Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Emotional AI

Emotional AI

* Gettysburg College (ACM Distinguished Speakers Series 2021)

B546a9b97d993392e4b22b74b99b91fe?s=128

Javier Gonzalez
PRO

April 01, 2021
Tweet

Transcript

  1. !"#$%&'(&)*+&'"#,$-.#/0#1'$213*1/4$ &'$4/,#$.3''&56#$57 For additional information, please visit http://dsp.acm.org/

  2. Thank you For additional information, please visit http://dsp.acm.org/

  3. Javier Gonzalez-Sanchez javiergs@asu.edu javiergs.com Artificial Emotional Intelligence Building Empathetic Machines

  4. Research Teaching Me

  5. 5 Emotions Motivation signals what humans care about is involved

    in rational decision-making and action selection.
  6. 6 Motivation rational decision-making

  7. 7 Motivation

  8. 8 Outline Background 1 § Key Ideas § Context and

    Workflow Sensing and Perception 2 § Data: Brainwaves, Facial Gestures, Eye Tracking, and More § Machine Learning Models Integration 3 § Fusion § Emotional Models: Ekman and Mehrabian Projects 4
  9. 9 Key Ideas Affect, affective state, emotion, emotional state, 👤

    feelings 🧠, mood ⏱.
  10. 10 Key Ideas +P+A+D Engagement +P-A+D Meditation Concentration Thought Relaxation

    +P+A-D Excitement Interest Dependence +P-A-D Starting Agreement Docility -P+A+D Disagreement Hostility -P-A+D Disdain -P+A-D Frustration Unsureness Anxiety -P-A-D Boredom
  11. 11 Key Ideas Many technologies may be improved by the

    capability to recognize human affect and to respond adaptively by appropriately modifying their operation Empathy is the capacity to understand what another person is experiencing Emotion AI
  12. 12 Context Rosalind Picard MIT MediaLab HCI Affective Computing 1997

  13. 13 Context

  14. 14 Context Rosalind Picard MIT MediaLab Winslow Burleson University of

    Arizona HCI Affective Computing 1997 SW Engineering Self-Adaptive Systems
  15. 15 Workflow

  16. 16 Outline Background 1 § Key Ideas § Context and

    Workflow Sensing and Perception 2 § Data: Brainwaves, Facial Gestures, Eye Tracking, and More § Machine Learning Models Integration 3 § Fusion § Emotional Models: Ekman and Mehrabian Projects 4
  17. 17 1

  18. 18 Brain

  19. 19 Brain https://askabiologist.asu.edu/brain-regions

  20. 20 Brain Timestamp AF3 F7 F3 FC5 T7 P7 O1

    O2 P8 T8 FC6 F4 F8 AF4 AccX AccY 101116112544901 4542.05 4831.79 4247.18 4690.26 4282.56 4395.38 4591.79 4569.23 4360 4570.77 4297.44 4311.28 4282.56 4367.18 1660 2003 101116112544901 4536.92 4802.05 4243.08 4673.85 4272.31 4393.33 4592.82 4570.26 4354.87 4570.26 4292.31 4309.74 4277.95 4370.77 1658 2002 101116112545010 4533.33 4798.97 4234.87 4669.74 4301.03 4396.92 4592.31 4570.77 4351.28 4561.03 4281.54 4301.54 4271.28 4363.59 1659 2003 101116112545010 4549.23 4839.49 4241.03 4691.28 4333.85 4397.95 4596.41 4567.18 4355.9 4556.41 4286.15 4306.15 4277.95 4369.74 1659 2003 101116112545010 4580 4865.64 4251.79 4710.26 4340 4401.54 4603.59 4572.82 4360 4558.46 4298.97 4324.62 4296.41 4395.9 1657 2004 101116112545010 4597.44 4860 4252.82 4705.64 4350.26 4412.31 4603.59 4577.44 4357.44 4555.9 4295.38 4329.23 4296.41 4414.36 1656 2005 101116112545010 4584.62 4847.69 4246.67 4690.26 4360 4409.23 4597.44 4569.74 4351.79 4549.74 4278.97 4316.92 4272.82 4399.49 1656 2006 101116112545010 4566.15 4842.05 4238.46 4684.1 4322.05 4389.74 4592.82 4566.67 4351.79 4549.74 4274.36 4310.26 4262.05 4370.77 1655 2005 101116112545010 4563.59 4844.62 4231.79 4687.69 4267.69 4387.69 4594.36 4580 4361.03 4556.41 4278.97 4310.77 4274.36 4370.77 1653 2006 101116112545010 4567.18 4847.18 4233.33 4688.72 4285.13 4409.23 4602.05 4589.23 4368.21 4560 4280.51 4310.77 4281.54 4390.26 1655 2004 101116112545010 4562.05 4840.51 4227.18 4673.85 4300 4405.13 4611.28 4601.03 4376.41 4561.54 4280 4303.59 4279.49 4374.87 1652 2000
  21. 21 Brain 14 channels 128 samples per second 1,792 values

    por second 107,520 values per minute 6,451,200 values per hour
  22. 22 Brain Timestamp Short Term Excitement Long Term Excitement Engagement

    Meditation Frustration 101116091145065 0.447595 0.54871 0.834476 0.333844 0.536197 101116091145190 0.447595 0.54871 0.834476 0.333844 0.536197 101116091145315 0.447595 0.54871 0.834476 0.333844 0.536197 101116091145440 0.487864 0.546877 0.834146 0.339548 0.54851 101116091145565 0.487864 0.546877 0.834146 0.339548 0.54851 101116091145690 0.487864 0.546877 0.834146 0.339548 0.54851 101116091145815 0.487864 0.546877 0.834146 0.339548 0.54851 101116091145940 0.521663 0.545609 0.839321 0.348321 0.558228 101116091146065 0.521663 0.545609 0.839321 0.348321 0.558228 101116091146190 0.521663 0.545609 0.839321 0.348321 0.558228 101116091146315 0.521663 0.545609 0.839321 0.348321 0.558228 101116091146440 0.509297 0.544131 0.84401 0.358717 0.546771 101116091146565 0.509297 0.544131 0.84401 0.358717 0.546771 101116091146690 0.509297 0.544131 0.84401 0.358717 0.546771 101116091146815 0.509297 0.544131 0.84401 0.358717 0.546771 101116091146941 0.451885 0.541695 0.848087 0.368071 0.533919
  23. 23 Brain 23 5 samples per second 5 affective states

    25 values per second 1,500 values per minute 90,000 values per hour
  24. 24 Brain

  25. 25 ML • Neural Networks • Random Forest

  26. 2

  27. 27 Face (Ekman and Friesen 1978) – Facial Action Coding

    System, 46 actions (plus head movements). 19 Lip Corner Depressor 26 Jaw Drop 27 Mouth Stretch
  28. 28 Face

  29. 29 Face Timestamp Agreement Concentrating Disagreement Interested Thinking Unsure 101116112838516

    0.001836032 0.999917 1.79E-04 0.16485406 0.57114255 0.04595062 101116112838578 0.001447654 0.9999516 1.29E-04 0.16310683 0.5958921 0.042706452 101116112838672 5.97E-04 0 1.5E-04 0.44996294 0.45527613 0.00789697 101116112838766 2.46E-04 0 1.75E-04 0.77445686 0.32144752 0.001418217 101116112838860 1.01E-04 0 2.04E-04 0.93511915 0.21167138 2.53E-04 101116112838953 4.18E-05 0 2.38E-04 0.983739 0.13208677 4.52E-05 101116112839016 1.72E-05 0 2.78E-04 0.9960774 0.07941038 8.07E-06 101116112839110 7.1E-06 0 3.24E-04 0.99906266 0.046613157 1.44E-06 101116112839156 2.92E-06 0 3.77E-04 0.99977654 0.026964737 2.57E-07 101116112839250 1.21E-06 0 4.4E-04 0.9999467 0.015464196 4.58E-08 101116112839391 4.97E-07 0 5.12E-04 0.9999873 0.008824189 8.18E-09 101116112839438 2.05E-07 0 5.97E-04 0.999997 0.005020725 1.46E-09 101116112839547 8.43E-08 0 6.96E-04 0.9999993 0.002851939 2.6E-10 101116112839578 3.47E-08 0 8.11E-04 0.9999999 0.001618473 4.64E-11 101116112839688 1.43E-08 0 9.45E-04 0.99999994 9.18E-04 8.29E-12 101116112839781 5.9E-09 0 0.001101404 1 5.21E-04 1.48E-12 101116112839828 2.43E-09 0 0.001283521 1 2.95E-04 2.64E-13
  30. 30 Face 30 30 frames per second 10 inferences per

    second 600 values per minute 36,000 values per hour
  31. 31 Face

  32. 32 ML • Support Vector Machine

  33. 3

  34. 34 Eye

  35. 35 Eye Timestamp GPX GPY Pupil Left Validity L Pupil

    Right Validity R Fixation Event AOI 101124162405582 636 199 2.759313 0 2.88406 0 48 Content 101124162405599 641 207 2.684893 0 2.855817 0 48 Content 101124162405615 659 211 2.624458 0 2.903861 0 48 Content 101124162405632 644 201 2.636186 0 2.916132 0 48 Content 101124162405649 644 213 2.690685 0 2.831013 0 48 Content 101124162405666 628 194 2.651784 0 2.869714 0 48 Content 101124162405682 614 177 2.829281 0 2.899828 0 48 Content 101124162405699 701 249 2.780344 0 2.907665 0 49 Content 101124162405716 906 341 2.853761 0 2.916398 0 49 Content 101124162405732 947 398 2.829427 0 2.889944 0 49 Content 101124162405749 941 400 2.826602 0 2.881179 0 49 Content 101124162405766 938 403 2.78699 0 2.87948 0 49 KeyPress Content 101124162405782 937 411 2.803387 0 2.821803 0 49 Content 101124162405799 934 397 2.819166 0 2.871547 0 49 Content 101124162405816 941 407 2.811687 0 2.817927 0 49 Content 101124162405832 946 405 2.857419 0 2.857427 0 49 Content 101124162405849 0 0 -1 4 -1 4 49 Content
  36. 36 Eye 30 o 60 frames per second 30 o

    60 inferences per second 1,800 o 3,600 values per minute 108,000 o 216, 000 values per hour
  37. 37 Eye

  38. 38 ML • Just Geometry

  39. 4

  40. 40 Pressure Sensor More

  41. 41 Galvanic Skin Conductance More

  42. 42 More

  43. 43 More Gonzalez-Sanchez et al, 2011

  44. 44 ML • Random Forest • Deep Learning

  45. 45 Outline Background 1 § Key Ideas § Context and

    Workflow Sensing and Perception 2 § Data: Brainwaves, Facial Gestures, Eye Tracking, and More § Machine Learning Models Integration 3 § Fusion § Emotional Models: Ekman and Mehrabian Projects 4
  46. 46 Sparse Learning timestamp fixationIndex gazePointX gazePointY mappedFixationPoin tX mappedFixationPoin

    tY fixationDuration Short Term Excitement Long Term Excitement Engagement/Boredom Meditation Frustration Conductance agreement concentrating 4135755652 0.436697 0.521059 0.550011 0.335825 0.498908 0.40169062 8 4135755659 213 573 408 570 408 216 4135755668 0.436697 0.521059 0.550011 0.335825 0.498908 4135755676 213 566 412 570 408 216 4135755692 213 565 404 570 408 216 4135755709 213 567 404 570 408 216 4135755714 4135755726 213 568 411 570 408 216 4135755742 213 568 409 570 408 216 4135755759 213 563 411 570 408 216 4135755761 4135755776 213 574 413 570 408 216 4135755792 213 554 402 570 408 216 4135755809 214 603 409 696 405 216 4135755824 4135755826 214 701 407 696 405 216 4135755842 214 697 403 696 405 216 4135755859 214 693 401 696 405 216 4135755876 214 700 402 696 405 216 4135755892 214 701 411 696 405 216 4135755909 214 686 398 696 405 216 4135755918 4135755926 214 694 399 696 405 216 4135755942 214 694 407 696 405 216 4135755959 214 698 404 696 405 216 4135755964 4135756027 0.436697 0.521059 0.550011 0.335825 0.498908 1 1
  47. 47 State Machine timestamp fixationIndex gazePointX gazePointY mappedFixationPoin tX mappedFixationPoin

    tY fixationDuration Short Term Excitement Long Term Excitement Engagement/Boredom Meditation Frustration Conductance agreement concentrati ng 4135755652 213 574 414 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755659 213 573 408 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755668 213 573 408 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755676 213 566 412 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755692 213 565 404 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755709 213 567 404 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755714 213 567 404 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755726 213 568 411 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755742 213 568 409 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755759 213 563 411 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755761 213 563 411 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755776 213 574 413 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755792 213 554 402 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135755809 214 603 409 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1 4135756027 215 728 406 804 387 183 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
  48. 48 PAD

  49. 49 Outline Background 1 § Key Ideas § Context and

    Workflow Sensing and Perception 2 § Data: Brainwaves, Facial Gestures, Eye Tracking, and More § Machine Learning Models Integration 3 § Fusion § Emotional Models: Ekman and Mehrabian Projects 4
  50. 50 Affect Recognition BCI and Gaze Points engagement

  51. 51 Affect Recognition BCI and Gaze Points frustration

  52. 52 Affect Recognition BCI and Gaze Points engagement

  53. 53 Affect Recognition BCI and Gaze Points frustration

  54. 54 Neuromarketing Chavez, M., Christopherson, R., Gonzalez-Sanchez, J., Atkinson, R.

    User Experience. 2018
  55. 55 Projects Gonzalez-Sanchez, J., Chavez, M., Gibson, D., and Atkinson,

    R. Multimodal Affect Recognition in Virtual Worlds. ACII 2013
  56. 56 Projects Harris, A., Hoch, A., Kral, R., Teposte, M.,

    Villa, A., et. al. Including affect-driven adaptation to the Pac-Man video game. ACM ISWC 2014
  57. 57 Projects Bernays, R., Mone, J., Yau, P., Murcia, M.,

    Gonzalez-Sanchez, J., et al. Lost in the dark: ACM UIST 2012
  58. 58 Projects Hang, B., Loucks, S., Patel, P., Wiseman, K.

    Capstone Project 2021-
  59. 59 Projects VanLehn, K., Burleson, W., Chavez, M., Gonzalez-Sanchez, J.,

    et al. The Affective Meta-Tutoring project ITS 2014 - 2018
  60. 60 Projects Rodriguez, J., Gonzalez-Sanchez, J., Del-Valle, C. Affect-Driven Robot-assisted

    Walking Therapy 2019-
  61. 61 Education Marketing Framework Tools Vision Health

  62. 62 Projects

  63. 63 Conclusion Let us rethink how scientist and engineers design

    future software systems
  64. 64 Artificial Emotional Intelligence: Building Empathetic Machines Questions Javier Gonzalez-Sanchez

    javiergs@asu.edu javiergs.com
  65. Thank you For additional information, please visit http://dsp.acm.org/

  66. !"#$%&'(&)*+&'"#,$-.#/0#1'$213*1/4$ &'$4/,#$.3''&56#$57 For additional information, please visit http://dsp.acm.org/

  67. About ACM § ACM, the Association for Computing Machinery (www.acm.org),

    is the premier global community of computing professionals and students with nearly 100,000 members in more than 170 countries interacting with more than 2 million computing professionals worldwide. § OUR MISSION: We help computing professionals to be their best and most creative. We connect them to their peers, to what the latest developments, and inspire them to advance the profession and make a positive impact on society. § OUR VISION: We see a world where computing helps solve tomorrow’s problems – where we use our knowledge and skills to advance the computing profession and make a positive social impact throughout the world. § I am proud to be an ACM Member.