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AI Orange Belt - Session 4

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AI orange belt for business managers - All about artificial intelligence

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AI Orange Belt - Session 4

  1. 1. Session 4: Wrap-up AI Orange Belt
  2. 2. 3 2 1 Day 1 : Technical prerequisites • What is AI • What can AI do and what it can’t do Day 2 : Tactics & Methods • How to select a project • What are the steps necessary for a first successful ML project Day 3 : Strategy & Governance • AI Transformation Playbook • Steps to AI Maturity • AI Management/Ethics • How to think like a leader What we have seen so far 2
  3. 3. Plan for today 1. Recap on ML model learning + Neural Network learning 2. Build vs Buy 3. Cloud eco-system + Cloud architecture 4. Ethics / Privacy / Risk 5. Human Interface 6. AI Business Game 3
  4. 4. Recap on learning mechanisms 4
  5. 5. Simple example (House pricing) Input : size Output : price 𝒑𝒓𝒊𝒄𝒆 = 𝟓𝟎 + 𝟎, 𝟏 𝒔𝒊𝒛𝒆 ba 𝒑𝒓𝒊𝒄𝒆 = 𝒂 + 𝒃 𝒔𝒊𝒛𝒆 “Learning” = finding this 5
  6. 6. Simple example (Breast Cancer) Input : tumor size, age Output : malignant or benign 6
  7. 7. 7
  8. 8. ❌ ❌ 8
  9. 9. ✅ ✅ 9
  10. 10. Minimise an error function • By iteratively adjusting the parameters 10
  11. 11. A complete example 11
  12. 12. ? source: 3Blue1Brown12
  13. 13. source: 3Blue1Brown13
  14. 14. source: 3Blue1Brown14
  15. 15. source: 3Blue1Brown15
  16. 16. source: 3Blue1Brown16
  17. 17. source: 3Blue1Brown17
  18. 18. source: 3Blue1Brown18
  19. 19. GoogleNet (InceptionV1) (Szegedy et al, 2014) 19
  20. 20. Convolution Convolution examples on images What can you do with it ? Blurs the image Kernel (Filters) Detects edges 20
  21. 21. source: 3Blue1Brown21
  22. 22. ResNet (He et al, 2015) 22
  23. 23. Inception v3 23
  24. 24. 24
  25. 25. What does a Convolution sees ? VGG16, convolutional layer 1-1, a few of the 64 filters 25
  26. 26. What does a Convolution sees ? Variation of kernel size Source : 26
  27. 27. Adversarial Examples The left image is predicted with 99.9% confidence as a magpie. 27
  28. 28. Adversarial Examples Machine Learning classifiers today are easily fooled ! 28
  29. 29. Adversarial Examples Machine Learning classifiers today are easily fooled ! 29
  30. 30. Adversarial Examples 30
  31. 31. The problem with explainability https://distill.pub/2019/activation-atlas/ 31
  32. 32. Convolutional Neural Networks on Text From 2 dimensions to 1 dimension convolutions… 32
  33. 33. Object Detection : Yolo 33
  34. 34. Face Detection: Siamese Networks Cosine similarity ? 34
  35. 35. Auto Encoders Encoder Encoding Decoder 35
  36. 36. Denoising AutoEncoders Add noise Reconstruction Loss 36
  37. 37. Denoising AutoEncoders Original Images Noisy Images Reconstructed Images 37
  38. 38. Build vs Buy The eternal dilemma 38 Source : https://landing.ai/ai-transformation-playbook/
  39. 39. 39
  40. 40. With a consultant you don’t know, always look to start with a small proof of concept deliverable to prove to yourself that this consultant knows their stuff. Work with the consultant to come up with a project that is a low hanging fruit. Something that they can deliver on quickly without much development effort (e.g. based on existing code they already have, and data you have already collected). If this first step goes well, then you can confidently move to a bigger project scope.
  41. 41. Infrastructure database, computing, storage, monitoring https://mattturck.com/bigdata2018/ 42
  42. 42. Industry specific use case 43
  43. 43. Specific use case : research! Examples in HR 44
  44. 44. 45
  45. 45. 46
  46. 46. 47
  47. 47. 48
  48. 48. 49
  49. 49. 50
  50. 50. 51
  51. 51. 52
  52. 52. 53
  53. 53. 54
  54. 54. 55
  55. 55. 56
  56. 56. 57
  57. 57. Should you build vs buy ? 1. Is the task core-business ? 2. Is the task generic or should it be customized to your company ? 3. Is the cost of building it yourselves (total cost of ownership) < an off the shelve solution sold by a vendor ? 4. To which extent are you data strictly confidential ? If the answer is generally YES, then you should BUILD, otherwise consider BUYING. The cost will highly depend on the building strategy! 58
  58. 58. Cloud Architecture to support Machine Learning Source : https://landing.ai/ai-transformation-playbook/
  59. 59. 60
  60. 60. Major Players in Cloud Platforms 61
  61. 61. 62 Major Players in Cloud Platforms
  62. 62. 63
  63. 63. 64
  64. 64. 65
  65. 65. Cloud machine learning engine 66
  66. 66. 67
  67. 67. Cloud Vision API Recommendations AMAZON PERSONALIZE Image & Video AMAZON REKOGNITION Text Analytics AMAZON COMPREHEND Document Analysis AMAZON TEXTRACT Forecasting AMAZON FORECAST Conversational Agents AMAZON LEX Transcription AMAZON TRANSCRIBE Voice AMAZON POLLY Translation AMAZON TRANSLATE 68
  68. 68. Decision Speech Language Search Vision Content Moderator Anomaly Detector Personaliser Speech-to-text Speaker recognition Entity Video Image Auto-suggest Entity Video Image Auto-suggest Face detection / emotion / etc Indexer Form recogniser Image classification Azure ML 69
  69. 69. Mock example build a face detection app https://azure.microsoft.com/en-us/pricing/details/cognitive-services/ https://cloud.google.com/vision/pricing https://aws.amazon.com/rekognition/pricing/ 70
  70. 70. Mock example build a face detection app Number of detections per month? Training price and refresh of the model? Accuracy comparison? 71
  71. 71. 3 2 1 Day 1 : Technical prerequisties • What is AI • What can AI do and what it can’t do Day 2 : Tactics & Methods • How to select a project • What are the steps necessary for a first successful ML project Day 3 : Strategy & Gouvernance • AI Transformation Playbook • Steps to AI Maturity • AI Management/Ethics • How to think like a leader What we have seen so far 73
  72. 72. 01 03 02 06 04 05 Monitoring & Updates Have the right talents & solutions Maintenance Select the right question Choose the performance metric Decide the level of explainability Identify Use the right architecture Have the talents in place Deploy Find the right data Structure annotate data Clean Data Data Decide on an acceptable error Test on the right scope Evaluate Select the right algorithm Tune the model Model Applied AI Lifecycle © PROPERTY OF AI BLACK BELT 74
  73. 73. Where will you get it? Then prioritise by availability, accessibility & cost - existing data sources - data enrichment (feature engineering) - data augmentation - data generation - manual data labeling - create new data sources (e.g. sensors) - Public data, scraping, etc 76
  74. 74. Bias in a typical ML paradigm 77
  75. 75. Classification Regression Clustering Anomaly detection Recommendations Data generation 7 8
  76. 76. 79
  77. 77. 80
  78. 78. 5) Tuning hyperparameters (with cross-validation) 81
  79. 79. Precision & Recall metrics Let us speak in terms of seeing your doctor: ● Recall: Over all the times you should go see your doctor, how many times you really went? 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃 𝑇𝑃 + 𝐹𝑁 ● Precision: Over all the times you did go see your doctor, how many of times you really needed to see him? 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃 𝑇𝑃 + 𝐹𝑃 TP TNFP FN YES NO YES NO Predicted Actual 82
  80. 80. AI Transformation Playbook 1. Execute pilot projects to gain momentum 2. Build an in-house AI team 3. Provide broad AI training 4. Develop an AI strategy 5. Develop internal and external communications 83
  81. 81. but we can still assess feasibility 84
  82. 82. Foster trust By Element AI 85

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