The document discusses how companies can gain a competitive advantage through artificial intelligence. It provides a brief history of AI and an overview of key concepts like machine learning, deep learning and neural networks. Examples are given of medical imaging using deep learning to detect tumors and predicting traffic congestion. The document advocates educating oneself on AI, finding simple applications to test it, and using available cloud services and APIs to get started. It emphasizes that data is important for training models and there are many opportunities but also challenges to developing self-learning systems.

1. Maak van artificiële intelligentie
een competitief voordeel
Nicolas Deruytter

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@ml6team

4. 7
A BRIEF HISTORY

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1950: ALAN TURING’S “TURING TEST”

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2011: MAINSTREAM AWARENESS

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KEYS TO SUCCESSFUL ML
Large Datasets Good Models Lots of Computation

9. 7
LEARNING BY HISTORICAL
EXAMPLES

10. ConvNets

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MEDICAL IMAGING DIAGNOSTICS WITH
DEEP LEARNING
Input Output

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A DEEP LEARNING MODEL IS TRAINED,
RESULTING IN TUMOR PROBABILITY MAP

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OUR DISTRIBUTED FRAMEWORKS ALLOW
US TO RAPIDLY IMPLEMENT OTHERS

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RED ALERT

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RED ALERT

18. DIGITIZING THE “BOEKENTOREN” WITH OCR

19. DIGITIZING THE “BOEKENTOREN” WITH OCR

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HOW TO START

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1. Educate yourself
2. Find low hanging fruit
3. TEST!
(Get your hands dirty)

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Use your own data to train models Ready to use Machine Learning models
Cloud
Vision API
Cloud
Translation API
Cloud
Natural
Language API
Cloud
Speech API
Cloud Machine
Learning Engine
TensorFlow
Cloud
Jobs API
Cloud Video
Intelligence
TWO WAYS

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TRAFFIC CONGESTION PREDICTION

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THE MODEL ACCURATELY PREDICTED
CONGESTIONS IN LONDON TRAFFIC

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“We will move from mobile first to
an AI first world.”
April 2016 - Sundar Pichai
CEO, Google

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29. 7
HOW TO START

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1. Educate yourself
2. Find low hanging fruit
3. TEST!
(Get your hands dirty)

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CONVERSATION GRAPH

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DIALOGFLOW

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SELF-LEARNING

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USED IN GAMES
10 minutes of training 120 minutes of training 250 minutes of training

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SHOWING HOW IT’S DONE

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PHYSICS SIMULATOR

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PHYSICS SIMULATOR

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STATE OF THE ART

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BACK TO REALITY
Sensor Data
IOT Database
Lab results
LaboTest a few samples
per batch
Adjust process
parameters
Can we use ML to predict lab-results using IOT data
in real-time for every single product?
Generalise results to
all samples in batch

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ABOUT THE SMART FACTORY
Suppliers &
Subcontractors
Suppliers &
Subcontractors
Customers
Customers
Smart Grid
Factory 1
Factory 2
Factory 3

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SOME FINAL THOUGHTS

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DATA IS THE NEW GOLD
“Deep Learning requires millions of
hand-labeled examples”

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DATA IS THE NEW GOLD
“Deep Learning requires loads of data and
some clever engineering”
“Deep Learning requires millions of
hand-labeled examples”

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IT’S NOT HARD

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IT’S NOT HARD

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ENDLESS OPPORTUNITIES

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MEDIA CAN BE DECEPTIVE
VS

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CONTACT:
NICOLAS DERUYTTER
NICOLAS@ML6.EU
ML6.EU / WVDO