lightning talks: an innovation showcase

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Solutions for the connected world


Somo accelerates mobile transformationthrough rapid innovation to create

products and experiences yourcustomers and employees will love.


Our agile approach to transformation

AmplifyExecute & IterateInnovate

Product & UX

workshopsProof of concept

Scaled global launch

Optimise & iterate

Minimum lovable product

Owned, earned,& paid media

Productise

Maintain, Scale & support

Strategicvision

& insight


Transforming Live

Transforming Engagement

Transforming Content

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Global client experience

Finance Retail & FMCG Automotive Publishing TMTUtility &

Government

London Bristol NYC

A selection of our success

✓ Audi e-tron pop up experience in London exceeded lead generation target by 223% ✓ The Wall Street Journal What’s News app ranked #2 in the App Store news category✓ Achieved an ROI of 18:1 with Very.co.uk’s multi-channel 2015 Christmas campaign

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Global partnerships with industry leaders


Digital experience Physical world

Multiple screens

Mixed realities

Interface

Internet of things

Som

o c

ore

foc

us

Desktop

360˚

Tablet Mobile Wearable

Virtual reality Augmented reality

Touch Voice Gesture

Connected car

Connected city

Connected home

Connected retailConnected fitness

Messaging

Machine Learning

Biometrics


Innovation focus: what’s next?

Our values

Create success

Be brave

Lead with knowledge

Love innovation


The Singularity is Near

Ruben Horbach, Senior Innovation Manager Somo

Dave Evans, CTO Somo

George Whitelaw, CTO Visii

Presentations

Messaging App Fragmentation

Deep Learning

Andrew Wyld, Technical Architect Somo

Machine Learning

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The Singularity is Near

Ruben Horbach - Senior Innovation Manager


Check-ins Payments Events

NFC use cases


Coca-Cola Samsung Burberry Nokia

NFC use cases


“The allure of NFC is its simplicity”

Why NFC?


“Traditional”


NFC implants


Dangerous Things


Slightly painful..


Different possibilities


Future possibilities


Innovation = collaboration


This is actually quite common


Welcome to the future

Proteus ingestible sensor Google glucose contact lens e-Dura implant


Wolverine?

Anatomics 3D printed Titanium ribs


Biology & Technology in 30 years

Ray KurzweilNicholas Negroponte


Today


Source: Peter H. Diamandis M.D. - Singularity University 2016

Exponential predictions

10E-10

10E-5

10E0

10E5

10E10

10E20

1900 2000 2100

10E25

10E30

10E35

10E40

10E45

10E50

10E55

10E60

2010

10e11

2023

10e16

2050

10e26

Calculations per second per $1000 vs. Time


Tomorrow?

• Physical world interface

• Virtual world interface

• Cognitive interface

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Machine Learning

Andrew Wyld - Technical Architect

Machine LearningAre you Sarah Connor?

My name is Siri and I have bad news.

Buckle upbuttercup

this will go fast

In at the deep endDeep learning gets a lot of headlines for super cool applications:

Image recognition

Speech recognition

Language processing

“Shallow” learning is still really useful and easier to apply:

Basically statistical techniques

Requires a little cleverness to handle nonlinear data

Nevertheless still very powerful and way easier to train.

Deep learning is based on these simpler techniques, so best to start there.

Deep learning is just shallow learning several times in a row anyway.

There’s a super cool hybrid that gets some of the advantages of both ….

Supervised vs unsupervised

Don’t tell me what to do!

Supervised learning requires the machine to be taught. This is good for situations where there’s a known right answer.

Unsupervised learning throws the machine in among the data and lets it look for patterns by itself.

Supervised vs unsupervised

Supervised learning looks for relationships in labelled data. Data is separated into “inputs” and “outputs”, where you want to predict the outputs from the inputs.

Unsupervised learning looks for patterns in unlabelled data. All of the data is “input” data; the outputs are any patterns found by the algorithm.

Linear regression

The little statistical analysis technique that could

Linear regression is a great place to start. If you have a bunch of data points, you try to fit a straight line to them.

Data that don’t fit a straight line can be handled by using functions of the data that do fit a straight line.

Linear Regression: improving the fit (1)

This line is visibly not a great fit for the data. The error lines are pretty long and asymmetrical.

We aim to minimize squared error, as this prevents positive and negative errors cancelling out.

Linear Regression: improving the fit (2)

This line is clearly a lot better. The data fits the line pretty well.

There are several algorithms to find the best fit for a linear regression. This is basically the simplest machine learning system there is, but it’s still very useful for continuous data!

ClassifiersThere are two types of people in the world: those who like binaries and a

continuum of others.

Classifiers are a huge category of machine learning system. Actually most systems are some kind of classifier, including deep learning systems.

Classifiers split things into categories.

Here we have a set of labelled data. A classifier is an attempt to separate positive ▪ from negative ▪ data, and predict whether new data will be positive or negative.

There are several methods of classification, but they all essentially aim to draw this line.

Logistic regression: best fit for definite people

Logistic regression is similar to linear regression, but where linear regression tries to find a line that fits continuous data well, this method tries to fit a logistic function (which has a suitable sigmoid curve) to a set of “true/false” data.

Support vector machines: best fit for very definite people

A support vector machine is very similar to logistic regression, but has a simpler function that heavily penalises errors in a wide margin, so the algorithm will try hard to avoid putting points there. It’s sometimes known as the wide margin classifier, for this reason.

Underfitting: the model is stupid

A model is said to underfit when it’s too simple to capture something important about the data. Very commonly data won’t exactly fit a linear model. A more complex model is needed to fit the data well.

Underfitting can’t be fixed by better data: no amount of training can bend that straight line round a curve.

Overfitting: the model is neurotic

On the flip side, a model can fit the data so well—hugging every tiny crevice—that it generalises poorly.

A high-dimensional model will tend to overfit. The advantage of an overfitting model over an underfitting one is that more data can usually cure the problem, as random wobbles in the data eventually cancel each other out.

One-vs-allOne against all and all against one! And every other one against every

other all.

Lots of classifications need more than two categories. The usual way to handle this is “one-vs-all” classification: train one classifier for every category, then predict new results using the classifier that is “most sure” of the ones you’ve trained.

One-vs-all classification

In a one-vs-all classifier, as many classifiers are trained as there are categories. Predictions are then based on how confident each classifier is, with the most-confident classifier winning.

Deep learningHugely powerful. Nobody knows

what’s inside it.

The “deep” in deep learning refers to the fact that several classifiers are stacked, one in front of the other. Each one can learn more sophisticated things by building on the previous layer.

Nobody really knows what goes on in the middle layers (although we’re beginning to research it).

Stack high the classifiers!

A neural network is just a sequence of classifiers in a stack. Each layer can use the output of the previous layer as input; thus, by the end, features can be very sophisticated, based on complex combinations of other, simpler features.

The hidden layers make the technique powerful but inscrutable.

Back propagation

Each output is compared to training data and scored. Paths that led from the previous layer to that output are strengthened or weakened depending on the score.

The scores are then passed backwards along the pathways and the process repeated.

Transfer learning

The early layers of (for example) a cat recognition system will probably pick up general image features—corners, colour transitions, diagonals and so on—that would be useful for any image recognition task.

If you want to make a dog recogniser but don’t have a lot of data, you could simply cut off the last layer, steal these early features, keep them the same, and glue a simple classifier on the end in place of the old last layer.

This works surprisingly well.

Any questions?Andrew Wyld

[email protected]@Andrew_Wyld

mailto:[email protected]

https://twitter.com/Andrew_Wyld

Cool stuff: a very non-exhaustive list

Stanford machine learning course https://www.coursera.org/learn/machine-learning/did it, loved it.

University of Washington machine learning specialisation https://www.coursera.org/specializations/machine-learning/ doing it now.

Tensorflow online neural networkhttp://playground.tensorflow.org/

have fun!

Google/Udacity deep learning course https://www.udacity.com/course/deep-learning--ud730 want to do it!

https://www.coursera.org/learn/machine-learning/

https://www.coursera.org/specializations/machine-learning/

http://playground.tensorflow.org/

https://www.udacity.com/course/deep-learning--ud730

You can get this slide deck here.

Andrew [email protected]

@Andrew_Wyld

https://docs.google.com/presentation/d/1K9owIkpuneAtuaqTguQ5gM7nboK-PWZhkwf3czaFN0M/pub


https://twitter.com/Andrew_Wyld

A quick dive into Deep LearningGeorge Whitelaw

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60

Overview

•Why complex problems require machine learning•What is a Neural Network•Solving complex problems•Deep learning in daily life

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Why complex problems require machine learning•We have an ever growing amount ofinformation that needs to be understood,often on demand.

•The problems are getting more complex.•Machine learning has been around since the 60s, many methods won’t cut it.

Source: Deep Learning in a Nutshell – what it is, how it works, why care? by Nikhil Buduma

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Why we need Neural Networks

Teaching computers rules (heuristics) takestime, is error prone and generally sucks.What is this?

Source: Deep Learning in a Nutshell – what it is, how it works, why care? by Nikhil Buduma

What is a neural network

63Source: Neural Networks, Manifolds, and Topology by Christopher OlahImage source: Wikimedia

•We are good at classifying things.•Neural networks simulate (crudely) the human brain.

•They require training on test data to give useful Output - was it 6 or 0?

•Complex problems require deeper networks.



DatasetLearn where a point belongs on a line

Without a NNPretty rubbish

With a NNBetter



•The hidden layer represents the dataset in a way that clearly separates a decision.

•Complex input requires more layers.

Solving complex problems

66Source: FaceNet: A unified Embedding for Face Recognition and Clustering by Google Inc

•Deeper networks can detect more interesting features.e.g. Faces grouped by individual features.

Deep learning in daily life

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•Deep learning helps to classify and organise overwhelming amounts of data.

•New technologies use Deep learning to help save time, reduce decision fatigue and generally make life more simple.

http://faces.visii.com























































































Stay connected

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Visii

Rainmaking Loft - International House1 St Katharine’s WayLondon, E1W 1UN

Website: www.visii.com

George Whitelaw (CTO)

Email: [email protected]: + 44 797 623 9524

AddressContact Info

http://www.visii.com


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TIME TO EXPLORE

Messaging App Fragmentation

Dave Evans - CTO

Reaching end users : Today

Ads targeted on keywordsor interests

Click to landingpage or app store

Advertising Medium isHTML/CSS/Jscript

Landing page is web pageServed by enterprise or

Interim step in App Store plus an app built by enterprise

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Messaging Platforms and Chat Bots

• Messaging apps are becoming platforms – with vast numbers of users • Offering developer API’s providing

ability to interact with end users – typically through send/receive/subscribe API’s

• Complex conversational flows enables enterprise to lead the end user

• Alert based and long running conversations enabled

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Reaching users tomorrow

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Chat Architecture #1

Response Formatting

Application Logic + Language Processing

Data Store

Message Receipt

Session/Conversation Management

Message App eg FB Messenger, Whatsapp

HTTP/s Comms

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Chat Architecture #2

Application Logic + Language Processing

Data Store

Apple iMessage

HTTP/s Comms

Response Formatting

Session/Conversation Management

Message Receipt

Message App

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• Messaging apps will need to be built to support specific messaging platforms • Architect the business logic and machine intelligence on the back end to support multiple platforms • Separate out the request/response presentation capabilities into separate layers/plug-ins

• Apples iMessage requires specific Messaging apps to be built and deployed • Message recipient requires that app on their phone – or flow will be interrupted as they download

(or not) the app • No support for Android – so audience is constrained to iMessage users

• No standard for cross platform messaging/formatting • One of reasons for success of SMS was the adoption of standards across handsets and carriers

However potential for rich dialog with end users, and massive distribution / reach when apps are done well.

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Messaging Apps Fragmentation