How to give your business superpowers.

DEMYSTIFYING MACHINE LEARNING

Expertise on demand.

Train your ideal decision-making process,then execute it anytime, anywhere, at any scale.

WHAT WE'RE TALKING ABOUT

Fill in the gaps and squash hype around ML,Build the case for using it now,

And provide easy ways to get started.

TODAY’S GOAL

● Why now?● Foundation● Use cases● ~Technical● Get started● Demos

OUR JOURNEY

Who thinks machine learning is some kind of voodoo?( That’s a good thing. )

● We’re not going to dive into the math● My goal is to show you how easy it is to use● It’s a tool — just another API

You don't need to understand howan engine works to drive a car.

KEEP IT SIMPLE

● Software is eating the world and machine learning is eating the software

● Machine learning (AI) will be the backbone of all next generation business

“mobile first” => “AI first”

WHY IT'S IMPORTANT

Whether you want to:

● Start a new business,● Enhance an existing business, or● Get a new job/promotion

Machine learning will give your applications superpowers ...for now.

(It will be the norm very soon)

WHAT IT CAN DO FOR YOU

● You don’t need a supercomputer● You don’t need to write a ton of code● You don’t need to invest massive amounts of time● You don’t need a data science degree● You don’t need to be a math whiz● You don’t need mountains of data

MYTH BUSTING

WE’VE HEARD IT BEFORE

Is machine learning hype living up to expectations this time around?

Everything is becoming software

● Limitless computing● Limitless storage ● Limitless data (IoT = massive need)● Deep learning● Targeted machine learning SaaS (easy access)

But, more importantly...

WHY NOW?

Because Google says so :)

“Machine learning is not the future. It is now.”

~Google I/O 2016

WHY NOW?

youtube.com/watch?v=3dXQxSI3XDY

Massive strides in the past year

Just in the past few months…

● Google open sources natural language processing platform

● Amazon open sources deep learning platform● Google announces quantum computing works● IBM offers access to quantum computer● Google’s DeepMind beats Go champion

WHAT’S NEW

WILL IT STICK THIS TIME?

The Internet gave us big data (greater need)The cloud gave us massive computing (more horsepower)

And it’s getting much, much bigger…

BIG DATAx

MASSIVE COMPUTINGx

100 million times faster...?

“I would predict that in 10 years there’s nothing but quantum machine learning”

~Hartman NevetHead of Google’s Quantum AI Lab

via: technologyreview.com

via: researchgate.net

ON A PATH TO UBIQUITY

“The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.”

~Mark WeiserScientific American, 1991

IN JUST 4 YEARS

Predicted for 2020...

● 13% of US households own consumer robots 1 (robotics)● 30% of new cars will have a self-driving mode 2 (auto)● 70% of mobile users access devices via biometrics 2 (security)● We interact with 150+ smart devices (IoT) every day 2 (lifestyle)

All are underpinned by machine learning

1 roboticstrends.com/article/13_of_us_households_to_own_consumer_robots_by_20202 weforum.org/agenda/2015/02/5-predictions-for-technology-in-2020

ADDING FUEL TO THE FIRE

Think global.

tractica.com/newsroom/press-releases/artificial-intelligence-for-enterprise-applications-to-reach-11-1-billion-in-market-value-by-2024

THE GOLDEN AGE OF AI

We’ve hit the tipping point.

Watching AI get smarter is like watching a bullet train.

The moment you see it coming, it’s already blown

past you.

HOW I GOT STARTED

Apache Mahout

Decision Forest

Behavior prediction

Suite of mobile apps

Determine the most relevant (highest-converting) sales offer to present to each individual user — and the best (highest-converting) time to present it.

Will the current user buy “Madden NFL” right now?

WHAT IS A DECISION FOREST?

is male?

is age> 16?

is Y app installed?

is X app installed?

end

has used > 30 days?

was X function

used?

was Y function

used?

no

yes

no

yes

no

yes

no

yes

end

(better ways to do this now)

no

yes

end

do it

FOUNDATION

Exploring the basics.

“An algorithm that can learn from data without relying on rules-based programming.”

WHAT IS MACHINE LEARNING?

analyticsvidhya.com/blog/2015/07/difference-machine-learning-statistical-modeling

Training your computer to do stuff, just like you would train a pet.

IN OTHER WORDS...

SIMILAR TO HOW WE LEARN

Data System Output

Model

Question Answer

Life experienceEmotions

Mindset

Training data

Algorithm

Perspective

● Model — The reference data pattern (decision-making stuff)● Algorithm — Process the computer uses to learn the model

(perspective)● Training — Building the model from historical data (life

experience)○ Supervised learning — Labeled training data○ Unsupervised learning — Unlabeled training data○ Reinforcement learning — Reward-based training

● Feature — Points of differentiation in the data

MAJOR COMPONENTS

cse.unsw.edu.au/~billw/mldict.html

ENDLESS ALGORITHMS

Different for each algorithm & platform

For Amazon Machine Learning (logistic regression)…

● Binary (Yes or no, Actionable or non-actionable)● Pick from list (Is this tweet a question, complaint,

or praise?)● Number (How much will this house sell for?)

Sky's the limit on how you can apply these

WHAT IS THE OUTPUT?

IN THE WILD

Recommender(pick from list)

Classifier(binary)

Visual recognition

(deep learning)

“Features”

How would you teach a child to recognize the

differences?

● Distance between eyes● Width of nose● Shape of cheekbones

HOW DOES IT CLASSIFY?

“Probability”

Each potential answer gets a

numeric probability

calculated for it.

Higher probability

means greater confidence.

HOW DOES IT MAKE DECISIONS?

We’re already using it.

LOOK FAMILIAR?

Understand & answer

SEARCH RESULTS

( ibm.com/smarterplanet/us/en/ibmwatson/developercloud/concept-insights.html )

PRODUCT RECOMMENDATIONS

techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html templates.prediction.io/PredictionIO/template-scala-parallel-universal-recommendation

SENTIMENT ANALYSIS

keyhole.co

ibm.com/smarterplanet/us/en/ibmwatson/developercloud/tone-analyzer.html templates.prediction.io/pawel-n/template-scala-cml-sentiment

SPEECH RECOGNITION

cloud.google.com/speechdeveloper.amazon.com/public/solutions/alexagithub.com/tensorflow/models/tree/master/syntaxnet (Parsey McParseface)

Assembled by machine learning

360° PHOTOS

bgr.com cloud.google.com/vision

TRANSLATION

AUTOMATED CAPTIONS

“A group of young people playing a game of frisbee.”

Great example of deep learning —

understanding the context of an image.

io9.gizmodo.com/computers-wrote-the-caption-for-this-photograph-and-ch-1660450610

( I believe every business will need these 2 systems moving forward. )

COMPOUNDING FUNCTIONALITY

Speechto Text

Sentiment Analysis

Actionable Analysis

Customer Support

PREDICTIVE ENGAGEMENT

Customer support call recordings

Convert audiointo text

Analyze formood keywords

Determine ifresponse is required

Reach out to customer/prospect

Blog & community comments

Social media mentions

Press & blog coverage

Customer support chat

Product reviews

Inbound emails

[ IBM Watson Speech to Text ] [ IBM Watson Tone Analyzer ] [ IBM Watson AlchemyLanguage ]

Behavior Prediction

Interest Tracking

PREDICTIVE PERSONALIZATION

Pages & content they’ve visited

Emails they’ve opened/clicked

Resources they’ve used/downloaded

Products they’ve viewed/wishlisted/bought

Searches they’ve made

Blog

Store

Find patterns Determine what they want to see/do/buy next (and when)

Days/time they’re active App

Search

Devices they’ve used (& geo location)Email

Social

• Recommended posts• Recommended products• Delivery day/time

• Dynamic content• Related posts• Sales offers

• Related products• Cross/up sell• Dynamic pricing

• Dynamic content• Sales offers• Functionality

• Query suggestions• Results ranking• Sales offers

• Content curation• Delivery day/time• Retweet/reshare

Tribe• Recommended topics• Topic curation• Member introductions[ Amazon Machine Learning ]

[ Amazon Machine Learning ]

Moving into the technical details.

A BIT DEEPER

A many-layered Artificial Neural Network (~self-learning)

WHAT IS DEEP LEARNING?

“deep”cs231n.github.io/neural-networks-1 “shallow”

(SIMPLE) NEURAL NETWORK

Each layer performs a discrete function

≥ 1 input neurons ≥ 1 output

neurons

≥ 1 hidden layers

Output “fires” if all weighted inputs sum to a set “threshold”

Each connection applies a “weighted” influence on

the receiving neuron

Layers build on each other(iterative)

Each input can be a separate

“feature”

Each neuron takes in multiple inputs

Hidden layers can’t directly “see” or act on outside world

HOW MUCH IS A HOUSE WORTH?Decisions based on combinations.

3 bedrooms

37 years old

1450 ft2

$191,172

Is it “old” or “historic?”

Is it “small” or “open floor plan?”

$32,108 per bedroom

$64,251 per acre

Need a lower weight for “old”

Apply initialabstractions

Set values

● Vanilla Neural Network — nothing fancy● Convolutional Neural Network — inspired by visual

cortex● Deep Belief Network — undirected connections● Recurrent Neural Network — multi-pass

MANY DIFFERENT FLAVORS

● R● Python● Matlab/Octave● Java● C / C++

kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html

POPULAR LANGUAGES

● Amazon Machine Learning ● Google Prediction API*● Google Cloud Machine Learning ● Microsoft Azure Machine Learning ● IBM Watson Machine Learning ● DiffBot ● Alibaba Cloud DT PAI

SaaS OPTIONS

● TensorFlow *● Amazon DSSTNE *● H2O *● PredictionIO ● Apache Mahout ● Scikit Learn ● Caffe *

OPEN SOURCE OPTIONS

● Microsoft CNTK *● Torch *● Theano *● MXnet *● Chainer * ● Keras *● Neon *

* Deep learning

How to get the ball rolling.

GET STARTED

● archive.ics.uci.edu/ml ● deeplearning.net/datasets ● mldata.org ● grouplens.org/datasets ● cs.toronto.edu/~kriz/cifar.html ● cs.cornell.edu/people/pabo/movie-review-data ● yann.lecun.com/exdb/mnist (handwriting)● kdnuggets.com/datasets/index.html (long list)● image-net.org (competition)

OPEN SOURCE DATASETS

● playground.tensorflow.org (neural network demo)● cs.stanford.edu/people/karpathy/convnetjs ● github.com/awslabs/machine-learning-samples ● ibm.com/smarterplanet/us/en/ibmwatson/devel

opercloud/starter-kits.html ● templates.prediction.io

EASY STARTING POINTS

Start now

● It’s here, today● It’s evolving exponentially● Build “AI-First”

RECAP

Let’s see some action.

DEMOS

● AlchemyLanguage ● Dialog ● Natural Language Classifier ● Personality Insights ● Relationship Extraction ● Tradeoff Analytics

ibm.com/smarterplanet/us/en/ibmwatson/developercloud/services-catalog.html

IBM WATSON

UNLEASH YOUR BUSINESSEMBRACE EXPONENTIAL

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