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WELCOME
LORENZO SPERANZONI (@inserpio)
● CEO @ LARUS Business Automation
● Neo4j Ambassador
● Love coaching & teaching
● Still love coding a lot
● R&D
● Venice, Italy
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WELCOME
SURYA JOSYULA
● Director of Marketing @ FUJITSU LABS
● Working on outbound initiatives for new innovations
● Supporting co-creation with customers and partners
● Previously, he spent 15 years @ SUN Microsystems in various
engineering and marketing roles
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2016
Neo4j JDBC Driver
20152011
First Spikes
in Retail for
Articles’
Clustering
2014 2018
Neo4j APOC, ETL,
GraphQL, Spark
2019
Neo4j Kafka Connector
2020
PREMIER Partner
AI Based on Neo4j
Neo4j Spark Connector
LARUS NEO4J
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Founded in 2004
HQ:Venice
Offices: Pescara, Rome, Milan
Global services
International projects
Data Engineer, Data Architect,
Data Scientist, Big Data
certified experts team
We help companies to
become insight-driven
organizations
Leader in development of data-
driven application based on
NoSQL & Event Streaming
Technologies.
LARUS: ABOUT US
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Big Data Platform Design &
Development (java, Scala,
Python, Javascript)
Data Engineering
Graph Data Visualization
Data Science
Strategic Advisoring for Data-
Driven Transformation Projects
Machine Learning and AI
graph based technology
LARUS: OUR SPECIALTIES
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Connectors and
Graph Integration Framework
official connectors to integrate
graph technology with other big
data systems or software products.
Data Visualization
tools to assist users in visualizing in a
user-friendly way the large amounts
of data and the insights derived from
it.
NoSQL databases
databases which allow to store
and retrieve data in a much more
scalable and flexible way than the
traditional relational databases.
Event Streaming
Event streaming: streaming of large
volumes of events in real-time.
Machine Learning
give computers the ability to learn
without explicitly being
programmed.
Distribute Processing
distributed computing): using a
network of computers to process
data.
LARUS: OUR TECHNOLOGIES
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FLA: Fujitsu Laboratories of America Inc
(Est. 1993)
▪ Trusted AI (Graph XAI)
▪ AI Ethics
▪ Software Engineering
▪ Sensibility Science
▪ Security
▪ Next generation computing (Digital
Annealer)
▪ Marketing/Open Innovation
Fujitsu Laboratories
in Japan
▪ Services and Solutions
▪ Human Centric
Computing
▪ IT Systems
▪ Network Systems
▪ Device and Materials
▪ Platform Technologies
FRDC: Fujitsu Research
& Development Centre
Co Ltd (Est. 1998)
▪ Communication
Systems
▪ Web Information
Processing
▪ System LSI
FLE: Fujitsu Laboratories of
Europe Ltd (Est. 2001)
▪ Cloud Computing
▪ Petascale Applications
▪ Sensing Solutions
(Healthcare)
▪ Next-Generation Wireless
Communications
FUJITSU’S GLOBAL LABORATORIES
14. ( 14 )
Graphs are a
foundational
building blocks of
the next
generation of
Machine Learning.
“The application of graph
processing and graph
databases will grow at 100%
annually through 2022 to
continuously accelerate data
preparation and enable more-
complex and adaptive data
science.”
Gartner predicts
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Many studies on extending deep learning
approaches with graphs have emerged.
Graph neural networks (GNNs) are divided
into four categories:
● Recurrent graph neural networks
● Convolutional graph neural networks
● Spatial-temporal graph neural networks
● Graph auto-encoders
GRAPH NEURAL NETWORKS
https://arxiv.org/pdf/1901.00596.pdf
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Graph embedding converts a graph into a
low dimensional space in which the graph
information is preserved.
By representing a graph as a (or a set of) low
dimensional vector(s), graph algorithms can
then be computed efficiently.
Embedding transforms graphs into a feature
vector, or set of vectors, describing topology,
connectivity, or attributes of nodes and
relationships in the graphs.
GRAPH EMBEDDING
https://arxiv.org/pdf/1709.07604.pdf
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Node2vec is an algorithm to generate vector
representations of nodes on a graph.
Given any graph, it can learn continuous
feature representations for the nodes, which
can then be used for various downstream
machine learning tasks involving predictions
over nodes and edges predicting the most
probable labels of nodes in a network
__________
NODE2VEC
https://arxiv.org/pdf/1607.00653.pdf
https://neo4j.com/docs/graph-data-
science/current/algorithms/node-
embeddings/node2vec/
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STRUCTURE
Relationships are some
of the strongest
predictors of behavior
CONTEXT
Graphs provide right
contextual, relevant
information
EXPLAINABILITY
provide transparency
into the way AI makes
decisions
WHY GRAPHS FOR MACHINE LEARNING?
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● Machine learning models are opaque, non-intuitive, and difficult for people to
understand
● Black box models may reflect human biases and prejudices
BLACK BOX EFFECT
Black Box AI
.
Explainable AI
.
Why did you do that?
Why not something else?
When do you succeed?
When do you fail?
When can I trust you?
How do I correct an error?
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When machines take decisions, we
want them to be clear on what stage
they have reached in this process and
when they are unsure, we want them
to tell us.
TRANSPARENCY
Transparency is especially relevant in
applications like:
● medical diagnoses
● crime predictions
● personality scoring
● lending decisions
● high-scoring cases of suspected
fraudulent activity
● GDPR
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EXPLAINABLE AI
Explainable AI (XAI) is the class of systems that provide visibility into how an AI
system makes decisions and predictions and executes its actions.
XAI explains the rationale for the decision-making process, surfaces the strengths
and weaknesses of the process, and provides a sense of how the system will
behave in the future1.
1 The Defense Advanced Research Projects Agency (DARPA) program on XAI identifies these as key
characteristics of XAI: Turek, Matt, Explainable AI, Program Information, Defense Advanced Research
Projects Agency, https://www.darpa.mil/program/explainable-artificial-intelligence, Accessed on October
20, 2019
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● Produce more explainable models, while
maintaining a high level of learning
performance (prediction accuracy)
● Enable human users to understand,
appropriately trust, and effectively
manage the emerging generation of
artificially intelligent systems
● Ensure impartiality in decision-making, to
detect, and consequently, to correct from
bias in the training dataset.
NO AI WITHOUT EXPLANATION
Learning
Sample Data Expected Result
Computer
Model Explanation
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EXPLAINABLE
DATA
What data was used to
train the model and
why?
EXPLAINABLE
ALGORITHMS
What are the individual
layer and thresholds
used for a prediction?
EXPLAINABLE
PREDICTIONS
What features and
weights were used for
a particular prediction?
HOW GRAPHS PROVIDE EXPLAINABILITY
https://neo4j.com/blog/ai-graph-technology-ai-explainability/
Amy E. Hodler, Analytics & AI Program Manager
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DATA TYPES AND DEEP LEARNING APPLICATIONS
FUJITSU ORIGINAL
Handwriting recognition
Time series data Graph dataVideo/Voice Text
COMMODITY / OSS
Expanded Deep Learning ApplicationCommon Deep Learning Application
Small amount of data/
Imbalanced data
………
OUTPUT
INPUT
Object recognition Topological Data Analysis (TDA) Deep Tensor Wide Learning
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Core Tensor
Representation
(captures important features)
BackpropagationExtended
Backpropagation
Graph data
Class A
Class B
Classification
errors
Core Tensor
optimization
Explanation
(factors influencing decision)
LEARNS FROM GRAPH DATA NATIVELY, AUTOMATICALLY EXTRACTING FEATURES
DEEP TENSOR®️ TECHNOLOGY
30. ( 30 )
GALILEO XAI
The graph-based platform
for eXplainable AI
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GALILEO XAI: ARCHITECTURE
MODULAR DESIGN
GALILEO XAI
ADD-ONS
RULE ENGINE
GALILEOXAI
FOR
FRAUDDETECTION
GALILEO XAI
FOUNDATION
GALILEOXAI
FOR
ITASSET
MANAGEMENT
GALILEOXAI
FOR
IDENTITYAND
ACCESS
MANAGEMENT
YOUROWN
GRAPH
APPLICATION
AUTH
OGMA
LKE
GALILEOXAI
FOR
GOVERNMENTS
DEEP TENSOR®️
GALILEOXAI
FOR
KNOWLEDGE
GRAPH
BIG GRAPHS
VIZ
GALILEOXAI
FOR
FINANCE
DATA
IMPORT
DASHBOARDS
EXTENSIONS
ANALYTICS
DASHBOARDS
INTELLIGENT
SEARCH
NEO4J LIBS
(GDS, APOC, KAFKA)
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GALILEO XAI: DEEP TENSOR®️ INTEGRATION
RDBMS
MACHINE
LEARNING
RESULTS
AND
EXPLANATION
TRAINED
MACHINE
LEARNING
MODELS
CONFIG
FOR
DEEP TENSOR
GENERAT
E
DEEP TENSOR
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GALILEO XAI: PIPELINE
RDMBS to GRAPHDB
● Graph model design by GDB specialist
● Domain specific knowledge
● LARUS and NEO4J has a lot of
knowhow
● Customer and GDB specialist
GDBRDBMS GDB GDB
GRAPH-AIGRAPH-ALGO GRAPH-XAI
GRAPHDB to GRAPH-AI
● Discovering and identify the cases
(issue) by graph-tool
● Designing the AI-problems
● Creating training data by graph-tool
● Run GAI and tuning
● Redesign graph model?
● GDB and GAI specialists
GRAPH-AI to GRAPH-XAI
● Discovering and identify the
explanation needs
● Designing the XAI-problems
● Application and visualization design
● Run XAI and tuning
● Customer, GDB and GXAI specialists
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DEMO VIDEO Presented by
SIMONE CECCARELLI
Head of Data Viz @ LARUS
![ARTIFICIAL INTELLIGENCE
TO TRUST OR NOT TO TRUST?
Lorenzo Speranzoni, CEO @ LARUS
Surya Josyula, Director @ FUJITSU LABS
[NODES 2020]](https://image-private.slidesharecdn.com/ewmdtxqzr9ahi0t3svps-signature-0a8d40d6d901b65bc0de673a18e451eec7d329786f0d2c42ae190e569dc36afd-poli-201017213339/85/slide-1-320.jpg?hdnea=acl=/ewmdtxqzr9ahi0t3svps-signature-0a8d40d6d901b65bc0de673a18e451eec7d329786f0d2c42ae190e569dc36afd-poli-201017213339/85/slide-1-320.jpg*~exp=1714074600~hmac=0d5983b13422cc98f7ee229742168e3c7075eabab2ce6f427a60fc9f0cac33ed&cb=1602970476)
