proposal digipolis acpaas - vub course published.pdf · myths and misconception debunked....

VUB Lecture

March 2019

Topics

• Introduction

• Getting into the industry

• Academy

• Audit report

• Proposal NLP

• What I wish I had known

• Start up work

• Ethics

• Research

My path

• 2010: Bachelor in Physics, ULB.

• 2012: Master in Astrophysics and Space Sciences, ULg

• 2017: PhD in Oceanography, GHER, ULg

• Machine Learning Engineer at Omina Technologies

Skills: Numerical Modelling, Data Assimilation, Artificial Intelligence

Master

• Astrophysics and Space Sciences:• Astronomy

• Celestial Mechanics

• General Relativity

• Atmosphere

• Optics

• Physical Oceanography

• Numerical methods and programming

Master Thesis

• Discovered applications of numerical modelling in Oceanography.

• Numerical modeling of the 2011 Tōhoku tsunami and its propagation in the Pacific ocean.

• Programming in matlab, fortranand bash.

• Handling data, working on a supercomputer, …

PhD

• GeoHydrodynamics and Environment Research

• Supervision of Dr. Alexander Barth

• Research, and some side activities:• EGU

• Calvi

• Workshops

PhD• Thesis: Constraining model biases in a global general circulation model with

ensemble data assimilation methods

Looking for a job

• Questions:• What do I want to do?

• What can I do?

• Where to find opportunities?

Deterministic

ModellingData

Assimilation

Artificial

Intelligence

Build your network !

Current career

• Research and development in artificial intelligence

• Consultancy at other companies:• Telecommunications

• Banking

• Marine technologies

• Transportation

• Domotica

• …

• Developing products and services

• Talks at conferences and AI schools

To the industry

We give you the power to turn your big data

into smart data

We give you the knowledge to take important decisions

at lightening speed

We give you the ability to learn from the past and

predict the future

Omina Leadership

Tom Vervust

COO

Rachel Alexander

CEO

Lynn Westall

VP Bus Dev

MANAGEMENT TEAM

Johan LoeckxHead of VUB

AILaboratory

Freddy Van Den Wyngaert

Former CIO AGFA Gevaert

Pol HauspieFormer CEO

Lerhnout and Hauspie

Luke PetershmidtEntrepreneur &

Creator of Bakugan

ADVISORY BOARD

Omina Technologies

Omina Consultancy

Omina Core

®

Full Stack Machine Learning Software Platform

Our powerful and unique ML software has been designed from the ground up to be

easily tailored for companies across a wide set of verticals and their diverse use

cases.

Scientific Research

Research and development partnership with the VUB for semi-generic toolbox

development.

End-to-End AI Solution Development

End-to-end development and support of AI solutions, customized application for

wide range of industries.

Omina Academy

Security & Legal

Data security, legal and privacy

issues when dealing with data. How

to setup AI experiments and

implementations.

HR

Will AI replace all the humans in your

company ? What might be the impact

on your organization. How to you

prepare for it.

What is AI

ABC of AI. Myths and misconception

debunked. Difference between AI and

automation. How could it benefit your

business. What is our approach.

Data is the Key

Is your data valuable or not.

Understand the importance of data

and learn how to optimize your data

now.

Governance

How to implement AI in your

company according to your

governance. Best practices & cases

studies that worked.

Best Practices

Ready to step in the driver seat ?

How did other companies

successfully started innovating.

Management ReportOmina Academy

December 2018

AI Globally

17

Understanding AI

❑ General customer refers to Science Fiction: Terminator, iRobot, Black Mirror, …

▪ Intelligence/Consciousness.

▪ General/Narrow intelligence.

▪ It will take over jobs.

▪ Doomsday is coming.

▪ Magically solve all problems.

❑ Consequences:

▪ Lack of credibility, Overselling.

▪ Marketing tool: AI for selling.

▪ Hypes and AI Winters.

https://wallpaper-gallery.net/single/i-robot-wallpaper-18.html

18

20

Classical Machine

Learning

NN

Deep

Learning

AI

Deep Learning (Algorithm)

21

0

1

2

3

4

5

6

7

8

9

4

Fully Activated

No Activation

Partially Activated

Hidden 1 Hidden 2Input Layer Output Layer

Mathematics: In Relation to SVM

22

❑ Support Vector Machine Algorithm:

▪ Find the largest geometric margin between the labeled data points.

❑ Convex optimization problem:

▪ 𝑚𝑎𝑥ෝγ,𝑤,𝑏 ොγ, with γ 𝑖 = 𝑦 𝑖 𝑤

𝑤

𝑇𝑥 𝑖 +

𝑏

𝑤

Subject to the constraint: 𝑦 𝑖 𝑤𝑇𝑥 𝑖 + 𝑏 ≥ ොγ

❑ Solved using the generalized Lagrangian :

▪ 𝐿 𝑤, α, β = 𝑓 𝑤 + σ𝑖=1𝑘 α𝑖 𝑔𝑖 𝑤 + σ𝑖=1

𝑙 β𝑖 ℎ𝑖 𝑤

23

Credit Risk Classifier

Train the model with major classification algorithms

Decision Tree Classifier is a simple and widely used classification techniques. Key objective of the learning algorithm is to build

predictive model that accurately predict the class labels of previously unknown records.

The decision tree classifiers organized a series of test questions and conditions in a tree structure.

Model 2: Decision Tree

Management ReportData Science Audit

December 2018

AGENDA AUDIT OBJECTIVES PROJECT SCOPE

Management Report Meeting

• Provide a general overview of AI

trends in the market

• Access publicly available data

regarding competitors

• Semi-structured interviews will inform

the capacity report

• Survey of currently used models and

technologies will also inform the

integration of ML and AI technologies

• Strategic road map will be informed

by both capacity and benchmarking

reports

• Skills workshop aims to build

capacities in ML and AI technologies

• Data deep dive will provide strategic

direction to tackle concrete use cases

• Benchmark report of the current state-

of-the-art developments in AI and ML

in business domain

• Capacity report analysing current

capacity with regard to AI and ML

technologies

• Strategic roadmap for integrating AI

and ML technologies into current and

future offerings

• Bundled management report

• Tailored skills workshop presenting

findings of capacity and roadmap

reports

• Deep dive into use case

Presentation of the content of the

Management Report:

1. Capacity report

2. List of possible use cases from

the interviews

3. Benchmark of the competition

and current AI activities

4. Strategic roadmap to achieve AI

transition

5. State of the art developments in

AI in marine technologies

Skill workshop: To be planned in

January 2019

Capacity Report: Strengths

Core development

teams

Interest in AI possibilities

Excellent Practical

Programming Knowledge

Company wide positive

feeling towards AI

Strong Theoretical background

for AI

Capacity Report: Weaknesses• General concepts and domains

• Lack of examples on past projects

• No link between currently used tools and AI equivalent

Lack of generalAI knowledge

• Mainly matlab and excel formula

• No knowledge about AI libraries

Limited Python experience

• Stringent Guidelines and rules to follow

• Licensed softwares

Project Specificities

• No repetitive work

• Often unique projects

• Difficulty to identify « quick wins »

Niche MarketPlayer

• Difficulties to keep up-to-date with latest developments

• Lack of knowledge around AI applications in respective fields

Research and Education

List of Use cases

Team 1

Bathymetry for sea-bedmigration

Fibre opticcables

protection monitoring

Soil thermo-conductivityinvestigation

Darwin platform for world-widewindfarm data

Team 2

Deterministicmodel

replacement

Local data-driven forecasts

Team 3

OvertoppingNeural

Networks

Coastal design parameterresponse

Materialpropertiesparameterresponse

Empiricalprocesses

formula replacement

Team 4

Image recognition on satellite data

Insight in complexsystems

Complex model local

replacement

Team 5

Breakwaterstructure flow investigation

Historical dikefailures stability

modelling

Team 6

Flood riskestimation

Team 7

Missing data filling from time

series

Image processing for data extraction

Competition Benchmark

Estimation of AI capacity

Financial capabilities

History and length of AI

activity

Historicalinterest in AI

AI technologyimplementation

AI skills

Partnership or in-house skills

Active recruitement of

AI experts

Leaders

Deltares

Royal HaskoningDHV

Artellia

Atkins

Followers

Antea

Cowi

Sumaqua

Sweco

VITO

Latecomers

Intertec

Arcadis

DHI

Egis

HKV

Outsiders

Hydroscan

Witteveen + Bos

ECOSHAPE

SBE

Strategic Roadmap: Classic approach

Execute Pilot program

•Build up familiarity

•Get stakeholders interest

•Get support

•Find business values project

•Avoid difficulties

Extend in-house capabilities with AI team

•Continue on momentum

•Define cohesivecompany strategy

•Share acquiredknowledge

Acquire broad AI training

•Broaden common knowledge

•Provide courses and formations to relevant people

•Brainstorm ideas

Develop an AI strategy

•Consolidation of AI approach

•Obtain competitiveedge

•New/betterproduct

•More customers

•More data

•Support companyshift towards AI

Developcommunication

•Effect on stakeholders

•Care for guidelines and rules

•Customers care

Strategic Roadmap: Market approach

AI suggested approach:

•Develop unique AI solution where no classic approach is possible

•Combine AI and domain knowledge into AI leaders in every department

•Learn from academia exploration

AI implementation obstacles:

•Uniqueness of every project

•Lack of data from previous projects

•Domain specific knowledge requires high innovation potential

•Has built a long history of AI activity in research

•Strong AI backgrounds and capabilities in-house

Competitor1

•Partnership with data sciences company

•Acquisition of AI company

•Combination of domain knowledge and AI experts in-house

Competitor2

•Development of specific products

•Partnership with Oracle and IBM

Competitor3

Strategic Roadmap: Solution

Bringing in knowledge

Identifyingtechnologies

Approachingcustomers

Bringing and building

knowledge

Recruitment of 1-2 AI experts

• Support for proposals writing

• Transfer knowledge

• Identification of previous use-cases

• Implementation support

Training of departmentvolunteers

• MOOC

• Self-education and research

Focus efforts on knowledgetransfers

Approachingcustomers

Identification of potentialinnovation partners

Development of new products

Strategic Roadmap: Technologies

Objectives:

•Complex process insight

•Model replacement

•Local forecasts

Neural Networks:

•CNN

•RNN

•Deep Learning

Model knowledge:

•SVM

•RF

•K-means

General knowledge:

•Bias/variance trade-off

•Parameter tuning

•Condition of applicability To avoid

Meta-Learning

GeneticAlgorithms

GANS

Proposal NLP Engine

Digipolis - A001021

November 2018

Requirements

• Can divide texts in sentences, words, part of words.

• To be able to identify semantic relations between entities, words, sentences.

• To be able to tag the content of documents.

• To identify the similarities between words, sentences and documents.

• Items like places, names, dates identification.

• Sentiment analysis on the author of the message. To estimate the objectivity of a sentence.

• To build a corpus around content domains.

• Model must also be able to learn from feedback info.

Requirements

• Use case 1:Portal of the city of Antwerp: Orders are placed through the portal. The solution must be robust and make the link between miss spelled words and semantic linked words: “Pencil, pensil, or pen.

• Use case 2:Following social media, in particular Twitter (with Twitter feeds, I guess it’s an already existing engine). Events of the cities get feedback from citizens on twitter. The solution must be able to retrieve the attitude of the citizen from tweets in relation to an event in particular.

Operational Requirements

• Support: Support of the delivered product, counting also support costs.

• Infrastructure: On premise or in the cloud, required description of the platform used to deliver the solution. Digipolis also requires the minimum capacity of storage, memory, CPU, networking, …, and which operational constraints are crucial.

• Availability:The solution is to be delivered as and generic platform, which can tackle both given use cases. The ultimate use of the solution will be business critical. Omina will need to provide insurances over the availability of the solution.

• Logging and monitoring:Omina will need to describe the planned built-in logging and monitoring, which will need to be operational when the product is delivered and used.

Proposal Rating

Price model: 25%• Budget compared to deliverable

• Maintenance cost after delivery.

• Marketing: How is the product available on the market? Open source, license, support, SaaS?

Concept: 25%: • Availability, quality, concept

• Design principle: API, microservice, …

• Architecture, components?

• Wireframes, POC ?

Development plan: 25%:• Agile principles

• Transparent sprint planning and monthly or bimonthly releases.

Technical aspects: 25%:• Use of future-proof technologies.

• Re-use of ACPaaS components.

• Reusability of the solution.

• Innovation of the solution.

Step 1: Connect to the Digipolis NLP interpreter engine

Upload your Document(s)

NLP ENGINE

Step 2: Choose the desired output

Select your service

SELECT LANGUAGE

+

SERVICE

Step 3: Upload the document(s) to be analyzed

Upload your Document(s)

UPLOAD DOCUMENT(S)

Step 4: Request is sent to the NLP engine

Select your service

REQUESTED

Step 5: Receive result from the NLP Engine

Token

number

Token Lemma Morphological

segmentation

PoS tag Confidence in

the POS tag

Named

entity type

Base Type of

dependency

1 Marie Marie[Marie] SPEC (deeleigen) 1.000000 B-PER B-NP 1 Su

2 Vroeg Vragen[vraag] Ww(pv, verl, ev) 0.532544 0 B-VP 0 ROOT

3 Zich Zich [zich] VNW (refl, pron, obl,

red, 3, getal)

0.999740 0 B-NP 2 Se

4 Af Af [af] VZ [fin] 0.996853 0 0 2 Svp

RESULTS

Possible procurement platform wireframe

Bestelformulier

Naam:

Departement:

Type bestelling:

Product:

Jan Janssen

WZC De goede deugt

Kantoormateriaal

Stylo

Bestellen Annuleren

Bedoelde u: Woonzorgcentrum De Goede Deugd

Bedoelde u: Balpen

Technical Architecture: Via User Interface

START

I log onto

the NLP

User

Interface

I want one or

more

documents

processed

by the NLP

Engine

I choose:

a) in which

language the

documents

are written

b) Which

domain the

documents

belong to

I choose which

actions are to be

performed on the

documents

a) Item ID

b) Semantic

Extraction

c) Content

Extraction

d) etc

An API is

called:

The

document

and chosen

parameters

are passed

in the JSON

JSON is

passed to

pre-

processor

This is then

passed to

the STEM

processor

The output is

a normalized

document

upon which

NLP actions

can be

performed

NLP

Microservices

are activated

based on the

parameters in

the JSON

Sentiment

Analysis

Domain

Association

Semantic

ExtractionContent

Extraction

Item

Identification

Spelling

Check

Main Micro Services

Document

Translation

XXX

(Additional

Services)

NLP Engine

1) Docments are saved in a database

2) Corpus (per domain) is updated

An API is called

passing the

information

requested in JSON

format back to the

end user

JSON is

translated

to a user-

friendly

output for

the user

The NLP Engine is a combination of the following open

source technologies customized for the usecases of Digipolis

- FROG

- LAMachine

- NLTK

- Python Libraries

MongoDB

Project Plan2018 2019

November December January February

W46 W47 W48 W49 W50 W51 W52 W1 W2 W3 W4 W5 W6 W7 W8 W9

K

Data

Deep

Dive

1.13

Infr.

5 7

84 6

9

Sprint 1.1 and 1.2 Sprint 2.1 and 2.2 Sprints 3-9 Sprint 10.1 and 10.2 Sprint 11

Front-

end

Input

10.1

Test, Train, Deploy

Improvements

Corpus

Data

Deep

Dive

1.2

Pre-

Processing

2.1

Pre-

Proces

sing

2.2

Item Module

Requirements Implementation Test Deploy

Front-

end

Output

10.2

• Mobilize the project team

• Align sprint planning &

approach

• Workshops with end-users

to understand system

requirements (inputs &

outputs)

• Data exploration to

evaluate complexity

• Understand existing

environment

• Identify Infrastructure

requirements

• Connecting databases

• Set up / adjust the IT

landscape

• Decomposing and

transforming data into

usuable formats for micro

services

• Process to clean data

• Collect requirements from

SMEs, implement solution,

test and validate with

stakeholders

• Service integration into

ACPaaS engine

Data Analysis Report Infrastructure blueprint &

workable IT environment

Cleansed Data Implemented micro services

Sponsor

Subject Matter Experts

DBA


DBA

DBA Subject Matter Experts

DBA


Data Engineer

Machine Learning Engineer

Project Manager

Data Engineer


Data Engineer


Software Developer

Data Engineer



Software Developer

Project Plan - Phases & Milestones

Kick-Off Data Deep DiveDevelop Pre-

Processing ModuleDevelopmentCore Modules

Infrastructure Set-up

Sprint 1.1 and 1.2 Sprint 2.1 and 2.2 Sprint 3-9

Project Plan - Phases & MilestonesSprint 3-9 Sprint 10.1 and 10.2 Sprint 11

• Collect requirements from

SMEs, implement solution,

test and validate with

stakeholders

• Service integration into

ACPaaS engine

• Extend existing ACPaas

user interface

• Create Output visualization

for EndUsers

• Consolidation of micro

services

• All-round demo

• Extend functionality with

additional features (tbd)

• Preparation of Test Cases

• Integration Testing

• User Acceptance Testing

• Performance Testing

• Review technical

documentation for

handover

• Create user guidelines

• Go-Live & hypercare

• Handover to Digipolis

• Collect customer feedback

• Continuous Improvements

• Maintenance & Support

Implemented Corpus module End user application interface Improved micro services &

test cases

Tested, Documented and

Deployed solution

Supported Solution


DBA

Subject Matter Experts Sponsor


DBA


DBA


Data Engineer


Software Developer


Software Developer

Date Engineer


Software Developer

Project Manager

Data Engineer


Software Developer

Project Manager


Software Developer

Development Corpus

Front End Developments

Test, Train & Deploy

SupportContinuous

Improvements

What I wished I hadknownNovember 2018

General Work Guidelines

• Know yourself and know what you do not want.

• Be pragmatic in work.

• Communication is key:• Clients

• Business

• Colleagues

• Real situations are never optimal.

AI Guidelines

• Keep Learning and stay ahead:• One apple a day keeps the doctor away; One paper a day keeps the doctor away.

• Learn « hard » skills:• Software engineering

• Big Data

• Learn « soft » skills:• Presentation of work

• Project management

• Work methods: Waterfall, agile, scrum, lean, six sigma, kanban, PMI/PMBOK

• Learn about tools:• Jira, Realtimeboard, Slack, …

Start-up environmentNovember 2018

Working at a start-up

• Freedom

• Innovation

• Grow opportunities

• Variety of work

• Actually building something from scratch

• Chaotic environment: Lack of explicit rules

• Lots of responsibilities

• Many different skills to learn

Ethics in AINovember 2018

Ethics

Be responsible.

Omina CoreNovember 2018

Omina Core

• Core idea: Make AI accessible to SME

• Semi-generic toolbox of modules

• Research partnership with VUB

Data AssimilationNovember 2018

Model and observations

Data Assimilation Methods

4DVar and Neural Networks

proposal digipolis acpaas - vub course published.pdf · myths and misconception debunked....

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