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Rise of the Machines - AI in the Agile World

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Slides from my talk at aginext 2019.

In this session we’ll examine the AI capabilities available today in simple layman’s terms and explore how these will be used to augment and shape the agile world of tomorrow.

Artificial Intelligence (AI) has catapulted us into a brave new world of self-driving cars, delivery drones and talking robots. A combination of AI technologies including advanced machine learning, deep learning and natural language processing are now set to change the way we build and deliver products enabling us to build smarter software faster.

Imagine a world where product backlog prioritisation and feature discovery are aided by unbiased data analytics from AI systems. Self-learning products and adaptive user interfaces will automatically respond and adapt based on data driven analysis of real time user behaviour. Trouble shooting and recovery from production outages is accelerated and assisted by AI operations bots. This convergence of AI systems with the agile world will offer teams unprecedented visibility into their work and their products.

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Rise of the Machines - AI in the Agile World

  1. 1. Rise of the Machines AI in the agile world
  2. 2. Aidan Casey senior manager - Johnson Controls Cork @aidanjcasey https://medium.com/@aidanjcasey
  3. 3. Alpha Zero 2017 Alpha Go 2016 Deep Blue 1997 Watson 2011
  4. 4. 1997 Deep Blue
  5. 5. source: mashable.com/2016/02/10/kasparov-deep-blue/
  6. 6. source: eliiza.com.au/what-is-ai/chess-game-tree/
  7. 7. 2011 Watson
  8. 8. source: blog.ted.com/how-did-supercomputer-watson-beat-jeopardy-champion-ken-jennings-experts-discuss/
  9. 9. Natural Language Processing (NLP) + Deep QA
  10. 10. source: watson4all.blogspot.com/2014/01/the-science-and-technology-behind-ibm Deep QA
  11. 11. natural language processing
  12. 12. I walked down the street in a hat with a smile.
  13. 13. I walked down the street in a hat with a smile.
  14. 14. I walked down the street in a hat with a smile. Parse Analyse Classify Contextualise
  15. 15. 2016 AlphaGo
  16. 16. source: Google DeepMind via YouTube
  17. 17. machine learning
  18. 18. supervised machine learning Data Acquisition Clean and Prepare Data Apply & Test Learning Algorithms Train Model Deploy Model iterate to find best model
  19. 19. source: becominghuman.ai/
  20. 20. neural networks
  21. 21. 2017 AlphaZero
  22. 22. Source : https://deepmind.com/blog/alphago-zero-learning-scratch/
  23. 23. supervised learning machine learning unsupervised learning classification regression clustering association
  24. 24. Alpha Zero 2017 Alpha Go 2016 Deep Blue 1997 Watson 2011
  25. 25. Alpha Zero 2017 Alpha Go 2016 Deep Blue 1997 Watson 2011 custom algorithms tree Search natural language processing deep QA supervised machine learning unsupervised machine learning monte carlo tree search
  26. 26. AI in the Agile World
  27. 27. AI assisted SDLC Natural User Interface Design Self Learning Software
  28. 28. AI powered expert tooling NLP will provide ● enriched requirement models ● better code generation ● improved test automation ML will provide ● more predictable timelines ● test coverage recommendations
  29. 29. Accenture’s Virtual Scrum Master
  30. 30. https://www.agilealliance.org/resources/experience-reports/individuals-and-interactions-over-processes-and-tools/
  31. 31. Cross Industry Process for Data Mining (CRISP-DM)
  32. 32. ChatOps
  33. 33. Self Healing Systems machine learning analysis of • operation logs => root cause and fixing outages • network traffic • the OT => neutralise cyber attack vectors
  34. 34. evolution of the user interface Command Line Interface Graphical User Interface Natural User interface
  35. 35. Natural user interface design Intuitive systems that respond to speech and gestures Immersive interfaces
  36. 36. self-learning software
  37. 37. Self learning systems Convergence of machine learning with web analytics will provide a deeper understanding of our users ● AI assisted personalization ● Automatic persona discovery ● Persona refinement ● Intelligent feature usage reporting ● cohort analysis
  38. 38. unbiased product backlog prioritisation
  39. 39. software developers from coding fixed business logic to training machine learning models AI powered tooling for code generation software testers Automatic test case generation AI assisted test scenario planning UX designers Natural User Interface Design AI assisted persona mapping operations ChatOps Self healing systems product owners AI assisted backlog prioritisation Feature / persona usage insights agile teams AI assisted planning through expert systems
  40. 40. What will a product team look like in the future?
  41. 41. Agile Team will (still) need Autonomy, Mastery & Purpose
  42. 42. AI Ethics
  43. 43. AI Bias
  44. 44. the importance of diversity in AI
  45. 45. we are at an ethical inflection point ….
  46. 46. it’s up to us to write right the future
  47. 47. proposing the AI manifesto “We recognise that artificial intelligence will have a significant impact on our society. We pledge to only build AI systems that will contribute to the greater good.” ❖ AI systems must treat all people equally, they must not create or reinforce unfair bias ❖ AI systems must not be weaponized ❖ AI systems must be socially beneficial ❖ AI systems must respect privacy

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