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Continuous Intelligence - David Colls (ThoughtWorks Live)

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In this talk, we will help you to identify and deliver with confidence on opportunities to intelligently empower your business, customers, team members and other stakeholders, sharing our approach for building the right organisational capabilities.

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Continuous Intelligence - David Colls (ThoughtWorks Live)

  1. 1. 1 CONTINUOUS INTELLIGENCE David Colls ThoughtWorks Live 2018
  2. 2. 2 ARTIFICIAL INTELLIGENCE
  3. 3. AMAZING OPPORTUNITY There is almost nothing we can think of that cannot be made new, different, or more valuable by infusing it with some extra IQ. The business plans of the next 10,000 start-ups are easy to forecast: take X and add AI. Kevin Kelly – The Inevitable “ “
  4. 4. AND UNPRECEDENTED RESOURCES 2 DEDICATED PARALLEL HARDWARE 3 ADVANCED MODELLING 1 WEB-SCALE DATA DATA VOLUMES DOUBLE EVERY YEAR MASSIVE ADOPTION OF GPU (AND TPU, HPU, FPGA) LATEST RESEARCH AND TOOLS
  5. 5. NO WONDER AI GENERATES INTEREST FROM LEADERS But the big change from last year is that 81% cited artificial intelligence and machine learning as either very important or extremely important to their company’s future, up from just 54% in 2016. Fortune.com Fortune 500 CEO survey 2017 81% “ “
  6. 6. There is almost nothing we can think of that cannot be made new, different, or more valuable by infusing it with some extra IQ. Kevin Kelly – The Inevitable “ “ THE HYPE PROBLEM So how do we think about opportunities? AI CAN DO ANYTHING WE WANT (IT’S MAGIC)
  7. 7. There is almost nothing we can think of that cannot be made new, different, or more valuable by infusing it with some extra IQ. Kevin Kelly – The Inevitable “ AND THE KERNEL OF THE TRUTH “ AND ABOUT PEOPLE AND WE CAN BUILD ITERATIVELY AI IS NARROW (BUT COMPOSABLE) There is almost nothing we can think of that cannot be made new, different, or more valuable by infusing it with some extra IQ. Kevin Kelly – The Inevitable There is almost nothing we can think of that cannot be made new, different, or more valuable by infusing it with some extra IQ. Kevin Kelly – The Inevitable There is almost nothing we can think of that cannot be made new, different, or more valuable by infusing it with some extra IQ. Kevin Kelly – The Inevitable
  8. 8. 8 INTELLIGENT EMPOWERMENT
  9. 9. AUGMENTATION OVER AUTOMATION
  10. 10. AUGMENTATION OVER AUTOMATION MACHINES Wider learners Scalable thinkers HUMANS Faster learners Flexible thinkers +
  11. 11. THE INTELLIGENT EMPOWERMENT CHALLENGE High volume 
 judgement tasks are high cost 
 or variable quality High quality human judgement is hard to deliver at speed or scale Empower all your people with the judgement of your best performers Leadership Lenses BUSINESS CUSTOMERS EMPLOYEES Outperform with insight Experience driven by AI Your people at their best
  12. 12. 12 COMPOSABLE INTELLIGENCE
  13. 13. PROBLEM EXAMPLE PREDICT A NUMBER ● How much will this house sell for? ● How many shoes will sell this week, based on recent sales? BINARY PREDICTION ● Will this customer churn this week? ● Is this transaction fraudulent? PREDICT A CATEGORY ● Is this customer NEW, LOYAL, FICKLE, …? ● Is the t-shirt in this image CREW, V-NECK, POLO, …? UNDERSTANDING
 NATURAL LANGUAGE ● What is the sentiment of this text? ● What action and parameters is a customer requesting? RECOMMENDATION ● Given your purchases, what else might you buy? ● Given your contacts, who else might you know? WHAT TYPES OF PROBLEMS?
  14. 14. 3 CONSTRUCTION: Weatherboard EXIF LOCATION BEDROOMS PRICE: $1M COMPOSABLE SOLUTIONS BEDROOMS:
 3 PHOTO CBD DISTANCE: 10km
  15. 15. CONSTRUCTION: Weatherboard EXIF LOCATION CBD DISTANCE: 10km EVOLVABLE COMPOSABLE SOLUTIONS BEDROOMS:
 3 PHOTO CONDITION:
 Good PRICE: $1.1M
  16. 16. 16 CONTINUOUS INTELLIGENCE
  17. 17. THE NEW NEW PRODUCT DEVELOPMENT GAME PRODUCT RULES
 + Software architecture CODE Research & Analyse Code Deploy Test Validate CUSTOMER OBJECTIVES
  18. 18. THE NEW NEW NEW PRODUCT DEVELOPMENT GAME Software Engineering Machine Learning DATA SET
 + Network architecture MODEL Research & Analyse Code Deploy Test Test Validate Curate & Transport Train Deploy RULES
 + Software architecture CODE PRODUCT CUSTOMER OBJECTIVES
  19. 19. THE NEW NEW NEW PRODUCT DEVELOPMENT GAME Software Engineering Machine Learning Validate RULES GENERATE DATA USAGE GENERATES DATA RULES
 + Software architecture CODE PRODUCT CUSTOMER OBJECTIVES DATA SET
 + Network architecture MODEL
  20. 20. DEPLOYMENT REQUIRES ENGINEERING TRANSPORT REQUIRES ENGINEERING THE NEW NEW NEW PRODUCT DEVELOPMENT GAME Software Engineering Machine Learning Validate RULES
 + Software architecture CODE PRODUCT CUSTOMER OBJECTIVES DATA SET
 + Network architecture MODEL
  21. 21. Model Capacity Data Estate PROGRESS SWEET ZONE X RAPID START BUT FRAGILE AND LIMITED DIFFERENTIATION (EG MLAAS) X SIMPLE TO DO BUT LEAVES UNREALISED DATA VALUE (EG SPREADSHEETS) ITERATIVE SOLUTIONS
  22. 22. PoC IDEA TOO MANY PoCs? MAKE IT SIMPLER TEST IN LAB? DEPLOY TO PROD COLLECT, EVALUATE MODEL ITERATION DARK LAUNCH OR A/B TEST IN LAB ADD VALUE UNCERTAIN HOW TO ADD VALUE PoC IDEA CLEAR VALUE ADD REPEAT! REPEAT!
  23. 23. 23 ML PRODUCT SQUADS
  24. 24. ML Principals Product Squads Scale Partners ML PRODUCT SQUADS Self-service data platform Consumer responsibilities Producer responsibilities Organisational agility and learning culture HCD+ML Diverse Teams
  25. 25. 25 ETHICS, RISK 
 & GOVERNANCE
  26. 26. RISKS Bias Explainability Attack Susceptibility ! ! TECHNOLOGY RESPONSES Baseline Model Asymmetric Costs Wide & Deep Networks Fallback on low Confidence Use Canaries as Early Warning Black/white list inputs/outputs
  27. 27. In 2018, AI will gain a moral compass MUSTAFA SULEYMAN, DeepMind “ “
  28. 28. CORRUPTION OF SOCIAL SPACES DATA COLLECTION & SECURITY SOCIAL LICENCE ETHICS OF ALGORITHMIC DECISION MAKING AUTONOMOUS WEAPONS
  29. 29. 29 GETTING STARTED
  30. 30. We Can Build Iteratively AI is Narrow It’s About People CONTINUOUS INTELLIGENCE COMPOSABLE INTELLIGENCE INTELLIGENT EMPOWERMENT THE NATURE OF SOLUTIONS
  31. 31. WHERE TO FIND OPPORTUNITY BUSINESS CUSTOMERS EMPLOYEES VOLUME QUALITY SCALE
  32. 32. VERY FIRST STEPS ! Introduce a step for Machine Learning in your data pipeline (input == output) ! Look for a binary Yes/No suggestion or a suggested delta to existing results ! Then iterate
  33. 33. THANKS David Colls- dcolls@thoughtworks.com

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