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Continuous Intelligence - Mat Kelcey
The document discusses the transformative potential of artificial intelligence (AI) across various industries and sectors, emphasizing how infusing AI can create new and more valuable opportunities. It highlights the importance of combining human judgment with machine capabilities to enhance decision-making and problem-solving tasks. Additionally, it addresses the challenges of ethics, risk, and governance in leveraging AI effectively.
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Continuous Intelligence - Mat Kelcey
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- 3. AMAZING OPPORTUNITY There isalmost 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 almostnothing 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 ANUMBER ● 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?
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- 8. AUGMENTATION OVER AUTOMATION MACHINES Widerlearners Scalable thinkers HUMANS Faster learners Flexible thinkers +
- 9. THE INTELLIGENT EMPOWERMENTCHALLENGE 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
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- 13. 3 CONSTRUCTION: Weatherboard EXIF LOCATION BEDROOMS PRICE: $1M BEDROOMS: 3 PHOTO CBDDISTANCE: 10km COMPOSABLE SOLUTIONS
- 14. CONSTRUCTION: Weatherboard EXIF LOCATION CBD DISTANCE: 10km BEDROOMS: 3 PHOTO CONDITION: Good PRICE:$1.1M EVOLVABLE COMPOSABLE SOLUTIONS
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- 16. THE NEW NEWPRODUCT DEVELOPMENT GAME PRODUCT RULES + Software architecture CODE Research & Analyse Code Deploy Test Validate CUSTOMER OBJECTIVES
- 17. THE NEW NEWNEW 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 NEWNEW 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 NEWNEW 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 STARTBUT FRAGILE AND LIMITED DIFFERENTIATION (EG MLAAS) X SIMPLE TO DO BUT LEAVES UNREALISED DATA VALUE (EG SPREADSHEETS) ITERATIVE SOLUTIONS
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- 22. ML Principals Product Squads MLPRODUCT SQUADS Self-service data platform Consumer responsibilities Producer responsibilities HCD+ML Diverse Teams
- 23. 23 ETHICS, RISK &GOVERNANCE
- 24. RISKS Bias Explainability Attack Susceptibility ! ! TECHRESPONSES 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 ATTHOUGHTWORKS For more information on ThoughtWorks please contact: Wayne Te Paa NAB Client Principal Email: wtepaa@thoughtworks.com Phone: 0416 041 772