Continuous Intelligence - Mat Kelcey
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Digital transformation has reinforced the success of an iterative and distributed approach to change, yet many organisations have adopted a 'big bang' or centralised mindset for their data strategy. To navigate the Artificial Intelligence (AI) landscape we should look to continuously augment human abilities at scale - in terms of speed, volume or quality - rather than simply look for the next wave of automation.
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Continuous Intelligence - Mat Kelcey
- 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. 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 “ HYPE, BUT A 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
- 5. PROBLEM EXAMPLE PREDICT A NUMBER ● How much will this house sell for? ● How many shoes will sell this week, based on recent sales? BINARY / CATEGORICAL PREDICTION ● Is this transaction fraudulent? ● Is this customer NEW, LOYAL, FICKLE, …? UNDERSTANDING NATURAL LANGUAGE ● What is the sentiment of this text? ● What action and parameters is a customer requesting? UNDERSTANDING IMAGES ● Does this image contain a specific type of object? ● Are these two images similar? RECOMMENDATION ● Given your purchases, what else might you buy? ● Given your contacts, who else might you know? WHAT TYPES OF PROBLEMS?
- 8. AUGMENTATION OVER AUTOMATION MACHINES Wider learners Scalable thinkers HUMANS Faster learners Flexible thinkers +
- 9. 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
- 13. 3 CONSTRUCTION: Weatherboard EXIF LOCATION BEDROOMS PRICE: $1M BEDROOMS: 3 PHOTO CBD DISTANCE: 10km COMPOSABLE SOLUTIONS
- 14. CONSTRUCTION: Weatherboard EXIF LOCATION CBD DISTANCE: 10km BEDROOMS: 3 PHOTO CONDITION: Good PRICE: $1.1M EVOLVABLE COMPOSABLE SOLUTIONS
- 16. THE NEW NEW PRODUCT DEVELOPMENT GAME PRODUCT RULES + Software architecture CODE Research & Analyse Code Deploy Test Validate CUSTOMER OBJECTIVES
- 17. 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
- 18. 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
- 19. 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
- 20. 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. ML Principals Product Squads ML PRODUCT SQUADS Self-service data platform Consumer responsibilities Producer responsibilities HCD+ML Diverse Teams
- 23. 23 ETHICS, RISK & GOVERNANCE
- 24. RISKS Bias Explainability Attack Susceptibility ! ! TECH RESPONSES Baseline Model Asymmetric Costs Wide & Deep Networks Fallback on low Confidence Distributional expectations on inputs/ outputs Black/white list inputs/outputs
- 25. THANKS Mat Kelcey - mkelcey@thoughtworks.com
- 26. YOUR CONTACT AT THOUGHTWORKS For more information on ThoughtWorks please contact: Wayne Te Paa NAB Client Principal Email: wtepaa@thoughtworks.com Phone: 0416 041 772