data science, self learning algorithms (by alexander frimout & max nie)

3.2 Simple sensors: case ‘smart metering’

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Template presentation Innovation Day 2016 CONFIDENTIAL

Max NieCoordinator digital lab & project [email protected]

Alexander FrimoutConsultant [email protected]

TRACK 3: EVOLVING ARCHITECTURES

DATA SCIENCE: SELF LEARNING ALGORITHMS


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EVOLVING ARCHITECTURESLearning 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)

http://how-old.net/

http://how-old.net/


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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 PLAYPrinciples 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 laterGetting stuck in an elevator causes huge dissatisfaction with my hotel guests

Better heat management canincrease operating life by several yearsUniform 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 TAKEDeveloping 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

SneakernetManual download

Scraping Custom scripts

DescriptiveExploratoryPredictiveInferential

CausalMechanistic

Audience analysisPremises

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

http://playground.tensorflow.org/

http://playground.tensorflow.org/


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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

https://vimeo.com/21711338

https://vimeo.com/21711338


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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 questionsis 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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