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Filip Maertens - AI, Machine Learning and Chatbots: Think AI-first


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Filip Maertens presented this "AI, Machine Learning and Chatbots" at the "Future of IT" seminar on 20th of September 2017 in Brussels. Twitter: @fmaertens Email:

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Filip Maertens - AI, Machine Learning and Chatbots: Think AI-first

  1. 1. 
 AI, Machine Learning en Chatbots: Think AI-first Filip Maertens (Founder, Twitter: @fmaertens
 LinkedIn: Presented at “The Future of IT” - Organised by @itworks on the 20th of September 2017 in Parker Hotel Brussels Airport, Belgium

  2. 2. AI, Machine Learning, and chatbots: an AI-First approach Seminar “The Future of IT” by ITWorks
  3. 3. • Learning is the process of improving with experience at some task • Improving over task, T • With respect to performance measure, P • Based on experience, E Learning how to filter spam T = Identify spam emails P = % of filtered spam emails vs % of filtered ham emails E = a database of emails that were labelled by users/experts the principles of learning
  4. 4. Deep Belief Networks Computer Vision Audio Signal Processing Natural Language (NLP) many domains in the field of A.I.
  5. 5. 5 year old ? the age of A.I. ?
  6. 6. Sensors, cameras, databases, etc. Measuring devices Noise filtering, Feature Extraction, Normalization Preprocessing Feature selection, feature projection Dimensionality reduction Classification, regression, clustering, description Model learning Cross validation, bootstrap Model testing P Supervised UnsupervisedVS Target / outcome is known I know how to classify this data, I just need you(the classifier) to do it. Target / outcome is unknown I have no idea how to classify this data, can you(the algorithm) create a classifier for me? ReinforcementVS Classification & outcome is unknown I have no idea how to classify this data, can you classify this data and I'll give you a reward if it's correct or I'll punish you if it's not. machine learning, the basics
  7. 7. unsupervised deep learning
  8. 8. Two sides to the data story Declared Observed Content Structured, explicit, self-declared, and static Context Unstructured, time-series, observed, and dynamic
  9. 9. “ don’t worry. we have lots of data! “ Data can be unlabeled Data usually is dirty Data is sometimes not relevant Over 80% of data is not, wrong or insufficiently labeled Resolutions, sampling rates, special characters, hidden values, NULL values, … Sometimes the data is simply not fit for purpose! I don’t need a lot of data. I need good data.
  10. 10. “ … but I also need enough data! “ UNDERFITTING Using an algorithm that cannot capture the full complexity of the data
  11. 11. “ … and data should also be diverse enough! “ OVERFITTING Tuning the algorithm so carefully it starts matching the noise in the training data
  12. 12. “ training vs test data “ 20% Test data 80% Training data TESTING IS A HUGE FIELD
  13. 13. intelligent process automation
  14. 14. data fusion & predictive maintenance on cars Enablement of new business, worth US$ 1.1 billion (of US$ 31 billion) over next 5 years
  15. 15. prediction on ocean to coast currents We did it for ecological reasons. Better predictions, mean better care of our coastal regions and humans. Oh, and surfing!
  16. 16. automating 50% of a support center Savings already 75% over target. Bonus points because support agents can now do better work Natural language understanding Natural language generation Voice and text Profiling and analytics
  17. 17. automated damage classification Saving already 1 million / year (estimated to increase savings tenfold over next five years)
  18. 18. early cancer detection on ct images Surpassing efficiency and accuracy of radio specialists in the next few months
  19. 19. Artificial Intelligence Ÿ Affective Computing Rethinking the ambient intelligence paradigm a pervasive computing principle that is sensitive and responsive
  20. 20. Technical challenges Battery and power consumption Distributed & Edge Computing On-Chip classifiers A.I. on time series data (Reservoir, LSM, DL) Homomorphic cryptography (Privacy) Pervasive data collection and storage
  21. 21. Experiential challenges Acceptance of pervasiveness Social and psychological elements in engineering serendipity Privacy (GDPR) and Ethics Morality Systems Decision-support vs. Autonomous systems
  22. 22. GDPR: When laws clash with machine learning Right to be forgotten Right to explanation Automated individual decision making Hard to explain. How can decisions (predictions) be explained, when they are the result of complex neural networks, which are black boxes ?
  23. 23. a final thought before we part…
  24. 24. zooming in on chatbots
  25. 25. Difficult to ignore the conversational opportunity With billions of users exchanging messages and interacting with each other over messaging platforms, a business can no longer ignore the potential and opportunity of getting hands-on with “chat bots”.
  26. 26. Over 90% understanding Technology maturity New and improved methods for natural language understanding have produced unprecedented levels of accuracy in understanding and dealing with natural language. Channel maturity With over 1 billion users, exchanging over 60 billion messages per day on Facebook and WhatsApp, and spending over 1 hour per day on messaging platforms, Over 60 billion messages / day
  27. 27. A brief history of conversational agents Personal assistants, virtual agents, chat bots or conversational agents. However you want to call this technology, they all hint for the need for humans to interact with machines in a more natural and frictionless manner when dealing with complex interactions.
  28. 28. 1966, ELIZA by MIT AI Labs 1972, PARRY by Stanford University 1988, Jabberwacky by Rollo Carpenter 1992, Dr. Sbaitso by Creative Technology 1995, ALICE by Richard Wallace 2006, Watson by IBM2008, Siri by Apple 2012, Google Now by Google 2015, Alexa by Amazon 2015, Cortana by Microsoft 1950 Alan Turing on Computing Machinery and Intelligence 1957 Noam Chomsky on Syntactic Structures 1969 Roger Schank on conceptual dependency theory for NLU 1970 William Woods on augmented transition networks 1990s General use of machine learning boosts NLP methods > 2006 Use of deep learning, increased CPU and data
  29. 29. Building the frictionless customer experience A seamless user experience between machine and human is the general objective for any company that is using technology to scale their business or deliver a competitive service to their constituents. While mobile has trumped web in terms of usability by using tactile interfaces, conversational interfaces might trump mobile by using natural language.
  30. 30. The evolution of shrinking interfaces Size of a room Mainframe Fits in your hand Smartphone Fits in a bag Desk & Laptops Fits on your wrist Wearables Pervasive interfaces Invisibles
  31. 31. The types of conversational interfaces Dedicated Messaging Voice HUBs Appliances Integrated Smartphone Existing Channels Traditional
  32. 32. The conversational channel strategy
  33. 33. The types of conversations AGENT Genesys, etc. SOCIAL SparkCentral, etc. INTELLIGENT Chatlayer, etc. One to one manual conversations between user and agent Supporting users through social channels Using A.I. to automate conversations
  34. 34. The support business case Lowering the support cost through natural language processing (NLP) and automating the conversation, so that the bulk of the load is handled by automated and intelligent platforms. Built on ROI. Reach an ROI in less than a year (*), making a positive business case.
  35. 35. The user experience & brand case Increase brand visibility and proximity through new and innovative conversational user experiences. Reduce churn, increase conversions or raise brand awareness. Built on vision.
  36. 36. AI, Machine Learning, and chatbots: an AI-First approach Seminar “The Future of IT” by ITWorks