Artificial Intelligence is being called the new electricity, data the new currency. Data processing platforms in the cloud are lowering the barriers to entry. What are the opportunities and challenges for companies to use data as an enabler for disruptive innovation?

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Template presentation Innovation Day 2016CONFIDENTIAL
Max Nie
Coordinator digital lab & project office
Max.Nie@Verhaert.com
Alexander Frimout
Consultant InnoLab
alexander.frimout@verhaert.com
TRACK 3: EVOLVING ARCHITECTURES
DATA SCIENCE:
SELF LEARNING ALGORITHMS

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EVOLVING ARCHITECTURES
Learning machines in a new data world

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A TRADITIONAL HARDWARE PRODUCT IS “MATURE”
Everything you need
(and will ever need)
In one handy box

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WHAT HAPPENS TO THE BOX ONCE IT LEAVES THE COMPANY?
“We have no idea”
Sounds familiar?
Maintenance?
How is it used?
How long does it last?
What goes wrong?
Are people happy with our product?

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THE PRODUCT IS EXPECTED TO ALWAYS PERFORM
TO ITS STANDARD
Sometimes an “error” with a product doesn’t show until later…
…or a users mess up the intended use of a product...
…and the only option is to fix/improve it in a next generation

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SOFTWARE DEVELOPMENT TAKES A DIFFERENT APPROACH

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EVEN SO CALLED “MATURE” SOFTWARE IS
NEVER TRULY FINISHED

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NEW, CONNECTED PRODUCTS ALSO HAVE THIS POSSIBILITY
Self learning machines can add enormous value!
• Personal experience tailored to the user
• Evolving products that promise more
• Better understanding of your own product
• Reduced costs for user & manufacturer

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ML CAN DO VERY COOL STUFF (BUT WE DON’T
FULLY UNDERSTAND WHY)

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• The difference between 95% and 99% accuracy
in speech recognition is game changing
• Training a speech recognition app requires $100 of electricity
• 1 super computer to run a Neural Net with 100 billion connections
• 10^19 floating point operations on thousands of parallel GPUs
• 4 TB training data.
THE CATCH: ML REQUIRES TRULY MASSIVE
AMOUNTS OF TRAINING DATA

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ML IS NOT NEW TECHNOLOGY,
THE BREAKTHROUGH IS IN THE SCALE

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Can you ignore this?
How do you play this game?
ML IS DRIVEN BY VERY BIG TECH WITH VERY BIG DATA

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A new class of software interfaces that interacts
at our own messy level
• Pictures
• Speech
• Text
• Expressions
• Behavior
ML ENABLES PRODUCTS THAT UNDERSTAND
AND INTERACT WITH US

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IMAGE RECOGNITION

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SPEECH AND NATURAL LANGUAGE RECOGNITION

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1. In this ecosystem volumes of training data are the currency
• Your added value is determined by how much you really know
2. Artificial Intelligence is the next computing platform
• New value chains and classes of products will emerge
3. Software and CPU’s are cheap; training data is not
• Algorithms and hardware are not a source of differentiation,
• Building training data is the basis for ROI
4. Performance of smart product continuously grows based on
the flywheel of user generated data feedback
• Through machine learning
• Through superior user insights
IMPLICATIONS

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1. What data assets do you own?
2. What data assets could you create?
YOUR DATA ASSETS

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HOW TO PLAY
Principles of smart product innovation

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Buying not just a product,
but a promise
Some services are only
possible after a sufficiently
large data set/user base
Our world is evolving fast,
we expect our products to
evolve with us
Making the most out of data to improve your product
INCREASING VALUE WITH LEARNING & ADAPTIVE PRODUCTS
Tesla cars have driven over 150
million miles autonomously

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Don’t just start collecting data without first knowing
Build the use cases for your product/service:
• What is the added value of this solution? What advantages
or improvements am I offering my users?
• Is this a good fit with my product? Can I do this technically?
• What market am I targeting? Can I make a profit with this?
BUILDING THE RIGHT USE CASES
WHY

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Involve experts from all fields
Typically during a pressure
cooker or sprint session
Keep an open mind and build
a wide range of diverging
cases
Select the right ones by
objectively criticizing all
aspects
BUILDING USE CASES REQUIRES
A MULTIDISCIPLINARY APPROACH!

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SOME EXAMPLES OF USE CASES
IF …
What is my trigger?
I detect the performance of my
elevator dropping
I can monitor the heat profile
and exact hotspot of a
transformer
THEN …
What action can I perform?
I want to dispatch a technician
early
I can set up cooling much
more rapidly and efficiently
BECAUSE …
What is the underlying driver?
I want to prevent is from
malfunctioning later
Getting stuck in an elevator
causes huge dissatisfaction
with my hotel guests
Better heat management can
increase operating life by
several years
Uniform cooling is inefficient

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3 THINGS ARE NEEDED FOR MACHINE LEARNING
1. Training Data which has been tagged, categorized,
or otherwise sorted by humans.
2. Software libraries which build the machine learning
models by evaluating training data.
3. Hardware CPUs and GPUs which run the software’s
calculations.
More and more
becoming commodities
• Computation in the cloud
• Low powered networking
• Low powered CPU
• Minimal storage

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1. Product performance increases as more training data is fed
2. New user growth from ever increasing performance
3. Unique insights from product data drive product evolution and revolution
ONGOING PRODUCT PERFORMANCE
IMPROVEMENT DRIVEN BY DATA
More
users
More
data
Better
product/
service
Better
algorithms
1. Training data

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Unique data can allow you to provide
a unique service or product
1. Many different factors can make
your data unique!
2. You don’t have to generate all data yourself
3. Putting together all the right pieces of the
puzzle is important
DATA IS NOT A COMMODITY!
Unique location
Established base
Always-on machinery
Product data
User data
Infrastructure access
Pre-installed sensor
You have more access to unique data than you think!
Product usage
Unique technology
Financial dataIntelligentX:
The beer that’s continuously
getting better
Market data
R&D testing

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• Hardware to collect & process data can & should be cheap!
• Cheap sensors
• Computation in the cloud is mandatory to exploit big data assets
• Low powered networking
• Low powered CPU
• Minimal storage
• Hardware design must enable data collection for the right use
cases and contexts
• Think beyond mobile apps to wearables and other devices
• Form factor and price will drive hardware innovation, not performance
IMPACT ON HARDWARE

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• Hiring people to produce training data is too expensive
• So you must acquire an audience and let them create your training data
• The ideal data driven application creates training data and delivers value,
powered by the data captured
Offer value or meaning in return for data
THE IDEAL DATA DRIVEN APPLICATION

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1. It tracks your steps
2. It tracks your distance run by GPS
3. It loads your Spotify playlists
4. It connects to online services
5. It synchs data with fitness apps
6. It’s SDK allows 3rd party development
7. It’s an Alexa powered PA:
• “Alexa, play my workout list”
• “Alexa, what will the weather/traffic be?
• “Alexa, what’s the latest news”
• “Alexa, add milk to my shopping list”
• “Alexa, set the house temperature to 22°
• “Alexander, you have one meeting today
PEBBLE CORE: A $69 SMART PHONE
REPLACEMENT (FOR RUNNING)

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Hardware suppliers can become service providers!
Transformation process for organization
Requires you to consider alternative business models!
THIS WILL IMPACT YOUR ORGANIZATION & BUSINESS MODEL!
From …
Buying a car
To …
Subscribing to a flexible
transportation service
Who will handle user communication?
Do we need an IT department? Who are our new stakeholders?
How will we handle data?
Map your new ecosystem!

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Try to think of use cases for improving a product with data for…
EXERCISE
…a pillow
Hint: you can include an actuators!

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WHAT DOES IT TAKE
Developing self learning algorithms

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DATA DRIVEN INNOVATION PROCESS
Create smart
concept (use case)
Solve the data
science problem
Develop &
introduce productINNOVATE
Have training
data
INSIGHTS, PERFORMANCE

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DATA SCIENCE PROCESS
DATA
QUESTION
DATA
PRODUCT
TIDY
DATA
DATA
PROCESSING
DATA
ANALYSIS
Sneakernet
Manual download
Scraping Custom scripts
Descriptive
Exploratory
Predictive
Inferential
Causal
Mechanistic
Audience analysis
Premises
Conclusion(s)
• Answerable
with data
• Data is
obtainable
• Business &
user validated
• Explore
• Clean
• Transform
• Combine
• Descriptive
analysis
• Exploratory
analysis
• Inferential
analysis
• Predictive
analysis
• Prescriptive
analysis
• Post
• Visualization
• App

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‘SUPERVISED LEARNING’ BASED ON TRAINING DATA SETS
Regression problems Classification problems
Hypothesis Function Cost Function
Source: http://www.andrewng.org/

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THE OPTIMAL HYPOTHESIS MINIMIZES THE COST FUNCTION
Iterative convergence
Source: http://www.andrewng.org/

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GRADIENT DESCENT ALGORITHM FOR COST
FUNCTION MINIMIZATION

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THE JOY OF CONVEX COST FUNCTIONS

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UNFORTUNATELY REAL PROBLEMS ARE NONLINEAR

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THIS REQUIRES A MORE FLEXIBLE APPROACH

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NON LINEAR CLASSIFICATION

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MODELING THE XNOR FUNCTION WITH A NEURAL NETWORK

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BUILDING COMPLEXITY AND SCALE WITH NEURAL
NETWORKS

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TRAINING STEP 1: DEFINE NEURAL NETWORK ARCHITECTURE

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TRAINING STEP 2: EVALUATE COST AND PARTIAL
DERIVATIVE FUNCTIONS

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TRAINING STEP 3: MINIMIZE NON CONVEX COST FUNCTION

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HOW DEEP LEARNING OVERCOMES THE BIAS
VARIANCE TRADE-OFF
1. Example: set benchmark for speech recognition at human error rate of 1%
2. If training error is too high, e.g. 5% then you have a bias issue
 run a bigger neural network
3. If validation set error is too high, e.g. 6% then you have a variance issue
 get more data
4. Otherwise you’re done.

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• Machine learning libraries: Theano, Keras, NumPy,…
• Big data tooling: Hadoop, MapReduce, Spark,…
• MLaaS by Amazon, Google, IBM, Microsoft,…
• And their cloud API’s for Speech, Vision, Natural Language, Translation
TAKE ADVANTAGE OF OPEN SOURCE AND CLOUD

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EXAMPLE CODE TO MODEL AND FIT A NN USING
KERAS AND NUMPY
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform',
activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy' , optimizer='adam',
metrics=['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=150, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

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1. Commercial data partners
• Big tech companies
• Data brokers in all industry domains
• High resolution satellite data
2. Public open data
• Government agencies
• Academic institutions
• International organizations
• NGO’s
• Space agencies
ENRICH YOUR DATA ASSETS WITH OPEN DATA
FOR UNIQUE INSIGHTS

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1. Large scale supervised machine learning enables adaptive,
self learning products.
2. Large volumes of training data is a key competitive advantage,
find or make your own data assets!
3. Finding the right use cases and answering the right data questions
is critical, and requires a multidisciplinary effort.
4. Algorithms and computing are becoming commoditized. Leverage
open source and cloud computing and focus on strategic differentiation
based on unique data.
CONCLUSIONS

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VERHAERT CONNECT / CWI CASE:
ILLEGAL PARKING PREDICTION
• Training data asset: several years of scan car data
• Application concept: a heat map showing illegal parking probabilities
• Data question: predict illegal parking probabilities for each city neighborhood
• Modeling approach: discrete choice regression model
• Cost function: TBD
• Algorithms: maximum likelihood estimators
• R&D plan: 3 months
• Product development plan: 3 months

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VERHAERT CONNECT: A NATURAL PROPOSITION
SENSOR FUSION
TECHNOLOGY
INTEGRATION
ALGORITHMS
CONTEXT SENSITIVE
USER CENTRICBIG DATA
ADDED VALUE
MULTIDISCIPLINARY

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Innovation Day is an initiative of Masters in Innovation,
the umbrella brand of the Verhaert Group which aims
to connect, train and accelerate professional innovators.
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