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What's Next: Cloudy with a chance of AI 2

How can an algorithm learn to be the best Go player in the world with no prior experience? Have chatbots finally become the answer to your business problems? In this webinar, Ogilvy’s Michael McCarthy and Andrew Van Aken will update you on news within the artificial intelligence and machine learning sector, why it is important and how Ogilvy is using some of this technology.

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What's Next: Cloudy with a chance of AI 2

  1. 1. Powered by CLOUDY WITH A CHANCE OF AI
  2. 2. Hello! Andrew Van Aken Senior Consultant, OgilvyOne Worldwide José Arteaga Creative Digital Strategist, OgilvyRED Michael McCarthy Senior Consultant, OgilvyOne Worldwide
  3. 3. What’s the weather like in your city? Tell us where you’re dialing in from!
  4. 4. Want this deck? It will be available for download shortly after the webinar on: slideshare.net/socialogilvy Ogilvy staff: It’s also on The Market! themarket.ogilvy.com Are you on the go? You can join our webinars on mobile, too! Download the GoToWebinar app from the App Store or Google Play
  5. 5. This Talk • A Look at Chatbots • Intro to Reinforcement Learning • Ogilvy Case Study
  6. 6. A Look at Chatbots
  7. 7. We’ll Talk About… • What are Chatbots and why are they so popular? • What are some of the pitfalls? Why do some Chatbots Fail? • How can a business evaluate if a Chatbot is worth the investment?
  8. 8. We’ll Talk About… • What are ChatBots and why are they so popular? • What are some of the pitfalls? Why do some Chatbots fail? • How can a business evaluate if a Chatbot is worth the investment?
  9. 9. We’ll Talk About… • What are ChatBots and why are they so popular? • What are some of the pitfalls? Why do some Chatbots Fail? • How can a business evaluate if a Chatbot is worth the investment?
  10. 10. What are Chatbots? Chatbots are automated interfaces designed to simulate an intelligent conversation with a human user via text
  11. 11. Sephora • Facebook Messenger Platform • Designed for a specific purpose • Conversations are linear
  12. 12. Replika https://www.popsugar.com/news/Replika-Bot-AI-App-Review-Interview-Eugenia-Kuyda-44216396 • Mobile App (iPhone and Android) • An “AI Friend”, no specific purpose • Pushing the limits of NLP and intelligent learning
  13. 13. GymBot • Facebook Messenger • Drive to fitness videos • Conversational, but with limits
  14. 14. GymBot
  15. 15. GymBot
  16. 16. Spectrum of Chatbots ?
  17. 17. Spectrum of Chatbots Chatbot may start conversation, but it can only gather information and will quickly re-route to an agent
  18. 18. Spectrum of Chatbots Chatbot may start conversation, but it can only gather information and will quickly re-route to an agent Chatbot is programmed to handle certain types of task to completion, but relies on agent for more complicated inquiries
  19. 19. Spectrum of Chatbots Chatbot may start conversation, but it can only gather information and will quickly re-route to an agent Chatbot is programmed to handle certain types of task to completion, but relies on agent for more complicated inquiries Chatbot can handle all but the most complicated issues, and can learn from new data and customer interactions
  20. 20. But how do they work?
  21. 21. How do they work? I. Rule Based II. Machine Learning III. Artificial Intelligence
  22. 22. How do they work? I. Rule Based II. Machine Learning III. Artificial Intelligence Input Intent Intent Intent Intent Response Response Response Response
  23. 23. I. Rule Based Hi! How can I help you? You can say (1) Get a Quote (2) Question About Billing or (3) Other Chatty the Generic Auto Insurance Bot
  24. 24. I. Rule Based Hi! How can I help you? You can say (1) Get a Quote (2) Question About Billing or (3) Other Get a quote. Chatty the Generic Auto Insurance Bot
  25. 25. I. Rule Based Hi! How can I help you? You can say (1) Get a Quote (2) Question About Billing or (3) Other Get a quote. Chatty the Generic Auto Insurance Bot Chatbot tries to map user input to a set of pre-defined intents Quote Billing Other
  26. 26. I. Rule Based Hi! How can I help you? You can say (1) Get a Quote (2) Question About Billing or (3) Other Get a quote. Chatty the Generic Auto Insurance Bot Chatbot tries to map user input to a set of pre-defined intents Quote Billing Other
  27. 27. I. Rule Based Hi! How can I help you? You can say (1) Get a Quote (2) Question About Billing or (3) Other Get a quote. Ok great! To start, what is your first and last name… Chatbot tries to map user input to a set of pre-defined intents Quote Billing Other Response Chatty the Generic Auto Insurance Bot
  28. 28. I. Rule Based Hi! How can I help you? You can say (1) Get a Quote (2) Question About Billing or (3) Other I’d like to insure an automobile. Chatty the Generic Auto Insurance Bot
  29. 29. I. Rule Based Hi! How can I help you? You can say (1) Get a Quote (2) Question About Billing or (3) Other I’d like to insure an automobile. Chatbot has to be manually programmed for each intent and response Quote Billing Other Chatty the Generic Auto Insurance Bot
  30. 30. I. Rule Based Hi! How can I help you? You can say (1) Get a Quote (2) Question About Billing or (3) Other I’d like to insure an automobile. I’m sorry, I didn’t quite understand. Would you like to (1) Get a Quote (2) Question About Billing or (3) Other help. Quote Billing Other Error Chatty the Generic Auto Insurance Bot Chatbot has to be manually programmed for each intent and response
  31. 31. II. Machine Learning Hi! How can I help you today? Chatty the Generic Auto Insurance Bot
  32. 32. II. Machine Learning Hi! How can I help you today? Chatty the Generic Auto Insurance Bot Chatbot can now accept a free-form response
  33. 33. II. Machine Learning Hi! How can I help you today? Chatty the Generic Auto Insurance Bot I’d like to insure an automobile. Chatbot can now accept a free-form response
  34. 34. II. Machine Learning Hi! How can I help you today? Chatty the Generic Auto Insurance Bot I’d like to insure an automobile. Chatbot tries to map user input to a set of pre-defined intents Chatbot can now accept a free-form response
  35. 35. II. Machine Learning Hi! How can I help you today? Chatty the Generic Auto Insurance Bot Chatbot tries to map user input to a set of pre-defined intents “like to, automobile, insure ” Probability I’d like to insure an automobile. Chatbot can now accept a free-form response
  36. 36. II. Machine Learning Hi! How can I help you today? Chatty the Generic Auto Insurance Bot Chatbot tries to map user input to a set of pre-defined intents “like to, automobile, insure ” Probability Quote Billing Other I’d like to insure an automobile. Chatbot can now accept a free-form response
  37. 37. II. Machine Learning Hi! How can I help you today? Chatty the Generic Auto Insurance Bot Chatbot tries to map user input to a set of pre-defined intents “like to, automobile, insure ” Probability Quote Billing Other I’d like to insure an automobile. Response Ok, I can help you with that. To start, what is your first and last name… Chatbot can now accept a free-form response
  38. 38. III. Artificial Intelligence Hi! How can I help you today? Chatty the Generic Auto Insurance Bot
  39. 39. III. Artificial Intelligence Hi! How can I help you today? Chatty the Generic Auto Insurance Bot I’d like to know more about your automobile insurance.
  40. 40. III. Artificial Intelligence Hi! How can I help you today? Chatty the Generic Auto Insurance Bot I’d like to know more about your automobile insurance. Natural Language Processing
  41. 41. III. Artificial Intelligence Hi! How can I help you today? Chatty the Generic Auto Insurance Bot I’d like to know more about your automobile insurance. Natural Language Processing “like to, know more, automobile”
  42. 42. III. Artificial Intelligence Hi! How can I help you today? Chatty the Generic Auto Insurance Bot I’d like to know more about your automobile insurance. Natural Language Processing “like to, know more, automobile” Probability
  43. 43. III. Artificial Intelligence Hi! How can I help you today? Chatty the Generic Auto Insurance Bot I’d like to know more about your automobile insurance. Natural Language Processing “like to, know more, automobile” Probability Certainly. We offer insurance for both commercial vehicles and privately owned… … Was this information helpful? Response
  44. 44. III. Artificial Intelligence Hi! How can I help you today? Chatty the Generic Auto Insurance Bot I’d like to know more about your automobile insurance. Natural Language Processing “like to, know more, automobile” Probability Certainly. We offer insurance for both commercial vehicles and privately owned… … Was this information helpful? Response Yes! Thank you.
  45. 45. III. Artificial Intelligence Hi! How can I help you today? Chatty the Generic Auto Insurance Bot I’d like to know more about my auto insurance bill. Natural Language Processing “like to, know more, automobile” Certainly. We offer insurance for both commercial vehicles and privately owned… … Was this information helpful? Response No, I’m looking for something else. Probability
  46. 46. III. Artificial Intelligence Hi! How can I help you today? Chatty the Generic Auto Insurance Bot I’d like to know more about my auto insurance bill. Natural Language Processing “like to, know more, automobile” Certainly. We offer insurance for both commercial vehicles and privately owned… … Was this information helpful? Response No, I’m looking for something else. Probability
  47. 47. Rule Based Machine Learning Artificial Intelligence Powering a Chatbot • Doesn’t try and do too much • Proven technology / interface • Easier analytics • Not conversational • Not likely to impress customers • Still requires careful programming to handle mapping of intents • Analytics are more complicated • Not accessible to the vast majority of companies • Newer technology, which has its own pitfalls • Can be conversational and engaging • Technology has advanced considerably and is now more readily available • Allows for innovation • Can drastically improve customer service costs • Potential to be leader in industry
  48. 48. So… why are Chatbots so popular?
  49. 49. The Rise of the Chatbot Messaging Apps are Outpacing Social Networking Apps https://learn.octaneai.com/the-complete-beginners-guide-to-creating-a-bot-on-facebook-messenger-1b557b971fb9
  50. 50. The Results Are Promising 100,000 bots within first year of launch 80% of customers re-used Chatbot within 2-weeks of launch 266% increase in conversion rate versus average social ad https://www.smartmessage.com.tr/impressive-numbers-stats-chatbot-dominance-far/
  51. 51. Why Do Some Chatbots Fail? :(
  52. 52. Some potential causes for concern… 2016 2017March 2016 Microsoft’s Tay Chatbot on Twitter goes on spamming rampage. Begins to spout racist, genocidal and misogynistic messages to users.
  53. 53. Some potential causes for concern… 2016 2017March 2016 July 2017 Microsoft’s Tay Chatbot on Twitter goes on spamming rampage. Begins to spout racist, genocidal and misogynistic messages to users. Facebook bots start talking to each other and forming their own language.
  54. 54. Some potential causes for concern… 2016 2017March 2016 July 2017 August 2017 Microsoft’s Tay Chatbot on Twitter goes on spamming rampage. Begins to spout racist, genocidal and misogynistic messages to users. Facebook bots start talking to each other and forming their own language. Two chatbots pulled from Chinese messaging app (QQ) after questioning the rule of the Communist Party
  55. 55. • Rule based logic is insufficient to meet customer inquiries • Chatbot doesn’t work with existing business systems • Weak use case What Else Can Go Wrong?
  56. 56. Companies pull back • Facebook shrunk its AI efforts after its bot API fail rate hit 70% • Everlane, announced that it’s removing several features of its Messenger bot and returning to email
  57. 57. Is a Chatbot Right for Your Business?
  58. 58. Determining if a Chatbot is Right for Your Business 1. Sooner you deploy, sooner you can iterate and learn 2. Chatbots are seeing record engagement rates 3. Customer service bots can alleviate other costs and have a direct ROI
  59. 59. Deploying a Chatbot Analyze Current Data 1
  60. 60. 2 Deploying a Chatbot Analyze Current Data Have a Clear Purpose 1
  61. 61. 2 Deploying a Chatbot Analyze Current Data Have a Clear Purpose Define Key Metrics 1 3
  62. 62. 2 Deploying a Chatbot Analyze Current Data Have a Clear Purpose Define Key Metrics Test and Optimize 1 3 4
  63. 63. Intro to Reinforcement Learning
  64. 64. First, let’s review last webinar
  65. 65. To Review
  66. 66. To Review
  67. 67. To Review New Example: David Ogilvy
  68. 68. How can we learn without data?
  69. 69. Enter Reinforcement Learning • An “educated” guess and check, with rewards • No starting data needed, just the rules of the road…
  70. 70. Every Time You See Breakthrough • Book published in 1998…
  71. 71. What is Reinforcement Learning? Agent
  72. 72. What is Reinforcement Learning? Agent Takes an Action
  73. 73. What is Reinforcement Learning? Agent Takes an Action Environment
  74. 74. What is Reinforcement Learning? Agent Takes an Action Environment Observation/Reward +1
  75. 75. What is Reinforcement Learning? Agent Takes an Action Environment
  76. 76. What is Reinforcement Learning? Agent Takes an Action Environment Observation/Reward Observation/Reward
  77. 77. What is Reinforcement Learning? Agent Takes an Action Environment Observation/Reward -100
  78. 78. What is Reinforcement Learning? Agent Takes an Action Environment Observation/Reward
  79. 79. What is Reinforcement Learning? Agent Takes an Action Environment
  80. 80. Video Games!
  81. 81. AlphaGo Zero
  82. 82. Getting to Greatness
  83. 83. Getting to Greatness VS
  84. 84. Getting to Greatness VS
  85. 85. Data/Accuracy
  86. 86. Data/Accuracy
  87. 87. Getting More Efficient
  88. 88. Will This Method Take Over? • If we just knew how the stock market works… • Or how proteins folded…
  89. 89. Yann LeCun • “If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we don’t know how to make the cake.”
  90. 90. Look a Like Modeling
  91. 91. What is it? • Building larger segments from smaller ones
  92. 92. Ogilvy Use Case - Everywhere • B2B/B2C • Across all mediums
  93. 93. Pretzel Company • How would a pretzel company increase sales?
  94. 94. Pretzel Company Company City Employees Alcohol Pretzels Pete’s Bar 1 45 1 12 Cinema 8 0 23 0 4 Cues and Shoes 0 140 1 1 Popcorn Palace 1 7 0 1
  95. 95. Collect New Data Company City Employees Alcohol Lbs of Pretzels Disco Dave’s 0 29 0 ? Millenium Movies 0 3 0 ? Gutter Bowl 1 56 1 ? Chips and Dips 1 40 0 ?
  96. 96. Collect New Data Company City Employees Alcohol Lbs of Pretzels Disco Dave’s 0 29 0 24 Millenium Movies 0 3 0 3 Gutter Bowl 1 56 1 13 Chips and Dips 1 40 0 4
  97. 97. What’s Important? Variable City City -0.123 Employees 0.3456 Alcohol 0.982
  98. 98. What are the Results? • Often can see 2-3x increase in performance over regular targeting, according to Exelate
  99. 99. Applying It • Need to aggregate and plan your data • Need competitive data • Need to execute
  100. 100. Want to Stay Present? • We write weekly on machine learning, artificial intelligence, cloud computing and other technology • Cloudy with a Chance of AI - Subscribe today!
  101. 101. Questions? Andrew Van Aken Senior Consultant, OgilvyOne Worldwide José Arteaga Creative Digital Strategist, OgilvyRED Michael McCarthy Senior Consultant, OgilvyOne Worldwide

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  • melodygaga

    Nov. 23, 2017
  • lautierc

    Dec. 13, 2017
  • JoshuaMedler

    Feb. 3, 2020

How can an algorithm learn to be the best Go player in the world with no prior experience? Have chatbots finally become the answer to your business problems? In this webinar, Ogilvy’s Michael McCarthy and Andrew Van Aken will update you on news within the artificial intelligence and machine learning sector, why it is important and how Ogilvy is using some of this technology.

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