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Smart IoT London, 13th April 2016


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My talk at Smart IoT London. About adding 'context' for data analytics in the consumer IoT, touching on machine learning, hidden variables, and UX/UI of communicating probabilities.

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Smart IoT London, 13th April 2016

  1. 1. How ‘smart’ is the IoT? Do we want it any smarter? (Be careful what you wish for…) @BorisAdryan
  2. 2. industry is easy Internet replaces wire It’s all about the context M2M consumer IoT defined I-P-O like it’s 1975 context context context Is this hot?the consumer is not
  3. 3. evolution-of-business-intelligence excerpt from imagine!
  4. 4. 2016: a little reality check
  5. 5. modified, image from health management air conditioning smart heating communications security entertainment lighting controlweather monitoring room occupancy
  6. 6. health management air conditioning smart heating communications security entertainment lighting controlweather monitoring room occupancy individual apps are NOT the future
  7. 7. source lost, seen on Twitter we want intelligent things that talk
  8. 8. David Rose: enchanted objects as UX paradigm modified, image from
  9. 9. a solid scientific foundation no magic involved enchantment has
  10. 10. time sleep monitor schedule location awareness building control mobilitycapacity weather prioritising planning provisioning
  11. 11. acquiring knowledge == learning machine learning creative thinking != decision making with statistics + algorithms ==
  12. 12. raw data information knowledge actionable insight action reaction barometric pressure, temperature, coordinates, schedule, … snow storm coming, airport hotel, need to travel flying and snow don’t go together rebook flight “context” structure rules “conversational” dynamic acquired
  13. 13. there’s no absolute truth out there data ✓ hard facts ✓ intuitive probability ✓ likelihood of some hypothesis being true given the data
  14. 14. 30 40 50 60 70 average speed at this point [MPH] time to target [min] 10 20 30 40 50 we have a sense for simple probabilities
  15. 15. blog post at
  16. 16. data temperature wind speed wind direction precipitation air pressure airport code airline aircraft fully booked? avg delays cancellations serve booze? black box training flights cancelled in the past classifier ranked list of relevant features weight of features thresholds for features performance metric new data prediction
  17. 17. the hypothesis itself is a mathematical model explain this to your user
  18. 18. good decisions are based on experience machine learning is an iterative process training classifier performance assessment good enough? get on with life moredatafortraining data no yes
  19. 19. from
  20. 20. the issue with missing data given all features, we can discover the causality between them
  21. 21. self-learning systems will have to seek ‘missing’ data other than saying ‘urgent meeting’ in the calendar, how can the system know it’s really urgent? …preemptively
  22. 22. most of us will want to be ‘spied on’ for the sake of convenience but as consumers we need to be in the know
  23. 23.
  24. 24. it’s none of your effing business
  25. 25. things getting more creepy… “Is there something you should tell me, Boris? I thought your wife was travelling…” …when they’re conversational
  26. 26. life is becoming increasingly dependent on probabilities and abstract quantities @BorisAdryan adding to our anxiety of uncertainty, the conversational IoT may potentially feel repetitive, disruptive and intrusive! quantitative and computational thinking is going to become an essential skill