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Future of AI-powered automation in business

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Starting from examples of current use cases of AI in business and in everyday life, we'll see what the future holds and we'll mention questions to address when giving autonomy to intelligent machines. We'll also aim at demystifying how AI works, in particular how machines can use data to automatically learn business rules and actions to perform in different contexts.

Published in: Technology

Future of AI-powered automation in business

  1. 1. Future of AI-powered automation in business @louisdorard #APIdays - December 9, 2015
  2. 2. AI is everywhere
  3. 3. @louisdorard
  4. 4. Lars Trieloff @trieloff (see source)
  5. 5. Amazon for David Jones (@d_jones, see source)
  6. 6. Amazon for David Jones (@d_jones, see source)
  7. 7. ChurnSpotter.io
  8. 8. How does it work?
  9. 9. Data + Machine Learning
  10. 10. Bedrooms Bathrooms Surface (foot²) Year built Type Price ($) 3 1 860 1950 house 565,000 3 1 1012 1951 house 2 1.5 968 1976 townhouse 447,000 4 1315 1950 house 648,000 3 2 1599 1964 house 3 2 987 1951 townhouse 790,000 1 1 530 2007 condo 122,000 4 2 1574 1964 house 835,000 4 2001 house 855,000 3 2.5 1472 2005 house 4 3.5 1714 2005 townhouse 2 2 1113 1999 condo 1 769 1999 condo 315,000
  11. 11. Bedrooms Bathrooms Surface (foot²) Year built Type Price ($) 3 1 860 1950 house 565,000 3 1 1012 1951 house 2 1.5 968 1976 townhouse 447,000 4 1315 1950 house 648,000 3 2 1599 1964 house 3 2 987 1951 townhouse 790,000 1 1 530 2007 condo 122,000 4 2 1574 1964 house 835,000 4 2001 house 855,000 3 2.5 1472 2005 house 4 3.5 1714 2005 townhouse 2 2 1113 1999 condo 1 769 1999 condo 315,000
  12. 12. ML is a set of AI techniques where “intelligence” is built by referring to examples
  13. 13. “Weak AI” vs. “Strong AI”
  14. 14. 27 Everyday use cases • Real-estate • Spam • Priority inbox • Crowd prediction property price email spam indicator email importance indicator location & context #people Zillow Gmail Gmail Tranquilien
  15. 15. 28 Business use cases • Reduce churn • Cross-sell • Optimize pricing • Predict demand customer churn indicator customer & product purchase indicator product & price #sales context demand RULES
  16. 16. –Katherine Barr, Partner at VC-firm MDV "Pairing human workers with machine learning and automation will transform knowledge work and unleash new levels of human productivity and creativity."
  17. 17. Decisions from predictions
  18. 18. 1. Descriptive 2. Predictive 3. Prescriptive 31 Phases of data analysis
  19. 19. 1. Show churn rate against time 2. Predict which customers will churn next 3. Suggest what to do about each customer
 (e.g. propose to switch plan, send promotional offer, etc.) 32 Churn analysis
  20. 20. 1. Show returned goods against {type, customer segment} 2. Predict risk shopper will return goods 3. ? 33 E-commerce returns
  21. 21. “Suggest what to do about each customer”→ prioritised list of actions, based on… • Customer representation + context • Churn prediction & action prediction • Uncertainty in predictions • Revenue brought by customer & Cost of actions • Constraints on frequency of solicitations 34 Churn analysis
  22. 22. 35 Pricing optimisation Again, from David Jones (@d_jones, see source)
  23. 23. Decide price given product and context… • For several price candidates (within constrained range): • Predict # sales given product, context, price • Multiply by price to estimate revenue 36 Pricing optimisation
  24. 24. Decide price given product and context… • For several price candidates (within constrained range): • Predict 95%-confidence lower bound on # sales given product, context, price • Multiply by price to estimate revenue 37 Pricing optimisation
  25. 25. 1. Show past demand against calendar 2. Predict demand for [product] at [store] in next 2 days 3. Suggest how much to ship • Trade-off: cost of storage vs risk of lost sales • Constraints on order size, truck volume, capacity of people putting stuff into shelves 38 Replenishment
  26. 26. • Context • Predictions • Uncertainty in predictions • Constraints • Costs / benefits • Competing objectives ( trade-offs to make) • Business rules 39 Decisions are based on…
  27. 27. 40 Who performs better? +vs. Star Wars: The Flat Awakens by Filipe de Carvalho vs.
  28. 28. 41 AI + Human perform better +
  29. 29. 42 Human alone performs better: dexterity
  30. 30. 43 AI alone performs better: replenishment
  31. 31. Decisions are faster, cheaper, and better 44 AI alone performs better: replenishment Again, from Lars Trieloff @trieloff (see source) Decision Quality Status Quo Predictive Prescriptive Automation Decisionquality
  32. 32. 1. Descriptive analysis 2. Predictive analysis 3. Prescriptive analysis 4. Automated decisions 45 Beyond prescriptive analysis
  33. 33. • Spam filter → decide to skip inbox • Autonomous Vehicles → decide who to kill 46 Autonomous decision-making systems “Tool AI”vs“High-stakes autonomous AI”
  34. 34. 47 Autonomous Vehicles
  35. 35. • Morality in decision-making algorithm: • Minimize loss of life • Account for probabilities of survival, age of occupants…
 → optimal formula? • Sacrifice owner? • “People are in favor of cars that sacrifice the occupant to save other lives—as long they don’t have to drive one themselves.” 48 Autonomous Vehicles
  36. 36. • Need wide acceptation to get adoption and provide benefit (e.g. save lives with AVs) • “The public is much more likely to go along with a scenario that aligns with their own views” • What will the public tolerate? → experimental ethics • Similar issues whenever AI decides for us and impacts many “Domain-specific/business rules”in decision making 49 High-stakes autonomous AIs
  37. 37. Role of APIs
  38. 38. 51 Communication between AIs 01000101101
  39. 39. Software components for automated decisions: • Create training dataset from historical data (merge sources, aggregate…) • Provide predictive model from given training set (i.e. learn) • Provide prediction against model for given context • Provide optimal decision from given contextual data, predictions, uncertainties, constraints, objectives, costs • Apply given decision 52 Separation of concerns
  40. 40. Software components for automated decisions: • Create training dataset from historical data (merge sources, aggregate…) • Provide predictive model from given training set (i.e. learn) • Provide prediction against model for given context • Provide optimal decision from given contextual data, predictions, uncertainties, constraints, objectives, costs • Apply given decision 53 Operations Research component
  41. 41. Software components for automated decisions: • Create training dataset from historical data (merge sources, aggregate…) • Provide predictive model from given training set (i.e. learn) • Provide prediction against model for given context • Provide optimal decision from given contextual data, predictions, uncertainties, constraints, objectives, costs • Apply given decision 54 Machine Learning components
  42. 42. Software components for automated decisions: • Create training dataset from historical data (merge sources, aggregate…) • Provide predictive model from given training set (i.e. learn) • Provide prediction against model for given context • Provide optimal decision from given contextual data, predictions, uncertainties, constraints, objectives, costs • Apply given decision 55 Predictive APIs
  43. 43. 56 Predictive APIs
  44. 44. The two phases of machine learning: • TRAIN a model • PREDICT with a model 57 Predictive APIs
  45. 45. The two methods of predictive APIs: • TRAIN a model • PREDICT with a model 58 Predictive APIs
  46. 46. The two methods of predictive APIs: • model = create_model(‘training.csv’) • predicted_output = create_prediction(model, new_input) 59 Predictive APIs
  47. 47. Amazon ML BigML Google Prediction PredicSis … 60 Providers of REST http Predictive APIs
  48. 48. Going further
  49. 49. • Define desired and acceptable behaviour
 → objectives and constraints/bounds • Monitor accuracy & bottomline • Self-monitoring & anomaly detection
 → thresholds and fallbacks 62 Ensuring performance of autonomous AI systems
  50. 50. 63 Performance guarantees? “construction worker in orange safety vest is working on road” 95%-accurate scene description
  51. 51. 64 Performance guarantees “black and white dog jumps over bar” 95%-accurate scene description
  52. 52. 65 Performance guarantees “a young boy is holding a baseball bat” 95%-accurate scene description
  53. 53. 66 Performance guarantees “a young boy is holding a baseball bat” weapon SIR, DROP THE WEAPON!
  54. 54. • Lars Trieloff:“Business reasons for automating decisions” • Daniel Kahneman: “Thinking, Fast and Slow” • Tom Dietterich:“Artificial Intelligence Progress” • MIT Technology Review:“Why Self-Driving Cars Must Be Programmed to Kill” • Conference: PAPIs Connect 67 Learn more
  55. 55. • Free ML resources: louisdorard.com • PAPIs updates: @papisdotio
  56. 56. @louisdorard LIKED IT? THEN

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