contract…
 Creation
 Approval
 Negotiation
 Signature
 Lifecycle Management
Evaluate the AI
Model
 Cross Validation
 Model and Experiment
Explanation
 Expert Collaboration
and Validation of
(Visualized) Results
 Model Safeguarding and
/ or Sharing
XMANAI AI Insights
Bundle
XMANAI Core AI & AI
Insights Bundles
Fit the AI Model
 Training
 Feature Tuning
 Experiment Planning
 Experiment Execution
 Surrogate Models
Execution
 Experiment Tracking
XMANAI Core AI & AI
Insights Bundles
 AI/ML Pipeline
Definition
 Feature Engineering
 Surrogate Models
Definition
 Models Storage
Build an AI Model

21. 21
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XMANAI Journey though the “Data
Engineer” Perspective
XMANAI Core AI & AI
Insights Bundles
Fit the AI Model
 Training
 Feature Tuning
 Experiment Planning
 Experiment Execution
 Surrogate Models
Execution
 Experiment Tracking
XMANAI Core AI & AI
Insights Bundles
 AI/ML Pipeline
Definition
 Feature Engineering
 Surrogate Models
Definition
 Models Storage
Build an AI Model
Data Engineer Perspective
 Model Packaging
 Model Deployment
 Model Immediate /
Scheduled Execution
 Model Scaling
XMANAI Core AI
Bundle
Deploy the AI
Model
Manufacturing
Systems
&
XMANAI
Demonstrators
Applications
AI Models

22. 22
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XMANAI Architecture
Core AI Management Platform
Open
APIs
Secure Execution Clusters (SEC)
Process Optimization App
XMANAI ON-PREMISE ENVIRONMENTS
Stakeholders’ On-Premise Environment (OPE)
XMANAI CLOUD INFRASTRUCTURE
XMANAI MANUFACTURING APPS PORTFOLIO
Product Demand
Forecasting App
Process/Product Quality
Optimization App
Process Optimization & Semi-
Autonomous Planning App
Services in a nutshell:
• Data & Models
Collection Services
• Scalable Storage
Services
• Data Manipulation
Services
• AI Model Lifecycle
Services
• AI Execution
Services
• AI Insights Services
• Secure Asset
Sharing Services
• Data & Models
Governance
Services

23. AI for Production
Optimization
AI for Product Demand
Planning
AI for Process/Product
Quality Optimization
AI for Smart Semi-
autonomous Hybrid
Measurement Planning
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XMANAI Demonstrators

24. XMANAI in Perspective
04
Methodology, Roadmap
24
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25. XMANAI Methodology
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Analyzing the
Manufacturing
Industry Needs
Early Preparing
Market Entry
Developing the
XMANAI Platform
 State-of-play analysis, Ethics
and human aspects in
decision making and AI
 Industrial needs & barriers
identification
 Business scenarios and
requirements consolidation
 Research agenda elaboration,
Minimum Viable Product
(MVP) conceptualization
 Market analysis
 Detailed exploitation
strategy
 New AI-enabled business
models
 Business model and plan
 Business cases from pilot
experience,
 Scale-up, Transfer Learning
and Replication
 Dissemination,
Communication &
Marketing
 Stakeholder engagement
 Industrial data management,
sharing and AI models
lifecycle management
methods elaboration
 Manufacturing data in depth-
exploration
 Baseline and trained XAI
models for manufacturing
 Architectural design
 Development of XMANAI
Data Services Bundles
 Integration of the XMANAI
core platform & on-premise
environm.
I IV
II
Verifying, Validating
and Demonstrating
the XMANAI Platform
 XAI Models Evaluation
 XAI Models Ethics and
Security assessment
 Project Verification &
Validation framework
elaboration
 Platform Technical
Verification and Validation
 Demonstrators Preparation
and Implementation
 Business Validation
 Impact Assessment &
Lessons learnt
III

26. Project “Technical” Roadmap
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M2
M37
M6 M8 M9 M11
Updated MVP
Asset Mgmt Bundles-1st
Rel & AI Bundles-1st Rel
Eval Plan
M3
M40 M39 M38
M42
M4 M5 M10
M7 M12
M23 M19 M17 M16 M14
M22 M21 M20 M15
M18 M13
M24
M26 M30 M32 M33 M35
M27 M28 M29 M34
M31 M36
M25
M41
Platform-Alpha Version
Final AI Models
Platform v1.0
Demo Results-Beta Phase & Impact Assess.
Concept & MVP
Architecture
Asset Mgmt Bundles Design
AI Bundles Design
Final MVP
SoTA
Demonstrators Reqs
Asset Mgmt Bundles-2nd Rel
AI Bundles-2nd Rel
Demo Results-Alpha Phase
Draft AI Catalogue
AI Models-1st Round
AI Models-2nd Round
Platform-Beta Version
Updated Demo Reqs

27. • “Research & Innovation”… Expectations are really high !
• High-quality results must be submitted to the EC on
time
• Teamwork… Clear roles and commitments already in
the XMANAI consortium
• Grasp the opportunities to innovate, align to the actual
needs and make a tangible impact in industry
27
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Key Messages
I. Without
Data, there is
no AI !
II. Without AI
explainability,
there is no trust !
III. Without AI
ethics, there is
no adoption !
If humans do not understand why/how a
decision/prediction is reached, they shall not
adopt/enforce it…
MAKING AI UNDERSTANDABLE !

28. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957362
Thank your for your attention!
28
fenareti@suite5.eu

29. This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 957362
www.ai4manufacturing.eu
info@xmanai.eu
/XMANAI
/XMANAI
/XMANAI
29
Dr. Fenareti Lampathaki
Technical Director
Suite5 Data Intelligence Solutions Limited
www.suite5.eu
fenareti@suite5.eu
/fenareti
/fenareti.lampathaki
/ fenareti.lampathaki