Sorting waste in the right way is an important part of our circular economy. All too often people doubt in which bin they have to put their waste. What if an app helps you with sorting? By combining AI and IoT technology in a gamification app that adds fun for users, we were able to realize an advertising platform for companies.

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CONFIDENTIAL Template Innovation Day 2018CONFIDENTIAL
INTEGRATING AI TECHNIQUES TO BUILD
MEANINGFUL PRODUCT CONCEPTS
Jochem Grietens
System engineer – A.I. passionate
jochem.grietens@verhaert.com
TRACK 1 - CIRCULAR ECONOMY FOR A BETTER WORLD

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Introduction

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1. Introduction
2. A recycling use case: Hackathon
3. Machine learning: What does it mean to “see” ?
4. How does a machine “see” ?
Content

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“Fail fast, fail cheap, learn faster”
Understanding comes from running through the cycle.
Rapid prototyping credo

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A recycling use case
Load Hackathon Case

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aaa
Load hackathon 2018
• Theme A: Help The Community
• Theme B: Change behavior
• aim for a sustainable world
• live a healthier life
• become a better citizen

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1. Business understanding
Hackathon process

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Closing the loop in a circular economy
Challenges for companies:
• Little incentive to close the loop
• Companies have no control over the downstream process.
Business understanding

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Sorting is hard
Sorting is boring
Sorting is a community effort
yellow for plastic and metal blue for paper green for glass
Business understanding

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Concept generation

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3. Data understanding
4. Data preparation
5. Modeling/app building
Hackathon process

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Goals
Help people sort
Fun and engaging
Create a community
Help close the circular economy loop.

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Hackathon process
6 Evaluation
7 Deployment

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Architecture

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Machine learning model
What does it mean to “see”

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What does it mean to “see”
Primate visual system

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What does it mean to “see”

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“Seeing”:
• Recognizing (=classification)
• Localization
• Segmentation
• Understanding
What does it mean to “see”

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Neural network to fit our goal

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How does a machine “see” ?

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Neural network

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Neural network
yellow for plastic and metal
blue for paper
green for glass

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1. Input Encoding
Neural network

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Input Encoding
Picture = 3 arrays of numbers
Neural network
R
G
B

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Output encoding
1. metal  ( 1 0 0 )
2. paper  ( 0 1 0 )
3. Glass  ( 0 0 1 )
Neural network

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Neural network
( 1 0 0 )
( 0 1 0 )
( 0 0 1 )

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The neuron
Neural network

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Neural network

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Neural network

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Neural network

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Convolutional neural network
Conv. neural networkInput
Output
0,9
0,1
0
yellow for plastic and metal blue for paper green for glass

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Neural network training
Forward pass

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Neural network training
0,9
0,1
0
Prediction

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Neural network training
0,9
0,1
0
Prediction
1
0
0
Truth
COMPARE

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Prediction Truth
Calculate the error
Neural network training
0,9
0,1
0
1
0
0

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Neural network training
Backwards pass
Prediction Truth
0,9
0,1
0
1
0
0

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Many layers stacked
Deep neural network

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Specific WIRING of the first regions for image recognition
Deep convolutional neural network

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• Convolution
• Fully connected
Convolutional neural network

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Convolution = comparing to patterns
Convolution

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=6600
Convolution
Feature learning
Great match

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= 0
Convolution
Convolution
Bad match

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Feature maps

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Neural network LEARNS the features from examples
Convolutional neural network

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Neural network LEARNS the features from examples
Convolutional neural network

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Convolutional network

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Flattening

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Flattening

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Classification

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Classification
0
1
Tesla
Volkswagen
One Hot encoding:
1
0
0
1
Tesla
0,2
0,8

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Convolutional neural network

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TrashNet dataset : to train on 2527 labeled images
Way too little to learn a deep neural net “to see” AND “to recognize”
Training data

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Convolutional neural network
Transfer learning = Learning to see on a different dataset.

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Can we learn how to see on Cifar dataset:
60,000 32x32 color images in 10 different classes.
Learn how to recognize on the trashnet database
Transfer learning

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Transfer learning
Freeze Finetune

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Learning by heart vs. learning general features
Algorithm should have learned general features :
• Visual effects of metallic light reflection
• Can shape features
• Size vs environment
• …
Instead it learned:
Very specific features, like the shadow on the hand …
Overtraining
 MODEL DOES NOT GENERALIZE WELL

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Central ML problem
Performance on new data ?
Test it!
How to detect overfitting
Training and tuning the
models.
Check performance
during development.
Don’t touch until the very
end.

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Detecting overfitting
“Performance training set >> test set  likely overfitting.”
Dealing with overfitting

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More data - Data augmentation
Dealing with overtraining

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Dealing with overtraining
X1 X2

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Achieved
• 70% accuracy on test set.
• 65% in real world validation.
Results

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One group, five brands
Our services are marketed through 5 brands each
addressing specific missions in product development.
INTEGRATED PRODUCT DEVELOPMENT
ON-SITE
PRODUCT
DEVELOPMENT
DIGITAL
PRODUCTS
DEVELOPMENT
OPTICAL
PRODUCTS
DEVELOPMENT
VENTURING