Florian Goossens is the co-founder and project lead at radix.ai, which brings the power of AI to business, from strategy to execution.

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The Future of IT is AI

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Today
1. AI – ML – DL
2. How to recognise ML opportunities
3. Matching job seekers with jobs at VDAB

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Artificial Intelligence
DL
AI
ML
Computers showing
human-like intelligence
3

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AI
Algorithms
Data
Computing Power
Why now?

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Narrow AI
General AI
Super AI
Where are we today?
Voorbeeldjes

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Machine Learning
DL
AI
ML
Algorithms that learn from data
to make predictions
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Old way:
Write software with explicit rules to
follow:
if email contains V!agrå
then mark is-spam;
if email contains …
if email contains …
New way:
Write software to learn from examples:
try to classify some emails;
change self to reduce errors
repeat;
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Machine Learning is a new way of programming

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input → model → output
Andrew Ng

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Step 1: collect inputs and outputs
input → output
70 m2
→ € 260.000
91 m2
→ € 330.000
102 m2
→ € 400.000
150 m2
→ € 448.000
120 m2
→ € ?
Real Estate Prices, Ixelles, Belgium, 2018

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Step 2: train the modelReal Estate Prices, Ixelles, Belgium, 2018
€ 400.000
€ 300.000
€ 200.000
output
70m2
100m2
130m2
input
model

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Step 3: predictReal Estate Prices, Ixelles, Belgium, 2018
€ 400.000
output
120 m2
input
model

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input → model → output
Appartement 2 Bedrooms
1050 Ixelles
140 m2
1980
Gas
...has a large terrace with a
nice view on the parc ...

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input → model → output
income
Decision Trees Neural Networks
savings
income
savings
income
savings
Logistic Regression

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House price
Days until sale
Number of
candidates
input → model → output
Chance of being sold
within a year
Chance of defaulting
on the loan
Chance of flooding
Loan yes/no
Picture is interior or
exterior
Best channel to
advertise a house
The output of a model can be a number, a probability, or a category.
123,45 83% A / B / C

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Look at your goal. Look at your data. Map.
How to recognize machine learning opportunities.
Goal:
- Telecom company
- Wants to Customer Lifetime Value
Data:
- User behaviour
- User demographics
- User billing
- User network
- User social media
Use Cases:
- Churn prediction
- Personalised offers
Output:
- 10% most likely churners
- Reasons for churn

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Why ML?
Better performance:
forecasting, job matching
object recognition, translation
Easy to adapt:
spam filtering, evolving job market

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Why not ML?
Simple: when the results are good enough without ML
accounting

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Deep Learning
DL
AI
ML
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Neural Networks

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Why DL? Feed any data type!
We are looking for an
educationally focused
Governess for a family
who live between Dubai
and London. The summer
months are spent in
London and Europe and
there is travel
throughout the year.
This is a sole charge
position as both
parents work. You will
be responsible for two
children aged 18 months
& 3 years old.
Never gonna give you up
Male dancing
Traveling nanny

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Why DL? Scale improves performance!
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Large Neural Network
Amount of data
ModelPerformance
Traditional ML models
E.g., SVM, GP, RF, LR
ML
DL

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Why DL? Useful embeddings!
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Useful word
embedding
h1 ⋯ ⋯h2
hNBabysitter
word

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Why DL? Useful embeddings!
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Babysitter
Nanny
Kinderverzorger

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Why DL? Useful embeddings!
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h1
h2
h3
king – man + woman =

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How to create word embeddings

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DL
AI
ML
Summary

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Matching jobs seekers & jobs with deep learning

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It’s a great time for job seekers
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Jobs
Job Seekers

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New technologies = new possibilities
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1050 Ixelles
Babysitter
EN
License B
1000 Brussels
Nanny
… speaks English …
… drive the kids
to school …

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Deep Learning
DL
AI
ML
Neural Network
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Summarize jobs and jobs seekers into embeddings
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Useful word
embeddings
Useful document
embeddings
Useful location
embeddings
Useful job (seeker)
embeddings
h1 ⋯ ⋯h2
hN
h1 ⋯ ⋯h2
hN
h1 ⋯ ⋯h2
hN
h1 ⋯ ⋯h2
hN
h1 ⋯ ⋯h2
hN
h1 ⋯ ⋯h2
hN

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DL reason 1: useful embeddings
Why embeddings are so cool

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Job Recommender Architecture
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Job
embedding
Job Seeker
embedding
.9
Similarity
Complex data Embedding
300 numbers
Score
1 number
h1 ⋯ ⋯h2
hN
h1 ⋯ ⋯h2
hN
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Embeddings are steered by click data
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Job
embedding
Job Seeker
embedding
.9
Similarity
Complex data Embedding
300 numbers
Score
1 number
h1 ⋯ ⋯h2
hN
h1 ⋯ ⋯h2
hN
RADIX.AI I 35

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In production soon at vdab.be!
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1050 Ixelles
Babysitter
EN, FR
License B
1000 Brussels
Nanny
… speaks English
and French …
… drive the kids
to school …

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hello@radix.ai
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Deep Learning
At our core, we are a
team of Machine
Learning engineers.
We make machines
learn.
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Natural
Language
Processing
Understanding natural
language is not easy.
It’s our favorite
challenge.
Job Matching
Matching jobs and job
seekers.
1
2

38. Contact us:
https://www.linkedin.com/in/itworks
https://twitter.com/itworks
www.itworks.be
Presented at:
The Future of IT
20 September 2018 in Brussels