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TopConf Linz, 02/02/2016


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The friction zone between probabilities, machine learning and user experience in the consumer Internet of Things -- talk at TopConfAT 2016

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TopConf Linz, 02/02/2016

  1. 1. the friction zone between probability, machine learning and user experience in the consumer IoT @BorisAdryan
  2. 2. MAKEing the Future
  3. 3. evolution-of-business-intelligence excerpt from imagine!
  4. 4. 2016: a little reality check aka
  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 [ðə ˈfjuːʧə]
  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. who likes more detail? aka
  16. 16. p-value < 2.2e-16 confidence interval FDR posterior probability 95% 0.025 5% explain this to your neighbour Battery is goingto die today, p < 2.7x10-3
  17. 17. simple x -> y mapping x k h l q w g f -> y mapping your FitBit temperature your friend’s dog the car computationally, your life is incredibly messy
  18. 18. blog post at
  19. 19. 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
  20. 20. the hypothesis itself is a mathematical model explain this to your user
  21. 21. 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
  22. 22. from
  23. 23. the issue with missing data given all features, we can discover the causality between them
  24. 24. 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
  25. 25.
  26. 26. it’s none of your effing business
  27. 27. things getting more creepy… “Is there something you should tell me, Boris? I thought your wife was travelling…” …when they’re conversational
  28. 28. 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