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Lightning Talks: An Innovation Showcase

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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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Lightning Talks: An Innovation Showcase

  1. 1. Confidential and copyright of Somo Custom Ltd. June 23 1 Solutions for the connected world
  2. 2. Confidential and copyright of Somo Custom Ltd. June 23 2 Somo accelerates mobile transformation through rapid innovation to create products and experiences your customers and employees will love.
  3. 3. Confidential and copyright of Somo Custom Ltd. June 23 3 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
  4. 4. Confidential and copyright of Somo Custom Ltd. June 23 4 Transforming Live Transforming Engagement Transforming Content
  5. 5. Confidential and copyright of Somo Custom Ltd. April 16 5 Global client experience Finance Retail & FMCG Automotive Publishing TMT Utility & Government London Bristol NYC
  6. 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 6Confidential and copyright of Somo Custom Ltd. June 23
  7. 7. Global partnerships with industry leaders 7Confidential and copyright of Somo Custom Ltd. June 23
  8. 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 8Confidential and copyright of Somo Custom Ltd. June 23 Innovation focus: what’s next?
  9. 9. Our values Create success Be brave Lead with knowledge Love innovation
  10. 10. Confidential and copyright of Somo Custom Ltd. June 23 10 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
  11. 11. Confidential and copyright of Somo Custom Ltd. April 16 11 The Singularity is Near Ruben Horbach - Senior Innovation Manager
  12. 12. Confidential and copyright of Somo Custom Ltd. June 23 12
  13. 13. Confidential and copyright of Somo Custom Ltd. June 23 13
  14. 14. Confidential and copyright of Somo Custom Ltd. June 23 14 Check-ins Payments Events NFC use cases
  15. 15. Confidential and copyright of Somo Custom Ltd. June 23 15 Coca-Cola Samsung Burberry Nokia NFC use cases
  16. 16. Confidential and copyright of Somo Custom Ltd. June 23 16 “The allure of NFC is its simplicity” Why NFC?
  17. 17. Confidential and copyright of Somo Custom Ltd. June 23 17 “Traditional”
  18. 18. Confidential and copyright of Somo Custom Ltd. June 23 18 NFC implants
  19. 19. Confidential and copyright of Somo Custom Ltd. June 23 19 Dangerous Things
  20. 20. Confidential and copyright of Somo Custom Ltd. June 23 20 Slightly painful..
  21. 21. Confidential and copyright of Somo Custom Ltd. June 23 21
  22. 22. Confidential and copyright of Somo Custom Ltd. June 23 22 Different possibilities
  23. 23. Confidential and copyright of Somo Custom Ltd. June 23 23 Future possibilities
  24. 24. Confidential and copyright of Somo Custom Ltd. June 23 24 Innovation = collaboration
  25. 25. Confidential and copyright of Somo Custom Ltd. June 23 25
  26. 26. Confidential and copyright of Somo Custom Ltd. June 23 26 This is actually quite common
  27. 27. Confidential and copyright of Somo Custom Ltd. June 23 27 Welcome to the future Proteus ingestible sensor Google glucose contact lens e-Dura implant
  28. 28. Confidential and copyright of Somo Custom Ltd. June 23 28 Wolverine? Anatomics 3D printed Titanium ribs
  29. 29. Confidential and copyright of Somo Custom Ltd. June 23 29 Biology & Technology in 30 years Ray KurzweilNicholas Negroponte
  30. 30. Confidential and copyright of Somo Custom Ltd. June 23 30 Today
  31. 31. Confidential and copyright of Somo Custom Ltd. June 23 31 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
  32. 32. Confidential and copyright of Somo Custom Ltd. June 23 32 Tomorrow? • Physical  world  interface   • Virtual  world  interface   • Cognitive  interface
  33. 33. Confidential and copyright of Somo Custom Ltd. April 16 33Confidential and copyright of Somo Custom Ltd. June 23 33
  34. 34. Machine Learning Andrew Wyld - Technical Architect
  35. 35. Machine Learning Are you Sarah Connor? My name is Siri and I have bad news.
  36. 36. Buckle up buttercup this will go fast
  37. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 55. Any questions? Andrew Wyld andrew.wyld@somoglobal.com @Andrew_Wyld
  56. 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. 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
  58. 58. Confidential and copyright of Somo Custom Ltd. April 16 58Confidential and copyright of Somo Custom Ltd. June 23 58
  59. 59. A quick dive into Deep Learning George Whitelaw 59
  60. 60. 60 Overview •Why complex problems require machine learning •What is a Neural Network •Solving complex problems •Deep learning in daily life
  61. 61. 61 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
  62. 62. 62 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. 63. What is a neural network 63 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. 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. 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. 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. 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. 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
  69. 69. 69 TIME TO EXPLORE
  70. 70. Messaging App Fragmentation Dave Evans - CTO
  71. 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 71Confidential and copyright of Somo Global Ltd. June 23
  72. 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 June 23 72 Reaching users tomorrow Confidential and copyright of Somo Global Ltd.
  73. 73. Confidential and copyright of Somo Global Ltd. June 23 73 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
  74. 74. Confidential and copyright of Somo Global Ltd. June 23 74 Chat Architecture #2 Application Logic + Language Processing Data Store Apple iMessage HTTP/s Comms Response Formatting Session/Conversation Management Message Receipt Message App
  75. 75. Confidential and copyright of Somo Global Ltd. • 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. June 23 75 Messaging Apps Fragmentation
  76. 76. Confidential and copyright of Somo Custom Ltd. April 16 76Confidential and copyright of Somo Custom Ltd. June 23 76

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