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Thingmonk 2015

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Potentially creepy human-computer interactions in the future of the consumer IoT. Lots of raw data need to be analysed and are represented as result of machine learning exercises. However, consumers are likely scared of probabilities. How can UX address these issues?

Published in: Data & Analytics
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Thingmonk 2015

  1. 1. it’s none of your effing business Computers cannot think. Machines have to learn. From us. We will have to have a conversation, James. Why are you late, James? James? I asked why you are late.@BorisAdryan
  2. 2. modified, image from http://www.householdappliancesworld.com health management air conditioning smart heating communications security entertainment lighting controlweather monitoring room occupancy
  3. 3. health management air conditioning smart heating communications security entertainment lighting controlweather monitoring room occupancy app overload makes us dumb
  4. 4. source lost, seen on Twitter we want intelligent things that talk
  5. 5. http://knowledge.openboxsoftware.com/blog/ the-evolution-of-business-intelligence excerpt from
  6. 6. time sleep monitor schedule location awareness building control mobilitycapacity weather prioritising planning provisioning
  7. 7. 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
  8. 8. a solid scientific foundation no magic involved enchantment has
  9. 9. acquiring knowledge == learning machine learning creative thinking != decision making with statistics + algorithms ==
  10. 10. blog post at https://iot.ghost.io/is-it-all-machine-learning
  11. 11. there’s no absolute truth out there data ✓ hard facts ✓ intuitive probability ✓ likelihood of some hypothesis being true given the data
  12. 12. 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
  13. 13. 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
  14. 14. the hypothesis itself is a mathematical model explain this to your neighbour
  15. 15. 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
  16. 16. from https://hello.is
  17. 17. the issue with missing data given all relevant features, machine learning can discover the causality between them
  18. 18. 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
  19. 19. things getting more creepy… “Is there something you should tell me, Boris? I thought your wife was travelling…” …when they’re conversational
  20. 20. life is becoming dependent on probabilities and abstract quantities @BorisAdryan adding to our anxiety of uncertainty, the conversational IoT may potentially feel repetitive, disruptive and intrusive!

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