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Artificial Intelligence: How Enterprises Can Crush It With Apache Spark: Keynote by Mike Gualtieri

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Artificial intelligence (AI) is not new. It emerged as a computer science discipline in the 50’s and has been a persistent theme in science fiction. What is new is that enterprises now have the prerequisites needed to create pragmatic AI applications: plenty of data, deep learning frameworks, and blazing fast distributed compute clusters à la Apache Spark. Forrester Vice President and Principal Analyst, Mike Gualtieri will enumerate and demystify nine essential AI technology building blocks that enterprises can use to add a modicum of intelligence to existing and new applications.

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Artificial Intelligence: How Enterprises Can Crush It With Apache Spark: Keynote by Mike Gualtieri

  1. 1. Artificial Intelligence: How Enterprises Can Crush It With Apache Spark Mike Gualtieri, VP & Principal Analyst February 9, 2017 Twitter: @mgualtieri
  2. 2. #Priority
  3. 3. © 2015 Forrester Research, Inc. Reproduction Prohibited 3 52% 53% 53% 54% 58% 64% 64% 65% 66% 73% 75% 0% 10% 20% 30% 40% 50% 60% 70% 80% Better leverage big data and analytics in business decision-making Create a comprehensive strategy for addressing digital technologies like mobile, social & smart products Create a comprehensive digital marketing strategy Better comply with regulations and requirements Improve differentiation in the market Increase influence and brand reach in the market Address rising customer expectations Improve our ability to innovate Reduce costs Improve our products /services Improve the experience of our customers Customer experience and product innovation are top priorities. › Base: 3,005 global data and analytics decision-makers › Source: Global Business Technographics Data And Analytics Online Survey
  4. 4. For you For all For segments For you Demographic Relationships Hyper-Personal, Digital Relationships Personal Relationships Mass Relationships CustomerExperience 1800 1900 1950 2000 2015
  5. 5. #Friends
  6. 6. Customers want and increasingly expect you to know them as well as friends know them.
  7. 7. • Learn individual customer characteristics and behaviors • Predict customer needs and desires in real-time • Adapt applications to serve individual customers Hyper-personal customer experiences must:
  8. 8. ~150
  9. 9. NUMBER DUNBAR’SThe cognitive limit to the number of people with whom one can maintain stable social relationships (friends).
  10. 10. “We can only ever have 150 friends at most.” Robin Dunbar - University of Oxford, Head of the Social and Evolutionary Neuroscience Research
  11. 11. • Our relationships form a hierarchically inclusive series of circles of increasing size but decreasing intensity. • 150 is the limit on personalized, reciprocated relationships. • 1,500 = limit on memory for faces. 5 15 50 150 Dunbar 500 1500 Who are the 5 people you care about the most?
  12. 12. #Customers
  13. 13. 25 million investors How many customers does your organization have? 6 million active customers 390 million visitors/month 1.8 vehicles per year 320 million citizens
  14. 14. #Quiz
  15. 15. © 2015 Forrester Research, Inc. Reproduction Prohibited 15 How well do you know this consumer? › Male › 35 years old › Single › Resides in New York City › Makes $100,000 per year What do you predict he would do if the bank accidently transferred $5,000 into his bank account? A. Give the money back B. Take the money and run Spark Summit Quiz
  16. 16. George Costanza
  17. 17. #AI
  18. 18. © 2016 Forrester Research, Inc. Reproduction Prohibited 18 Enterprises that plan to invest in AI expect customer experience and business model benefits
  19. 19. © 2016 Forrester Research, Inc. Reproduction Prohibited 19 Artificial intelligence (AI) interest is high; adoption is nascent Opportunity
  20. 20. AI is now on the syllabus at top-tier business schools such as Harvard, Insead, MIT, Northwestern, Stanford
  21. 21. #PureAI
  22. 22. The purest benchmark for AI is technology that strives to mimic human intelligence…
  23. 23. “ . . . within a generation, the problem of creating ‘artificial intelligence’ will substantially be solved.” (1967) Marvin Minsky (1927-2016) Co-founder of MIT’s AI Laboratory in 1959
  24. 24. #PragmaticAI
  25. 25. © 2016 Forrester Research, Inc. Reproduction Prohibited 26 Enterprises can use AI building blocks today to add a modicum of intelligence to apps Pragmatic AI Nope! Yep!
  26. 26. #BuildingBlocks
  27. 27. Pragmatic 1 Pragmatic 2 Pragmatic 3Research Use one or more of these AI building block technologies to build a modicum of intelligence in your apps. Pure ? None yet Good Enough Pretty Good Creepy Good
  28. 28. #ML
  29. 29. Machine learning is used by data scientists to train probabilistic predictive models.
  30. 30. A recommendation engine is a finely tuned prediction about what you might enjoy watching.
  31. 31. Leading financial services firm uses machine learning to predict clients’ portfolio actions.
  32. 32. InteractiveTel AI listens in real-time to 200,000 minutes of car buyer conversation per month at a large dealership.
  33. 33. © 2016 Forrester Research, Inc. Reproduction Prohibited 34 Machine learning augments programmed logic with learned logic to create AI apps Streaming data Application interface App logic Appli- cations Context Actions Real-time context Programmed logic Learned logicMachine learning Learning External actions External context From other data sources of applications To other data sources or applications
  34. 34. #GoDeep
  35. 35. Deep learning requires “stupidly powerful” compute processing for more accurate models.
  36. 36. © 2016 Forrester Research, Inc. Reproduction Prohibited 38 Deep learning open source libraries are available now ›Caffé ›Deeplearningforj ›MxNet ›OpenAI ›Theano ›Tensorflow ›Torch
  37. 37. Property insurers can use DL to automatically assess damage and repair costs.
  38. 38. #
  39. 39. #Models
  40. 40. 10 characteristics + 10 behaviors + 10 needs = 30 AI models per customer 1 million customers x 30 models = 30 million AI models AI Models
  41. 41. #Data
  42. 42. All data originates in real-time…
  43. 43. 110010011011001 010010011011001 010011001101101 010010011011001 CustomerData Transactions Everything IoT …but, traditional analytics to gain insights and build models is usually done much, much later.
  44. 44. Millions of AI models… …updated continuously with real-time data!
  45. 45. Great data pipelines are a pre-requisite to pragmatic AI.
  46. 46. © 2016 Forrester Research, Inc. Reproduction Prohibited 52 Enterprises can use AI building blocks today to add a modicum of intelligence to apps Pragmatic AI Nope! Yep! Prerequisite
  47. 47. #
  48. 48. Spark is designed for speed.
  49. 49. Spark is designed to scale.
  50. 50. Spark is designed for distributed machine learning.
  51. 51. Spark does deep learning.
  52. 52. Spark has a vibrant ecosystem.
  53. 53. #Pragmatic
  54. 54. © 2016 Forrester Research, Inc. Reproduction Prohibited 60 Pragmatic use cases can work today. Pragmatic AI Nope! Yep! Prerequisite Make it converse Make it predict Make it see Make it discover Make it move
  55. 55. #DoYourJob
  56. 56. Do your job…
  57. 57. Do your job…with Apache Spark!
  58. 58. forrester.com Thank you Mike Gualtieri mgualtieri@forrester.com Twitter: @mgualtieri

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