The presentations from our lightning talks, during London Tech Week. Delivered by a number of Somo team members and the CTO of Visii, George Whitelaw.

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Solutions for the connected world

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Somo accelerates mobile transformation
through rapid innovation to create
products and experiences your
customers and employees will love.

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Our agile approach to transformation
AmplifyExecute & IterateInnovate
Product
& UX
workshops
Proof of
concept
Scaled
global
launch
Optimise
& iterate
Minimum
lovable
product
Owned, earned,
& paid media
Productise
Maintain,
Scale &
support
Strategic
vision
& insight

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Transforming
Live
Transforming
Engagement
Transforming
Content

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Global client experience
Finance
Retail & FMCG
Automotive Publishing TMT
Utility &
Government
London Bristol NYC

6. 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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7. Global partnerships with industry leaders
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8. Digital experience Physical world
Multiple
screens
Mixed
realities
Interface
Internet
of things
Somocorefocus
Desktop
360˚
Tablet Mobile Wearable
Virtual reality Augmented reality
Touch Voice Gesture
Connected car
Connected city
Connected home
Connected retail
Connected fitness
Messaging
Machine Learning
Biometrics
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Innovation focus: what’s next?

9. Our values
Create success
Be brave
Lead with knowledge
Love innovation

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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

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Check-ins Payments Events
NFC use cases

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Coca-Cola Samsung Burberry Nokia
NFC use cases

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“The allure of NFC is its simplicity”
Why NFC?

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“Traditional”

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NFC implants

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Dangerous Things

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Slightly painful..

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Different possibilities

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Future possibilities

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Innovation = collaboration

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This is actually quite common

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Welcome to the future
Proteus ingestible sensor Google glucose contact lens e-Dura implant

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Wolverine?
Anatomics 3D printed Titanium ribs

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Biology & Technology in 30 years
Ray KurzweilNicholas Negroponte

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Today

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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

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Tomorrow?
• Physical
world
interface
• Virtual
world
interface
• Cognitive
interface

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34. Machine Learning
Andrew Wyld - Technical Architect

35. Machine Learning
Are you Sarah Connor?
My name is Siri and I have bad news.

36. Buckle up
buttercup
this will go fast

37. In at the deep end
Deep 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 ….

38. 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.

39. 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.

40. 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.

41. 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.

42. 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!

43. Classifiers
There 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.

44. 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.

45. 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.

46. 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.

47. 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.

48. 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.

49. One-vs-all
One 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.

50. 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.

51. Deep learning
Hugely 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).

52. 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.

53. 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.

54. 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.

55. Any questions?
Andrew Wyld
andrew.wyld@somoglobal.com
@Andrew_Wyld

56. 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 network
http://playground.tensorflow.org/
have fun!
Google/Udacity deep learning course
https://www.udacity.com/course/deep-learning--ud730 want to do
it!

57. You can get this slide deck here.
Andrew Wyld
andrew.wyld@somoglobal.com
@Andrew_Wyld
https://docs.google.com/presentation/d/1K9owIkpuneAtuaqTguQ5gM7nboK-PWZhkwf3czaFN0M/pub

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59. A quick dive into Deep Learning
George Whitelaw
59

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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 of
information 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) takes
time, 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

63. What is a neural network
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Source: Neural Networks, Manifolds, and Topology by Christopher Olah
Image 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.

64. What is a neural network
64
Source: Neural Networks, Manifolds, and Topology by Christopher Olah
Image source: Wikimedia
Dataset
Learn where a point belongs on a line
Without a NN
Pretty rubbish
With a NN
Better

65. What is a neural network
65
Source: Neural Networks, Manifolds, and Topology by Christopher Olah
Image source: Wikimedia
•The hidden layer represents the dataset in a
way that clearly separates a decision.
•Complex input requires more layers.

66. 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.

67. Deep learning in daily life
67
•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.

68. Stay connected
68
Visii
Rainmaking Loft - International
House
1 St Katharine’s Way
London, E1W 1UN
Website: www.visii.com
George Whitelaw (CTO)
Email: george@visii.com
Mobile: + 44 797 623 9524
AddressContact Info

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TIME TO EXPLORE

70. Messaging App Fragmentation
Dave Evans - CTO

71. Reaching end users : Today
Ads targeted on keywords
or interests
Click to landing
page or app
store
Advertising Medium is
HTML/CSS/Jscript
Landing page is web page
Served by enterprise or
Interim step in App Store
plus an app built by enterprise
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72. 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
Confidential and copyright of Somo Global Ltd.

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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

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