Emotion AI

Emotion AI

Universidad de Guadalajara, Centro Universitario Tonalá.
Jornadas de la Ciencia 2019.

B546a9b97d993392e4b22b74b99b91fe?s=128

Javier Gonzalez

November 21, 2019
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  1. Emotion AI: Inteligencia Artificial y reconocimiento de emociones Javier Gonzalez-Sanchez,

    PhD javiergs@asu.edu www. javiergs.com
  2. Dr. Javier Gonzalez-Sanchez Arizona State University | Universidad Panamericana jgonzalezs@up.edu.mx

    | javiergs@asu.edu www.javiergs.com @mscjaviergs
  3. Inteligencia 3 Conocimiento emocional Comprensión Aprendizaje Planificación Solución de problemas

  4. Afecto 4

  5. Emotion AI

  6. Emotion AI El Afecto (Emociones y Sentimientos) controla nuestras decisiones

    racionales
  7. Decisiones racionales 7

  8. Agenda 1. Inteligencia Artificial - ¿cómo funciona eso? 2. Reconocimiento

    de Emociones - ¿funciona eso? 3. Y - ¿para qué me sirve eso?
  9. Inteligencia Artificial ¿cómo funciona eso? 1

  10. None
  11. None
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  13. None
  14. None
  15. None
  16. None
  17. None
  18. Temas

  19. Temas

  20. Neural Networks

  21. Neural Network | Neuron input output

  22. Neural Network | Neuron X1 X2 X3 x4

  23. Neural Network | Neuron X1 X2 X3 x4 • Sumar

    x1 + x2 + …
  24. Neural Network | Neuron X1 X2 X3 x4 • Normalizar

  25. Neural Network | Neuron X1=1 X2=0.5 X3=0.5 X4=-1 • Sumar

    • Normalizar 0.73
  26. Neural Network X1 X2 X3 X4 W1 W2 W3 W4

    sig (W1*X1 + W2*X2 + W3*X3 + W4*X4) • Sumar • Multiplicar • Normalizar
  27. Neural Network | Weights input output

  28. Neural Network Y, ¿dónde está la inteligencia? En el aprendizaje

    y razonamiento para calcular los valores de W Calcular los Valores W sig (W1*X1 + W2*X2 + W3*X3 + W4*X4)
  29. Ejemplo

  30. Ejemplo

  31. Neural Network X1 X2 X3 X4 W1,1 W2,1 W3,1 W4,1

    sig (W11*X1 + W21*X2 + W31*X3 + W41*X4) W1,2 W2,2 W3,2 W4,2 sig (W12*X1 + W22*X2 + W32*X3 + W42*X4) W [i][j] = W[weight][neuron]
  32. Neural Network X1 X2 X3 X4 sig (W111*X1 + W121*X2

    + W131*X3 + W141*X4) W1,1,2 W1,2,2 W1,3,2 W1,4,2 W [k][i][j] = W[layer][weight] [neuron] sig ( ) sig (W112*X1 + W122*X2 + W132*X3 + W142*X4) sig ( ) W2,1,1 W2,2,1 W2,1,2 W2,2,2 layer_1 layer_2
  33. Back propagation • Partial derivative of the cost function with

    respect to any weight in the network. • We want to see how the function changes as we let just one of those variables change while holding all the others constant. • Black-box
  34. Necesitamos Datos

  35. Reconocimiento de Emociones ¿funciona eso? 2

  36. Sensores, Percepción e Integración 36

  37. Historia

  38. 1.1 Tu Cerebro te delata Ondas cerebrales 38

  39. Tu Cerebro te delata 39

  40. Tu Cerebro te delata 40 $ 799.00

  41. Tu Cerebro te delata 41

  42. Tu Cerebro te delata 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
  43. Tu Cerebro te delata 43 14 canales 128 lecturas por

    segundo 1,792 valores por segundo 107,520 valores por minuto 6,451,200 valores por hora
  44. Tu Cerebro te delata 44 https://askabiologist.asu.edu/brain-regions

  45. Tu Cerebro te delata 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
  46. Tu Cerebro te delata 46 5 reportes por segundo 5

    estados afectivos 25 valores por segundo 1,500 valores por minuto 90,000 valores por hora
  47. Demo 47

  48. 1.2 Un mundo te observa Gestos Your Date Here Your

    Footer Here 48
  49. Un mundo te observa 49 (Ekman and Friesen 1978) –

    Facial Action Coding System, 46 actions (plus head movements). 19 Lip Corner Depressor 26 Jaw Drop 27 Mouth Stretch
  50. Un mundo te Observa

  51. Un mundo te Observa 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
  52. Un mundo te Observa 52 30 cuadros por segundo 10

    inferencias por segundo 600 valores por minuto 36,000 valores por hora
  53. Demo 53

  54. 1.3 Rastreo de Ojos ¿Qué estas viendo? Your Date Here

    Your Footer Here 54
  55. ¿Qué estas viendo? 55 $ 199.00 $ 9,999.00

  56. ¿Qué estas viendo? 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
  57. ¿Qué estas viendo? 57 30 o 60 cuadros por segundo

    30 o 60 inferencias por segundo 1,800 o 3,600 valores por minuto 108,000 o 216, 000 valores por hora
  58. Demo 58

  59. Y ¿para qué me sirve eso? 3

  60. Demo 60 BCI and Gaze Points engagement

  61. Demo 61 BCI and Gaze Points frustration

  62. Demo 62 BCI and Gaze Points engagement

  63. Demo 63 BCI and Gaze Points frustration

  64. Demo 64 BCI and Gaze Points

  65. Demo 65 Emotion Adaptation | Games

  66. Demo 66 Emotion Adaptation | Games

  67. Demo 67

  68. En Conclusión 68

  69. Dr. Javier Gonzalez-Sanchez Universidad Panamericana | Arizona State University jgonzalezs@up.edu.mx

    | javiergs@asu.edu www.javiergs.com @mscjaviergs !Gracias!