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Intelligent Automation 2019

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The modern enterprise is becoming an increasingly automated environment: technological advancements in AI, Machine Learning and RPA are allowing organisations to strip out layers of inefficiency, optimise process and enhance productivity. Right across the enterprise, operations are changing in line with new automation tools, from low-level administrative tasks to self-regulating Industrial IoT systems and customer service chatbots.

This conference will contextualise the role of intelligent automation within the enterprise, looking at how the increasing sophistication of AI, RPA and IoT technologies are transforming operations. The conference is geared towards senior IT and digital leaders, providing an insightful peer-led environment and a crucial forum for knowledge exchange, engagement and high-level networking

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Intelligent Automation 2019

  3. 3. RAY BUGG DIGIT @DIGITFYI #iascot
  5. 5. Harnessing Data and Machine Learning to Improve Retail Decision Making Independent Consultant In Marketing & customer analytics and data science Founder/Owner of The Analysis Foundry Ltd
  6. 6. “Harnessing Data and Machine Learning to Improve Retail Decision Making” •The winners in retail are driven by those with the best quality data •You need an agile delivery method to succeed •How to make good use of Business Intelligence •There is still a lot of low hanging fruit that is ripe for automation •Skills are a primary barrier for capitalising on opportunity
  7. 7. Why Are Retailers Interested? “We are in the era of big data, and big data need statisticians to make sense of it. The democratization of data means that those who can analyse it well will win. Data is the sword of the twenty-first century, those who wield it well, the samurai.” Eric Schmidt & Jonathon Rosenberg “How Google Works”
  8. 8. And Retailers Have Actually Being Doing This Stuff
  9. 9. Where Are Retailers Trying To Go? IBM “The Battle For Personalised Loyalty” What will set winners apart? - Who has access to the most data? - Who has the best ability to garner insight & act on data? - Who can execute the best at the moment of truth?
  10. 10. Who Has Access To The Most Data?
  11. 11. Unstructured Data vs Untidy Data
  12. 12. Data Factory vs Data Laboratory WhoHasAccessToTheBESTData?
  13. 13. Who Has Best Ability To Garner Insight?
  14. 14. Data Scientists The “Sexiest Job in the 21st Century”
  15. 15. Working The Factory And The Lab “Your scientists were so pre occupied with whether or not they could, they didn’t stop to think if they should.” Dr Ian Malcolm, Jurassic Park
  16. 16. The Right Skills For The Right Jobs
  17. 17. Low Hanging Fruit?
  18. 18. The “Day Job” Analytics Tool Kit
  19. 19. Not All Insights Are Automated
  20. 20. 4 C’s
  21. 21. Give People The Right Tools
  22. 22. Who Can Execute Best?
  24. 24. © 2019 TVSquared All Rights Reserved Advertise. Attribute. Act. Supporting Those Who Support Others; Scaling Customer Support Regina Berengolts June 20, 2019
  25. 25. 27 Agenda • The “Abouts” • The Relationship Between Automation and Business Scale • Case Study: Customer Support
  26. 26. 28 The Worldwide Leader in TV Attribution TVSquared is trusted by thousands of brands, agencies and networks in more than 70 countries
  27. 27. 29 Global Footprint
  28. 28. 30 About Me Head of Data Science New Product Research Internal Business Improvements
  29. 29. 31 To Grow a Business, You Need More Than Just Algorithms Build and maintain the platform Market/sell to new clients Onboarding of new clients Support and customer success Do more with, at least, the same resource 1 2 3 4 5
  30. 30. 32 Automation and Scaling
  31. 31. 33 Intelligent Automation as a Continuum Automation and scaling Automation • With manual intervention Robotic Process Automation • With digital triggers or self service Machine Learning • With analytics and decision engines Artificial Intelligence • With deductive analytics Process-Driven Data-Driven
  32. 32. 34 Case Study: Customer Support
  33. 33. 35 Customer Support at TVSquared Client Onboarding Data Checks & QA Model Calibration Integrations Training Sales and Pre- Sales Support Ad-hoc Change and Service Requests Client Care
  34. 34. 36
  35. 35. 37 Where and How to Have the Greatest Impact Automation • Basic client communications Robotic Process Automation • Self-service onboarding Machine Learning • Data Checking • QA • Model Calibration Artificial Intelligence Process-Driven Data-Driven
  36. 36. 38 Get your hands dirty Define what “good” looks like Map out the process and iterate “Training” the System
  37. 37. 39 Overcoming Challenges in Adoption Trust is hard to get and easy to lose Collaboration Transparency Staged Rollout
  38. 38. 40 Team Impact Process Validation and Implementation Client care (daily requests and tickets) Training and demos Supervision of junior employees Customer success/insight for higher value clients Provide expertise internally Onboarding 70% time reduction QA 50% time reduction + Improved accuracy Model Calibration Immediate output + Improved accuracy Junior Roles Senior Roles
  39. 39. 41 Business Scale More Customers More Satisfied Customers Maintained Costs
  40. 40. 42 Thank you
  42. 42. QUESTIONS & DISCUSSION #iascot
  44. 44. Presenters: Intelligent Automation (IA) “The Journey to Scale” Presenters: Susan Weerts EY Intelligent Automation Delivery Leader June 2019 Jonathan Angove Blue Prism Solution Consultant
  45. 45. Intelligent Automation Innovation enabled by an integrated suite of digital tools, using existing applications and interfaces, leading to cost reduction, improved customer experience and staff satisfaction
  46. 46. Building a blended workforce People Automation Virtual workforce characteristics •Empathy & sympathy •Judgement •Complex problem solving •Scenario modeling •Building relationships •Delivering low-frequency and exception tasks •Managing change and improvement •Rules based execution of high volume transactions •Algorithm-driven insights •Unstructured to structured data translation •Advanced data analysis •Big Data focused •Communicating via emails, text, social media •Optical Character Recognition •Natural Language Processing • Non-invasive • Works 24/7 with consistency and accuracy • Benefits can include: — Increased productivity for high value employees — Improved customer & staff satisfaction — Cost reduction or avoidance — Enhanced revenue — Unlocked capacity
  47. 47. Blue Prism
  48. 48. 14,000 hours 1 week to 2 days For supplier query response times
  49. 49. 170 processes 280k hours
  50. 50. Hear from EY’s Chairman DRAFT - Confidential
  51. 51. What defines a successful IA adoption at scale? Clearly defined purpose Well equipped people Enhanced processes to realised benefits Technology fit for purpose
  52. 52. What lessons can you learn from organisations at scale? Strategy and planning Discover and solution Build and run
  53. 53. The Journey beyond process automation Cognitive automation Intelligent Chatbots Basic process automation Hybrid solutions
  54. 54. Work logically with a clearly defined purpose and aligned goals Be structured but flexible in your approach Communicate and be transparent
  55. 55. Contact Neil MacLean EY Intelligent Automation Lead Partner Office: +44(0)131 777 2035 Mobile: +44(0)7467 442037 Email: Jonathan Angove Blue Prism Solution Consultant Mobile: +44(0)7912673311 Email: Susan Weerts EY Intelligent Automation Delivery Lead Mobile: +44 7552 271 211 Email: Ed Mitchell EY Intelligent Automation Lead Mobile: +44 (0)7799 620707 Email:
  56. 56. EY | Assurance | Tax | Transactions | Advisory Ernst & Young LLP © 2019 Ernst & Young LLP. Published in the UK. All Rights Reserved. EYG00001-162Gbl The UK firm Ernst & Young LLP is a limited liability partnership registered in England and Wales with registered number OC300001 and is a member firm of Ernst & Young Global Limited. Ernst & Young LLP, 1 More London Place, London,SE1 2AF.
  58. 58. Machine Learning & Vision Applications Eyad Elyan, PhD Case Study: Toward Fully Automated Framework for Analysing & Interpreting Piping and instrumentation diagrams
  59. 59. 1 Background Machine Learning Deep Learning 2 Challenges & Research at CSDM Training Examples Authenticity 3 Case Study: Engineering Drawings Heuristic-based Solution Advanced Methods 4 Conclusion & Future Direction
  60. 60. Plan 1 Background Machine Learning Deep Learning 2 Challenges & Research at CSDM Training Examples Authenticity 3 Case Study: Engineering Drawings Heuristic-based Solution Advanced Methods 4 Conclusion & Future Direction
  61. 61. Machine Learning Machine Learning gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959) Observations (past examples) are used to train computers to perform certain tasks such as predicting future events: Spam detection Fraud detection Give a customer a loan? Shape and object detection and recognition, ... Massive amount of data, computational power and advanced Deep Learning models
  62. 62. 65 Years ago Paul Meehl published his book ‘Disturbing little book’ and in one of his studies he com different medicalcases In each of these 20 cases, the simple algorithms outperformed the well-informed huPaul E. Meehl, Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence Minneapolis, MN: University of Minnesota Press,1954)
  63. 63. Traditional Methods
  64. 64. 1 1 yann/talks/lecun-ranzato-icml2013.pdf
  65. 65. Going Deepe
  66. 66. 2 2 yann/talks/lecun-ranzato-icml2013.pdf
  67. 67. Significant Improvement
  68. 68. Object Recognition Until 2012: Leading methods used hand-crafted features + encoding methods (e.g, SIFT+Bag-of-Words+SVM) ImageNet - 2012 Large Scale Visual Recognition Challenge 1.2 million high resolution training images and each image belongs to one of the 1000 classes. The task is to get "correct" class in the top 5 best. Winning result - Kritzevsky - 16.4% - Convolutional Deep Neural Network Source:
  69. 69. Face Recognition DeepFace3 , a face recognition system was first proposed by FaceBook in 2014 achieved an accuracy of 97.35%, beating the state-of-the-art then, by 27%. 3 Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, June 2014, pp. 1701–1708
  70. 70. Cancer Detection, 2017 Google AI Just Beat Human at Detecting Cancer (89% vs 73% humans accuracy) 4 4 detectin.aspx
  71. 71. Plan 1 Background Machine Learning Deep Learning 2 Challenges & Research at CSDM Training Examples Authenticity 3 Case Study: Engineering Drawings Heuristic-based Solution Advanced Methods 4 Conclusion & Future Direction
  72. 72. Complexity & size of the data
  73. 73. Computing Power Deep Learning solutions can be deployedon small devices and normal pc’s. however, training these models require more computational power (i.e. cloud, GPUs).
  74. 74. Challenge: Training Examples - Data Availability & Distribution?
  75. 75. Training Examples - Availability Large number of annotated training images Transfer Learning has been successfully applied to different problems However, for a specific domains, manual annotation is still the most common approach Results:Adamu Ali-Gombe, Eyad Elyan, Chrisina Jayne, "Fish Classification in Context of Noisy Images". International Conference of Engineering Applications of Neural Networks (EANN) 2017: 216-226,DOI:
  76. 76. Training Examples - Distribution Learning algorithms including deepmodels tend to be biased towardthe dominant class of examples (common problem in different domains: Security, health, banking, energy. . . )
  77. 77. Challenge: Training Examples - Data Availability & Distribution? Possible Solutions
  78. 78. CDSMOTE: Class decomposition Class decomposition improved classification accuracy significantly Can weapply it to majority-class instances to reduce its dominance? Unlike other undersampling methods, no loss ofinformation
  79. 79. CDSMOTE Apply (CD) to majority-class instances to reduce its dominance Oversample minority-class instances N P 0200600 N_C1 N_C2 N_C3 N_C4 N_C5 P 0 150 100 50 200
  80. 80. Overlap-Based Method Identify and remove potential overlapped majority-class instances Use soft clustering technique i.e. Fuzzy C-means instead ofk-means Results:P. Vuttipittayamongkol, E. Elyan, A. Petrovksi, C. Jayne, "Overlap-based undersampling for improving imbalanced data classification. International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2018). Springer; November 2018
  81. 81. Generate Training Examples Use GANs5 to create realistic training samples and add it to the original dataset Challenge: Requires large number of training examples? 5 Goodfellow et al. Generative Adversarial Nets
  82. 82. Generate Training Examples (a) Imbalanced dataset[1] (b) Generate data to improvelearning[1,2] 1 A. A Gombe, E. Elyan, and C. Jayne 2019 Multiple Fake Classes GAN for Data Augmentation in Face Image Dataset. To appear at the International joint conference on neural networks 2019 ( I J C N N 2019), July 2019. 2 A. A Gombe, E. Elyan, Y Savoye, and C. Jayne 2018. Few-shot classifier GAN. Presented at the International joint conference on neural networks 2018 ( I J C N N 2018), 8-13 July 2018, Rio de Janeiro, Brazil.
  83. 83. Learning from Imbalanced Data Learning from few examples Results: A. A Gombe, E. Elyan MFC-GAN: Class-Imbalanced Dataset Classification... To appear (GANS)
  84. 84. Challenge: Authenticity of Images, Videos and other Types ofData
  85. 85. [source]
  86. 86. Video Tampering "Deepfake videos could ’spark’ violent social unrest"6 Recent developments in deeplearning-based methods have made it possible not only to also to fully synthesize video content 6 source
  87. 87. Video Tampering - The Challenge So many ways to edit/ forge a video or image No training data available!
  88. 88. Video Tampering Detection
  89. 89. Video Tampering Detection P. Johnston, E. Elyan and C. Jayne, “Video tampering localisation using features learned from authentic content”, Neural Computing and Applications, 2019, pp. 1-15, DOIL P. Johnston, E. Elyan and C. Jayne, “Spatial effects of video compression on classification in convolutional neural networks”,2018 International Joint Conference on Neural Networks (IJCNN), Rio deJaneiro, Brazil, 2018, pp. 1-8, DOIL P. Johnston, E. Elyan and C. Jayne, “Toward Video Tampering Exposure: Inferring Compression Parameters from Pixels”, Engineering Applications of Neural Networks, EANN 2018. Communications in Computer and Information Science, vol 893. Springer, DOI:,
  90. 90. Plan 1 Background Machine Learning Deep Learning 2 Challenges & Research at CSDM Training Examples Authenticity 3 Case Study: Engineering Drawings Heuristic-based Solution Advanced Methods 4 Conclusion & Future Direction
  91. 91. 1nd Phase (Jan-2017 to Jan 2018) Digitising Engineering Drawings (P&ID Diagrams) This Work was Supported by the Data Lab - Innovation Centre and DNV.GL
  92. 92. Engineering Drawings
  93. 93. Engineering Drawings Complex problem (different shapes of symbols, text, lines, lowquality documents. . .) Little existing work, despite the significant progress in core image processing tasks such as shape and object detection, recognition(R-CNN, SSD, R-FCN, YOLO) C. F. Moreno-García, E. Elyan & C. Jayne, "New trends on digitisation of complex engineering drawings", Neural Computing and Applications (2018) 31:1695.
  94. 94. Symbols Detection
  95. 95. Symbols Detection
  96. 96. Symbols Detection
  97. 97. Symbols Detection
  98. 98. Symbols Detection C. F. Moreno-García, E. Elyan, and C. Jayne, "Heuristics-Based Detection to Improve Text/Graphics Segmentation in Complex Engineering Drawings", 2017 International on Engineering Applications of Neural Networks. EANN 2017, Springer, DOI:
  99. 99. Dataset X = x21 x11 x12 x22 2n . .. . .. . . . . . . . x ... . ... x1n ,Y = .. .. xn1 xmn ym A dataset A with m instances of symbols x1, x2, ..., xm, where each instance xi is defined by an n features (pixels) as xi = (xi1, xi2, ..., xin). y1 .. Learn a function h(x) that maps a symbol xi ∈A to a class yj ∈Y .
  100. 100. Results & Limitations 87 E. Elyan,C. F. Moreno-García, and C. Jayne, “Symbols classification in engineering drawings”,to-appear in 2018 International Joint Conference on Neural Networks (IJCNN),DOI: Heuristics needto be adapted to meet other types of drawings / standards Requires extensive user interaction (two hours to complete adrawing)
  101. 101. 2nd Phase (Jul-2018 to July-2019) Digitising Engineering Drawings (P&ID Diagrams) This Work was Supported by the Oil and Gas Innovation Centre, The Data Lab Innovation Centre andDNV.GL
  102. 102. Deep Learning Framework Data collection Data/ Image annotation Train and Test Deep Learning models (detection, classification) Front-end Iterate & improve
  103. 103. Engineering Drawings Minimise user’s intervention Reduce processingtime Overcome limitations of the heuristics-based solution Account for different types of diagrams Create a pipeline of work
  104. 104. Engineering Drawings Problem Un-digitised, paper work Solution Automated method
  105. 105. Engineering Drawings Problem Un-digitised, paper work Solution Automated method
  106. 106. Engineering Drawings Problem Un-digitised, paper work Solution Automated method (less than 5 minutes)
  107. 107. Results 172 Diagram 90%:10% for training(155) and testing(16) P&IDs annotation of 29 class of symbols On average 180 symbol in each diagram 92% of symbols were recognised 80% of text elements wereaccuratelyidentified
  108. 108. Plan 1 Background Machine Learning Deep Learning 2 Challenges & Research at CSDM Training Examples Authenticity 3 Case Study: Engineering Drawings Heuristic-based Solution Advanced Methods 4 Conclusion & Future Direction
  109. 109. Conclusion & Future Direction Data is certainly the newoil Progress in Deep Learning solved many problems and at the same time creating new ones Collaboration: Joint projects with academia/ industry (H2020, EPSRC, KTP, Data Lab, OGIC, UKRI Innovate UK, Industry PhD programs...)
  110. 110. Thank You @ElyanEyad
  111. 111. DNV GL © 20 June 2019 SAFER, SMARTER, GREENERDNV GL © 20 June 2019 OIL & GAS Academic-Industrial Collaboration Case Study: 118 Automated Extraction of Information From Engineering Drawings Brian Bain: DNV GL Eyad Elyan: Robert Gordon University
  112. 112. DNV GL © 20 June 2019 Global reach – local competence 150+ years 100+ countries 100,000+ customers 12,000 employees 5% R&D of annual revenue MARITIME DIGITAL SOLUTIONS BUSINESS ASSURANCE ENERGYOIL & GAS Technology & Research Global Shared Services 119
  113. 113. DNV GL © 20 June 2019 Data is valuable – unlock it’s potential 120 Manage the quality, sharing and use of data for better insights and analytics Collate data from multiple sources Connect the data to provide a more complete picture Get insights and analytics
  114. 114. DNV GL © 20 June 2019 Our innovation initiatives: Near-term solutions and long-term foresight 121 EMPLOYEE-SOURCEDINNOVATIONSTRATEGY-LEDINNOVATION Years to market2 3 4 5 60 1 Long-term foresight into technology for sustainable industry growth Developing in-house technology expertise in collaboration with universities Intense projects to develop bold concepts or deeply explore specific fields of technology Developing near-term solutions to digitalizing the oil and gas industry ‘Crowd-sourced’ independent projects in collaboration with industry partners, solving common challenges Strategic Research Technology Leadership Extraordinary Innovation Projects Digital Innovation Joint Industry Projects
  115. 115. DNV GL © 20 June 2019 The Problem: Extraction of Data From Piping and Instrumentation Diagrams (P&IDs) 122 Equipment Items Instruments and Displays Coded Text Drawing Continuation Labels
  116. 116. DNV GL © 20 June 2019 The Problem: Extraction of Data From Piping and Instrumentation Diagrams (P&IDs) 123 Event Name Equipment Name Equipment Size Equipment Count ABC/JRP_WDP/JAS_SEP/G Vessel N/A 0.508 ABC/JRP_WDP/JAS_SEP/G Flange 6 3 ABC/JRP_WDP/JAS_SEP/G Flange 12 2 ABC/JRP_WDP/JAS_SEP/G Flange 26 1.5 ABC/JRP_WDP/JAS_SEP/G Flange 0.75 2 ABC/JRP_WDP/JAS_SEP/G Man Valve 1 9.5 ABC/JRP_WDP/JAS_SEP/G Man Valve 0.75 9.5 ABC/JRP_WDP/JAS_SEP/G Man Valve 8 3 ABC/JRP_WDP/JAS_SEP/G Man Valve 4 7.5 ABC/JRP_WDP/JAS_SEP/G Man Valve 12 1 ABC/JRP_WDP/JAS_SEP/G Man Valve 6 3 ABC/JRP_WDP/JAS_SEP/G Man Valve 16 1.25 ABC/JRP_WDP/JAS_SEP/G Act. Valve 6 1 ABC/JRP_WDP/JAS_SEP/G Piping 12 6 ABC/JRP_WDP/JAS_SEP/G Piping 0.75 10 ABC/JRP_WDP/JAS_SEP/G Piping 6 12 ABC/JRP_WDP/JAS_SEP/G Piping 1 29 ABC/JRP_WDP/JAS_SEP/G Piping 8 6 ABC/JRP_WDP/JAS_SEP/G Piping 4 12 ABC/JRP_WDP/JAS_SEP/G Piping 16 2.5 ABC/JRP_WDP/JAS_SEP/G Piping 26 3 ABC/JRP_WDP/JAS_SEP/G Piping 24 1 ABC/JRP_WDP/JAS_SEP/O Vessel 1 1.5 ABC/JRP_WDP/JAS_SEP/O Flange 1 32 A lot of information stored P&IDs is required to obtain a listing of process equipment for input to engineering studies. This is a labour intensive process which we would like to automate
  117. 117. DNV GL © 20 June 2019 Some More Detail ▪ The oil and gas industry (like most others) stores it’s data in multiple forms. ▪ More recent data will be in a digital form but a large mass of legacy information is in pdf files or paper copies. ▪ Even information which was created in a “smart” format with imbedded information may not have been passed on in this way. It may have to be reversed engineered to extract this information. ▪ This is a labour intensive process which is costly and prone to error. ▪ Can machine learning and deep learning techniques be used to ❑ automate this process? ❑ make it more accurate? ❑ make it faster? ❑ make it cheaper? ❑ add more value? ❑ establish an audit trail? 124
  118. 118. DNV GL © 20 June 2019 Establishing the Project and Obtaining Funding ▪ DNV GL and RGU discussed the problem and contacted the Data Lab to seek funding. ▪ An initial one year project was funded for 2017 to prove the concept ▪ Phase 2 of the project was set up to advance the technology. ▪ Funded primarily by the Oil and Gas Innovation Centre (OGIC) with further assistance from The Data Lab ▪ Current phase is due to be completed at end of July. 125
  119. 119. DNV GL © 20 June 2019 Making Use of the Extracted Data ▪ As shown, information on the location of equipment is saved in a file for further processing. ▪ For the initial application post processing is required to identify connection between components and inherit data from one to the other. ▪ In this application the main components are; • Vessels • Vessel Regions (split between areas containing gas, oil and water) • Vessel connection points to the pipework. • Pipework sections • Process equipment (valves, flanges, meters, heaters, coolers, pumps etc.) • Instruments • Pipe Connections • Text • Drawing connections 126
  120. 120. DNV GL © 20 June 2019 Constructing a Global Network ▪ Information obtained from the deep learning tool can be used to work out which elements are connected. ▪ It can also determine which pieces of text are associated with some of the elements. ▪ Combining all the connection information enables a global network to be established. ▪ The drawing connection information allows the network to cover multiple drawings. ▪ Information may be know about some of the elements and through inference this information can be transmitted throughout the network. ▪ The process allows us to contextualise the data automatically, making it richer while reducing the man hours involved. 127
  121. 121. DNV GL © 20 June 2019 Global Network – Vertical View Vessels 128 Vessel Regions Connection Points (Parent) (Child/Parent) (Child)
  122. 122. DNV GL © 20 June 2019 Global Network – 3D View 129 Vessel Nodes Pipes Pipe Junctions Equipment Items
  123. 123. DNV GL © 20 June 2019 Plane View – Plan View Seeding and Data Inference 6”/4” 8” 6” 6” 4” 4” 4” 4” 4” 4” 4” 3” 3” 3” 3” 3” 3” 3” 3” 3” 8”/10” 2” 2” 2” 8” 8” 8” 8” 8” 8” 8” 8” 8” 10” 10” 10” 10” 10” 10” 10” 8” A pipe size can be used to seed part of the network Equipment which changes the pipe size can also be used. A similar process can be used for other parameters/properties.
  124. 124. DNV GL © 20 June 2019 Extraction of Data From P&IDs – The Solution 132 Event Name Equipment Name Equipment Size Equipment Count ABC/JRP_WDP/JAS_SEP/G Vessel N/A 0.508 ABC/JRP_WDP/JAS_SEP/G Flange 6 3 ABC/JRP_WDP/JAS_SEP/G Flange 12 2 ABC/JRP_WDP/JAS_SEP/G Flange 26 1.5 ABC/JRP_WDP/JAS_SEP/G Flange 0.75 2 ABC/JRP_WDP/JAS_SEP/G Man Valve 1 9.5 ABC/JRP_WDP/JAS_SEP/G Man Valve 0.75 9.5 ABC/JRP_WDP/JAS_SEP/G Man Valve 8 3 ABC/JRP_WDP/JAS_SEP/G Man Valve 4 7.5 ABC/JRP_WDP/JAS_SEP/G Man Valve 12 1 ABC/JRP_WDP/JAS_SEP/G Man Valve 6 3 ABC/JRP_WDP/JAS_SEP/G Man Valve 16 1.25 ABC/JRP_WDP/JAS_SEP/G Act. Valve 6 1 ABC/JRP_WDP/JAS_SEP/G Piping 12 6 ABC/JRP_WDP/JAS_SEP/G Piping 0.75 10 ABC/JRP_WDP/JAS_SEP/G Piping 6 12 ABC/JRP_WDP/JAS_SEP/G Piping 1 29 ABC/JRP_WDP/JAS_SEP/G Piping 8 6 ABC/JRP_WDP/JAS_SEP/G Piping 4 12 ABC/JRP_WDP/JAS_SEP/G Piping 16 2.5 ABC/JRP_WDP/JAS_SEP/G Piping 26 3 ABC/JRP_WDP/JAS_SEP/G Piping 24 1 ABC/JRP_WDP/JAS_SEP/O Vessel 1 1.5 ABC/JRP_WDP/JAS_SEP/O Flange 1 32 Continuity Labels Number Tag x y w h Direction Location 1 JA04-03-AP-00094 5581 3468 283 61 right G4 2 JA04-03-AP-00013 182 2437 284 61 right A3 8 JA04-03-AP-00045 5581 1080 283 60 right G2 9 JAO4-03-AP-00177 182 979 284 61 right A2 Sensors Number Type Tag x y r Location 1 Local Sensor 103-PT-1029 3662 2230 70 D3 2 Local Sensor 103-LD-1018 3954 2264 70 E3 3 Local Sensor 103-PST-1028 1534 2264 70 B3 18 Panel Mounted Sensor 103-LI-1018 3954 2006 70 E3 19 Local Sensor 103-LI-1015 3784 1658 70 E2 20 Panel Mounted Sensor 103-TIC-1021 3572 2834 66 D4 21 Panel Mounted Sensor 103-LSI-1017 3338 1772 65 D3 22 Panel Mounted Sensor 103-UA-1028 2110 1828 65 C3 Tags Number Assoc. Equip. No. Tag x y r Location 1 8 103-SDV-1024 3796 3520 70 E5 2 14 103-SDV-1026 5106 3362 66 F4 7 62 103-PSV-1027 1472 834 65 B1 8 61 103-PSV-10278 2538 834 66 C1 Equipment Symbols Number Class x y w h Location 1 DB&BBV (Vertical-Left) 4865 3855 82 74 F5 2 DB&BBV (Vertical-Left) 5133 3854 83 76 F5 13 Valve Ball 4589 3430 72 71 E4 14 ESDV Valve Slab Gate 4960 3339 71 145 F4 15 DB&BPV (Vertical) 3963 3304 99 73 E4 16 Valve Plug (Vertical) 4226 3249 49 72 E4 17 Reducer (Vertical-Down) 3980 3216 37 39 E4 18 Valve Ball 4377 3059 72 71 E4 28 Vessel Connection 2764 2583 33 38 C3 29 DB&BPV (Horizontal) 2675 2566 71 99 C3 This involves breaking the drawing down into its component parts and identifying them. The data can be exported to a file Post processing of this data can be used to allocate the parts count information to various loss of containment scenarios. This processed data then becomes input to further analytical applications.
  125. 125. DNV GL © 20 June 2019 Equipment to Sensor and Instrumentation Linking Automated Validation Checks 133 Visualisation and Search Tools HAZOP Node Identification Data InheritanceNetwork BuildingClassificationSegmentationLoading image Parts Count Allocation Process Stages and Alternative Information Sets From P&IDs
  126. 126. DNV GL © 20 June 2019 Extension to Other Drawing Types 134 Piping Isometrics Structural Layouts Tabulated Data Electrical Diagrams
  127. 127. DNV GL © 20 June 2019 Business Impact ❑ Faster Processing ❑ Less manhours = less cost ❑ More accurate (possibly) 135 Existing Processes New Opportunities ❑ Transform paper copies to a digital format ❑ Amalgamation of different data types ❑ Digital twins containing data on physical assets ❑ Improved visualisation and identification ❑ Automated Design Checks
  128. 128. DNV GL © 20 June 2019 SAFER, SMARTER, GREENER The trademarks DNV GL®, DNV®, the Horizon Graphic and Det Norske Veritas® are the properties of companies in the Det Norske Veritas group. All rights reserved. Automated Extraction of Information From Engineering Drawings 136
  130. 130. Organisational challenges of A.I implementations MartinThorn CYBGPlc
  131. 131. WhoareCYBG?
  132. 132. Classification: Public endto the possibilities there is no we are only limited by our imaginations Machine learning & A.I – it’s the future, right?
  133. 133. Classification: Public So what’s stopping us? meet Derek
  134. 134. Classification: Public Why isn’t Derek on side? overselling trust unflattering people don’t like change machine learning = A.I scaredabout job terminator the those in black mirror robot dogs
  135. 135. Classification: Public Overselling revolutionise this will your business only going we’re to get one shot everyone elsedoing it is voice to chatleft behind we can’tbe the savingswill be huge
  136. 136. Classification: Public Trust trust me the algorithm works it all out 7th May 2018 IFttt r a d a rcruise control do the techies trust the tech? customerchurn model human makes mistake ‘puter makes mistake
  137. 137. Classification: Public Change can you have a revolutionwithout changing things? meaningful change is never easy people liketheir way of doing things can I say to my goodbye car? rationally it might make sense giving the chat project to the voice person people rational aint @ home
  138. 138. Classification: Public Unflattering this will make us ten timesmore productive you’re saying we aren’t productive now? first time resolution i know a good employee when I see one what if “it” spots something we missed? if it was worth we’d have already doing done it we’ve already optimised this workflow
  139. 139. Classification: Public Hmmmmmm well that’s exceptionally depressing i’m afraid it is toughsorry this is not going to be easy sorry sorry if it was everyone easy would do it
  140. 140. Classification: Public My top tips start reallysmall convince a non-techie don’t ask for £100k ask for £10k lots and lots of tiny experiments overcommunicate worst case:build it out of hours dumb in prod > amazing on the shelf hire data scientists that can talkto humans pattern recognition not machine learning algorithms aren’t sexy d.p.o are your friends
  141. 141. Thanks for listening @martin_thorn
  143. 143. Privacy and Security in AI Ivana Bartoletti – Head of Privacy, Data Protection and Ethics
  144. 144. Contents ▪ Background to Privacy and Security in AI ▪ Approach to Privacy and Security in AI ▪ Key issues with Privacy and Security in AI ▪ Digital Persistence ▪ Repurposing ▪ Training and Testing Data ▪ Solutions ▪ Persistence ▪ Accuracy ▪ Transparency ▪ Security ▪ Regulation of AI systems ▪ Questions and Answers Gemserv 152
  145. 145. Background to AI and Privacy • Organisations are increasingly looking towards data analytics to make more informed and efficient decisions. • Data analytics allows companies to make sense of data and develop patterns and predictions. • Artificial Intelligence (AI) can allow for evolving analysis of data, predictive functions and even decisions. Gemserv 153 However, AI can lead to: • Persistence: data once created will persist longer that human have created it. • Lack of transparency: AI systems may appear to be a “black box”. • Synthetic and Inferred Data: Systems could rely on presumptions in using or creating data. • Security issues: AI systems can use extensive databases and wide data collection, which can pose issues for keeping data secure.
  146. 146. Approach to Privacy and Security in AI Gemserv 154 Context and Setting An individual’s ‘reasonable expectation’ of privacy will differ depending on the context. Security Elements Different system setups used in AI systems – including the use of databases, APIs and cloud servers, can involve different security vulnerabilities. Transparency Individuals may not be aware of how their personal data will be used, particularly when algorithms or “black box” decisions are used. Behavioural Economics An individual’s desire for data privacy will depend on how they anticipate that data's effect on future economic outcomes.
  147. 147. Background to the deployment of AI • Online Advertising Systems characterise individuals into social and demographic categories on the basis of tracking their online behavioural interests. • AI-based chatbots supplement the work of call-centre staff in the pressure of rising demand and reduced resources. • Smart Homes monitor residents’ and homeowners’ use of appliances at home and behavioural habits, in order to reduce water and energy use. • HR systems powered by AI and used to shortlist and screen candidates on the basis of their backgrounds or worthiness assessments. • Robotic Process Automation (RPA) for high-volume repetitive activities which are difficult or costly to automate with traditional system integration techniques. Gemserv 155 • E-commerce and Retail websites using chatbots, cookies, online advertising/profiling. • Healthcare increasingly relies on diagnostic tools to monitor patients and prescribe treatment. • Energy sector is using AI in various applications, including the context of analysing consumption trends, smart grids, and oil and gas exploration. • Manufacturing is using AI to improve customer interaction with products, such as connected cars and smart devices. • Local government are using AI to facilitate the delivery of public services, including decisions around the allocation of social benefits and ‘smart cities’. SectorsProducts
  148. 148. Key Privacy Issues in Security and AI Gemserv 156 PERSISTENCE ACCURACY TRANSPARENCY SECURITY
  149. 149. Persistence The persistence of data creates problems for individuals to exercise their rights not be subject to processing and for control or autonomy over their personal data. Gemserv 157 Retention Periods In the digital environment, information can persist for long periods and in different forms. Organisations can continue to use personal data for ongoing analysis. Applicability In particular, the currency of data may change over time – rendering it inaccurate or irrelevant. Use of AI systems can exaggerate these effects, where the algorithm continually learns from and reuses the data. Example A woman uploads photos onto a social media platform, aged 15. Ten years later, the recruitment department of a company where she applies for a role examines her Facebook profile to decide if she will be a reputable candidate.
  150. 150. Accuracy The inaccuracy of data may run into compliance issues when such data is used to make decisions about individuals. Individuals generally have a right for it to be corrected. Gemserv 158 Inferences In the course of profiling, AI systems may extrapolate or infer certain characteristics about an individual. Additionally, statistical data could be used to profile people, based on demographic characteristics and trends. Discrimination Discrimination can result from inaccurate data that makes assumptions about trends and behaviours to group individuals. For example, defining ‘good employees’ and ‘productive workers’. Example A large retailer uses personal data collected through cookies about browsing history and interests in order to create profiles of individuals. This is used to target specific advertising at them. Learning If the system "learns" from biased or inaccurate training data, AI systems can exacerbate existing inaccuracies.
  151. 151. Transparency Summary Gemserv 159 Class labels Issues can be unfairly applied to individuals where companies can choose their own variables and labels. For example, defining ‘good employees’ and ‘productive workers’. Efficiency trade-offs There is a trade-off between limiting algorithm functions to ensure transparency and accountability and ensuring efficiency. The case of the Black Box and the myth of explainability. The GDPR • Art. 22 • Recital 71 • Article 15: the way forward? Interplay with information rights (13, 14)
  152. 152. Explanations Gemserv 160 Explanations • Model Centric Explanations (MCEs) • Subject Centric Explanations (SCEs) • Limits and barriers Better explanations with the GDPR? • Right to erasure • Data Portability Privacy by Design: • Bias & fairness • External agencies to test systems • Audit trails • DPIAs • Certification Schemes Scope of Article 22 GDPR
  153. 153. Security Gemserv 161 Database security Large databases used in AI systems represent a significant target to identity thieves, data breaches and other incidents. Organisations can protect such data by using methods such as encryption at rest. Authentication Staff will typically have need access to AI systems or database to support data subjects with their right to human intervention. GDPR example The GDPR requires organisations to introduce appropriate technical and organisational measures for data security on a risk basis. Organisations should consider relevant measures to ensure the confidential, integrity and availability of the data. Summary Cloud or server-based If data is stored on cloud-based system, including that: • Communications for the transfer in and out of the cloud is kept secure; • Ensure you comply with legislation for international data transfers.
  154. 154. AI and Privacy Solutions Gemserv 162 The following principles should be followed in the delivery of AI solutions: Data sets should be… …Chosen and cleaned to avoid any issues with accuracy or content that could have unfair impacts on individuals Algorithmic functions need to be constrained… …To avoid inferring or ascribing characteristics to individuals unfairly Privacy by Design principles… …Need to be embedded in the system to ensure data collected is the minimum necessary … The issue of the BLACK BOX and the question of explainability
  155. 155. AI and Privacy Solutions Gemserv 163 Data Security Organisations should ensure Data Security around the use of AI analytics, such as by: • Ensuring appropriate access controls are introduced for uses on APIs and databases used by AI systems. • Carrying out an assessment of the vulnerabilities on networks where data are stored and accessed. Third Parties With regard to third parties, organizations must: • Carry out due diligence of third parties, including hosting systems and bought-in data sets. • Ensure that liability for decisions made as a result of AI systems is apportioned with third parties. Codes of Conduct Organisations should ensure transparency around the use of AI analytics, such as by: • Publishing a Code of Conduct, outlining the organisation’s commitment to fair and unbiased AI and to protecting privacy. • Use transparency notices to inform individuals of how their personal data with be used and their rights. The following principles should be followed in the delivery of AI solutions:
  156. 156. AI and Privacy Solutions Gemserv 164 Training and Testing When training and testing AI systems, organisations should: • Introduce procedures for checking for accuracy and data cleansing. • Evaluate the impact of data analytics and profiling on groups of individuals. Individuals Rights Policies and procedures should ensure that: • Data subject rights, including access and correction to personal data, can be processed by staff and systems. • Staff are trained to recognized, respond and escalate such requests. Algorithmic Impact Assessments (AIAs) Algorithmic Impact Assessments take the form of an audit or gap analysis that allows an organisation to determine whether they comply with legal, industry and ethical requirements and norms. They have been recommended to be conducted by the IEEE. The following principles should be followed in the delivery of AI solutions:
  157. 157. Gemserv 165
  158. 158. Key Points Gemserv 166 AI should service defined goals and the public interest AI systems can exaggerate many existing data privacy issues Transparency and data ethics should be at the core of systems AI systems and networks present security challenges
  159. 159. Thank you, any questions? Ivana Bartoletti Head of Privacy, Data Protection and Ethics
  161. 161. QUESTIONS & DISCUSSION #iascot
  162. 162. DRINKS & NETWORKING #iascot
  163. 163. DX3 Distributed - Decentralised - Disruptive DX3os is a decentralised operating system for autonomous industrial machines | © DX3 2018-2019
  164. 164. The Machine Economy "The Machine Economy is one in which machines are autonomous market participants that have their own bank accounts. In the near future, it's expected that M2M participants will be able to lease themselves out, hire their own service engineers and pay for their own servicing and replacement parts” - McKinsey © DX3 2018-2019 | Simon Montford | | @dx3os
  165. 165. Machine Commerce © DX3 2018-2019 | Simon Montford | | @dx3os
  166. 166. Machine Automation Source: PWC © DX3 2018-2019 | Simon Montford | | @dx3os
  167. 167. Real-World Examples © DX3 2018-2019 | Simon Montford | | @dx3os
  168. 168. Open Platform © DX3 2018-2019 | Simon Montford | | @dx3os
  169. 169. What is DX3? DX3 is a decentralised ecosystem that will facilitate autonomous machine-to-machine interactions and transactions. What is DX3os? DX3os is an operating system [similar to middleware] that connects autonomous industrial machines. © DX3 2018-2019 | Simon Montford | | @dx3os
  170. 170. Project objectives Objectives: increase operational efficiency, trust, and safety. Reduce costs of doing business and exposure to cyberattack. Energy | Mining | Construction © DX3 2018-2019 | Simon Montford | | @dx3os
  171. 171. DX3 Consortium Mission: open-source enterprise- focused Blockchain Platform and Ecosystem, that will give all participants an equal opportunity to create and obtain value. © DX3 2018-2019 | Simon Montford | | @dx3os
  172. 172. Commercial Benefits Value Creation: increase ROI by automating tasks and enabling autonomous industrial machines and edge devices to operate autonomously. © DX3 2018-2019 | Simon Montford | | @dx3os
  173. 173. DX3C - Contact us to find out how to join The DX3 Consortium Distributed - Decentralised - Disruptive | © DX3 2018-2019 | Simon Montford | | @dx3os
  174. 174. Geoff Ballinger / Head of Platform / @geoffballinger The Internet Of (very big) Things: Adventures in Using Event Based Vision Systems for Localisation on Trains
  175. 175. Future transport will be highly automated and coordinated. Knowing the position of each vehicle will be critical.
  176. 176. Sensors, data and algorithms are replacing infrastructure- based localisation such as GPS Image courtesy of Waymo
  177. 177. People didn’t need infrastructure to navigate in the past
  178. 178. The ground has measureable structure that is unique to a location
  179. 179. Image courtesy of Prophesee Event-based vision solves many problems inherent in traditional computer vision
  180. 180. Our on-vehicle camera-based mapping solution determines accurate location using ground fingerprints
  181. 181. Fast Forward a few years …
  182. 182. Currently no localization solution is accurate enough for predictive maintenance
  183. 183. We improve localisation by reducing motion estimation error AND continuously correcting for drift State of the art GNSS error 5% motion est. error 1% motion est. error
  184. 184. Initial trials gave 2cm accuracy at 100km/h GPS
  185. 185. RailLoc RailLoc Engine RailLoc Sensor RailLoc Map Server GPS Track ID, Position & Velocity Offline GPS Correction
  186. 186. Geoff Ballinger / Head of Platform / @geoffballinger Thanks for listening!
  187. 187. Adopting Automation Andrew Bone
  188. 188. “ To take the path of automation, you must desire progress and you must trust technology” Andrew Bone, a few seconds ago
  189. 189. What kind of Automation? Projects Optimised Plans People
  190. 190. 2. Controls a process that is: • Strategically important • High risk • Emotive Automation intertwined with people 1. Works in partnership with human planners
  191. 191. Automation Time 0: None 1: Assisted 2: On Demand 3: Always On TrustAutomation Adoption Curve
  192. 192. Andrew Bone, 10 minutes ago “To take the path of automation, you must desire progress and you must trust technology”
  193. 193. Desire Trust Ostriches Staunch Traditionalists Nervous Flyers Confident Champions Attitudes to automation
  194. 194. Case Study 1: Big 4 Delivery Centre • >1,000 people based in Poland • >50,000 tasks a month • Tasks last minutes or hours • Very dynamic • Complex task assignment criteria • Pressure to be more efficient
  195. 195. Automation Time 0: None 1: Assisted 2: On Demand 3: Always On Automation Adoption Curve
  196. 196. Case Study 1: What was our journey? Desire Trust 12 3 Desire Trust 1 2 3 4
  197. 197. Case Study 1: What did we learn? Huge Desir e Start simple and build trust! Quest for Perfectio n Too Clever Lack of Trust
  198. 198. Case Study 2: UK Prof. Services Firm • 4,500 auditors based in UK • Approximately 36,000 tasks a month • Tasks last days or weeks • Somewhat dynamic • Incumbent system 20 years old
  199. 199. Automation Time 0: None 1: Assisted 2: On Demand 3: Always On Automation Adoption Curve
  200. 200. Desire Trust 2 1 3 Case Study 2: What was our journey? Desire Trust 1 2
  201. 201. Case Study 2: What did we learn? Automation is not everything Easy Wins Automatio n Deprioritise d Compellin g Data Future Automation
  202. 202. “To take the path of automation, you must desire progress and you must trust technology” Andrew Bone, 25 minutes ago
  203. 203. Desire Trust Modest Wins ProveValue
  204. 204. Andrew Bone
  205. 205. Good-Loop AI MadTech
  206. 206. The Internet The internet was invented in the 1970s by the US military, Sir Tim Berners-Lee, and Al Gore to share photos of cats. It has grown a bit since then. 2018 marked the point when most of the human race was online. A huge step forward in information sharing. But not everything is going as planned. Let’s talk about data, AI, and advertising.
  207. 207. GOOD-LOOP We giveadvertisermoneyto charity. And everyone wins.
  208. 208. TalkStructure 1. Introduction 2. TheDataEconomy&Current Trends 3. Ethics 4. Howpersonaldataworksin ads 5. Questions&Comments
  209. 209. Howbig isthepersonal dataeconomy? ● DMPs(minusthebig two): $0.5 to $1.5 billion (Forrester,MarketResearch Future) ● Overallmarket effect: $18 billion to $1,000 billion (OnAudience,EuropeanCommission) DMP = Data Management Platform
  210. 210. EverySaleisaPersonalSale EveryAdvert isTargeted…buthow well?
  211. 211. CurrentTrendsin AI-for-Advertising ● Bigger,faster, stronger! ○ Morebuilt-in AI ○ Moreclosedloops ○ Moredata? ● Growthof chatand voice. ● Intentdrivenadvertising( keywords) ● AIassistantsfor campaigns ...includingsomecreative content
  212. 212. Bewarethe Hype
  213. 213. LiveRamp? Lotame? BlueKai? Have you heard of them? But they have heard of you. Or rather, they provide a grey market for data on you to be bought and sold. Welcome to data-laundering.
  214. 214.
  215. 215. 30 billion times a day
  216. 216. HowTrackingWorks ● Ads and webpages include tracking pixels from various DMP companies(e.g. BlueKai, AppNexus,butalsoGoogle, Facebook). ● A pixeldropsacookie onyourbrowser. ● Thebrowsercanthenberecognisedacross sitesandadsthatcarrythatDMP’s pixels. ● Loginsorguessescantrackacrossdevices (e.g.desktop→ phone). ● Cookie-syncing betweencompaniesjoins activityacrossdatasets(maybeillegal).
  217. 217. You look 13 to 54
  218. 218. Thank you Questions & Comments