Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Do Androids Play Games? Where Does Gamification Fit in a World of Robots and AI, Dr. Michael Wu

125 views

Published on

Artificial Intelligence (AI) is emerging from the digital ether into our physical world. They are already providing solutions to business through a multitude of smart objects, devices, and machines enabled by the Internet of things (IoT). It is an inevitable future that robots and AI will replace a large part of our current workforce, and this foreseeable future will come sooner than we think.

What role will gamification play in such a future? Clearly, robots won’t need gamification to perform; they don’t even need to be motivated. Will gamification still be relevant in such society? A deeper look into how AI and IoT work will reveal that gamification is still very much needed in such a world. Moreover, it plays a crucial role to ensure the wellbeing of mankind. Come find out how we can co-create a fully automated world where human and machine will peacefully coexist.

Published in: Leadership & Management
  • Get Paid To Manage Facebook Fan Pages! Facebook Fan Page Workers Required - Start Immediately. ★★★ https://tinyurl.com/rbrfd6j
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

Do Androids Play Games? Where Does Gamification Fit in a World of Robots and AI, Dr. Michael Wu

  1. 1. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Do Androids Play Games? Where Does Gamification Fit in a World of Robots + AI? Michael Wu, PhD (@mich8elwu) chief AI strategist @ PROS 2018.11.27
  2. 2. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Michael Wu, PhD (@mich8elwu) chief AI strategist @ PROS 2018.11.27 @mich8elwu
  3. 3. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. The Concept of AI isn’t New supersmart fast ~light speed remember every detail work 24/7 always learning get smarter everyday never get tired never complaint … etc…
  4. 4. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. A Very Brief Evolution of Business Analytics (Business Intelligence)
  5. 5. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. An Example R Rresistance buy sell 50% sell 50% buy descriptive predictive prescriptive R R R sell
  6. 6. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. temporal predictive analytics  trend line historical data x Predictive Analytics—Estimate future datafuture prediction present 𝑓 𝑥 model
  7. 7. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Predictive Analytics—Estimate 𝑓 𝑥 model text data x = tweet, blog, comment, news articles, sms… sentiment classifier 𝑓 𝑥 (+) positive (0) neutral (−) negative social media interactivity x = retweet, like, reply, share, +1… influence algorithm 𝑓 𝑥 influence score data that you have data that you don’t have temporal predictive analytics  trend line general predictive analytics aren’t limited to time domain  social media examples • influencer scoring • sentiment analytics  other examples? future predictionhistorical data x
  8. 8. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Predictive Analytics—Estimate R Rresistance descriptive simplest examples of:  predictive analytics: trend line general predictive analytics  not limited to forecasts in temporal domain  sentiment + influence
  9. 9. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Prescriptive Analytics—Optimize GPS navigation systems simplest examples of:  predictive analytics: trend line  prescriptive analytics: GPS general predictive analytics  not limited to forecasts in temporal domain  sentiment + influence general prescriptive analytics  not limited to prescription of routes in geo-spatial domain  can prescribe biz strategies
  10. 10. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Prescriptive Analytics—Optimize GPS navigation systems simplest examples of:  predictive analytics: trend line  prescriptive analytics: GPS general prescriptive analytics  not limited to prescription of routes in geo-spatial domain  can prescribe biz strategies do you know other examples? simplest examples of:  predictive analytics: trend line  prescriptive analytics: GPS general predictive analytics  not limited to forecasts in temporal domain  sentiment + influence general prescriptive analytics  not limited to prescription of routes in geo-spatial domain  can prescribe biz strategies
  11. 11. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary.
  12. 12. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. business intelligence (BI): passive decision support human still makes the decision because traditional analytics have limited accuracy
  13. 13. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Analytics Accuracy 𝑓 𝑥linear model complexity logistic 2nd order 2-3 layer neural net deep neural net (~10s layers) analytics results accuracy of model qualitydata quality CPU cycles bytes kb Mb Gb Tb Pb Zb Eb
  14. 14. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. 1950’s Vacuum Tube Computers 𝑓 𝑥linear model complexity logistic 2nd order 2-3 layer neural net deep neural net (~10s layers) we already have a theoretical understanding of simple neural networks (2-3 layers deep) we are limited to simplistic models b/c of computing power most data (even if available) are not digitized (inaccessible) bytes kb Mb Gb Tb Pb Zb Eb
  15. 15. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. 1950’s Transistor Computers 𝑓 𝑥linear model complexity logistic 2nd order 2-3 layer neural net deep neural net (~10s layers) in the next few decades, our theoretical understanding will improve linearly, but our data storage and computing power will increase exponentially bytes kb Mb Tb Pb Zb Eb Gb
  16. 16. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. 1970’s IC + Mainframes bytes kb Mb Gb Tb Pb Zb Eb 𝑓 𝑥linear model complexity logistic 2nd order 2-3 layer neural net deep neural net (~10s layers) in the next few decades, our theoretical understanding will improve linearly, but our data storage and computing power will increase exponentially
  17. 17. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. 1980’s Microprocessor + PC 𝑓 𝑥linear model complexity logistic 2nd order 2-3 layer neural net deep neural net (~10s layers) we almost have enough CPU power to use the most advanced neural network models then bytes kb Mb Gb Tb Pb Zb Eb
  18. 18. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. 1990’s Client-Server + Internet 𝑓 𝑥linear model complexity logistic 2nd order 2-3 layer neural net deep neural net (~10s layers) although we can use the most advanced model, we still can’t use it with all the available data due to practical limitations bytes kb Mb Gb Tb Pb Zb Eb
  19. 19. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. 2000’s Distributed Computing 𝑓 𝑥linear model complexity logistic 2nd order 2-3 layer neural net deep neural net (~10s layers) we (in theory) have infinite CPU power, so this constraint becomes an economic limit, and we are now only limited by data volume bytes kb Mb Gb Tb Pb Zb Eb
  20. 20. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. 2010’s Big Data + GPU 𝑓 𝑥linear model complexity logistic 2nd order 2-3 layer neural net deep neural net (~10s layers) we can use the most advanced model and not worry too much about data or cpu constraints bytes kb Mb Gb Tb Pb Zb Eb
  21. 21. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. what happens when humans are always in agreement with machines predictions? do humans still need to have the final say?
  22. 22. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. artificial intelligence (AI): automation of decision + proper execution of all subsequent actions decision action data
  23. 23. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. decision action automated loan origination algorithmic trading
  24. 24. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary.
  25. 25. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. What Makes AI Intelligent? deep learning machine learning AI
  26. 26. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. What Makes AI Intelligent? deep learning machine learning AI V8 engine car
  27. 27. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. big data machine learning model regression, classification, clustering feedforward neural networks, recurrent neural network, reinforcement learning, deep learning, LSTM, generative adversarial learning, convolutional neural networks, boosting, bagging, random forest, decision trees, gradient boosted decision trees, adaBoost, Kalman filter, latent Dirchlet allocation, Dirichlet process, latent semantics analysis, principle component analysis, linear discriminant analysis, k-means algorithm, spherical k-means, agglomerative hierarchical clustering, Gaussian mixture model, Gaussian process, collaborative filtering, etc. … What Makes AI Intelligent?
  28. 28. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. outcome big data feedback machine learning analytics results actions model decisions survey What Makes AI Intelligent?
  29. 29. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. outcome big data feedback machine learning analytics results actions model decisions survey real-time automatically tracked semi-automated What Makes AI Intelligent?
  30. 30. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. model outcome big data feedback machine learning analytics results actions model decisions What Makes AI Intelligent?
  31. 31. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. model outcome big data feedback machine learning analytics results actions model decisions LEARNING LOOP What Makes AI Intelligent?
  32. 32. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Lee Sedol
  33. 33. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Gamification is Really a Big Data Discipline requires a lot of data: for feedback/reinforcement generates a lot of data: from the driven behaviors fairly reward players compare player performance etc. drive desired player behaviorstrack all player behaviors understand player behaviors, intrinsic motivation, cheating etc. data 33
  34. 34. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Components of Gamification System user behavior behavior data rules engine gamification platform gamification management system behavior reporting, dashboards + analytics points, badges, leaderboards, ranks, etc. 34 feedback mechanism
  35. 35. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. the definition of AI evolves  AI is whatever machine can do now that can’t be done before AI = machine mimicry of certain aspects of human behaviors with 2 important characteristics:  automation: the ability to automate decisions and/or subsequent actions  learning: the ability to learn and improve its performance with usage A Definition of AI
  36. 36. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. CHANGE SAME ? ? ? will we adopt AI-based technology? technology adoption can be analyzed as a behavior change
  37. 37. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. - Fogg’s behavior model (FBM):  3 factors underlying human behavior  temporal convergence of 3 factors The Behavior Model for Gamification 37 motivation ability triggeraction wants can told to BJ Fogg. 2009. A behavior model for persuasive design. In Proceedings of the 4th International Conference on Persuasive Technology pp7
  38. 38. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. - Fogg’s behavior model (FBM):  3 factors underlying human behavior  temporal convergence of 3 factors  ability = access to required resources at the moment when you need to perform the behavior The Behavior Model for Gamification 38 action motivation ability trigger activation threshold (simplicity)
  39. 39. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. CHANGE SAME ? ? ? will we adopt AI-based technology? absolutely… because simplicity drives behavior changes!
  40. 40. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. What happens when Ordinary “Things” Starts to Learn and Think?
  41. 41. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. feedback over network compute distributed over network the internet Key Tech Enablers of AI + Internet of Things (IoT) big data machine learning compute power human decisions + actions Vint Cerf where did all the big data come from? where did the internet come from?
  42. 42. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. big data feedback over network compute distributed over network the internet Key Tech Enablers of AI + Internet of Things (IoT) big data machine learning compute power human decisions + actions Vint Cerf Kevin Ashton the internet of things (IoT) cheap internet bandwidth cheap open source hardware: Arduino or Raspberry Pi
  43. 43. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Growth of IoT source: Mario Morales - IDC
  44. 44. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Growth of IoT 50 40 30 20 10 0 billionsofdevices ‘90 ‘92 ‘94 ‘96 ‘98 ‘00 ‘02 ‘04 ‘06 ‘08 ‘10 ‘12 ‘14 ‘16 ‘18 ‘20 1990 world population 2018 world population
  45. 45. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Architecture of IoT Devices sensors local compute /storage network /internet cloud compute /storage things internet • sensor + big data • light weigh local (edge/fog) compute/storage • local network (via wifi mesh network) + internet • intensive cloud compute/storage enables collective learning if I’m not home
  46. 46. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. Top Industries Using IoT
  47. 47. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. AI + IoT is not w/o Risk
  48. 48. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. AI + IoT is not without risk
  49. 49. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. to ensure our own survival we need to be better humans AI+IoT is still at its infancy unlike a human child • learning is constant, complete (learns everything) + fast • never forget • they’re smarter + will be better than us in everything we do they learn from us, humans, our every decisions + behaviors
  50. 50. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. We All Have a Mission as Gamification Practitioners we need to gamify ourselves as a human race to exhibit better human behaviors (e.g. integrity, empathy, compassion, etc.) so our digital creation (AI and robots) will have good data to learn from, just as our children will have good parents to model after
  51. 51. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. thank you, q&a, + follow me twitter: @mich8elwu linkedin.com/in/MichaelWuPhD
  52. 52. twitter: @mich8elwu linkedin.com/in/MichaelWuPhD ©2018 PROS, Inc. All rights reserved. Confidential and Proprietary. PROS Headquarters Houston, Texas 3100 Main Street, Suite 900 Houston, TX 77002, USA +1-800-555-3548 PROS San Francisco 101 Montgomery Street, Suite 400 San Francisco, CA 94104, USA +1-415-283-3000 PROS Chicago 5215 Old Orchard Road, Suite 505 Skokie, IL 60077, USA +1-847-583-8450 PROS Austin 3600 Parmer Lane, Suite 205 Austin, TX 78727, USA +1-713-335-5829 PROS Toulouse Le Galilée 185 rue Galilée 31670 Labège France +33 (0) 811 70 78 78 PROS München Leopoldstrasse 23 80802 München Germany +49 (0) 89 24442 3097 PROS Paris 10 Boulevard Haussmann 6th Floor 75009 Paris France +33 811 70 78 78 PROS London 4th Floor, East Wing Communications House South Street Staines-Upon-Thames TW18 4PR United Kingdom +44 (0) 1784 777 010 PROS Frankfurt Frankfurt Herriot’s 2nd Floor, Herriotstraße 1 60528 Frankfurt, Germany +49 (0) 69 677 330 15 PROS Sydney The Ark Level 32 101 Miller Street North Sydney NSW 2060 Australia +61 2 8912 2199 PROS Dublin Ormond Building 31-36 Ormond Quay Upper Dublin 7 Ireland +1-800-555-3548 PROS Sofia 1 Alabin Str., TELUS Tower 16th floor Macedonia square Sofia 1000, Bulgaria +359 2 958 05 95 Thank you

×