WEBVTT

1
00:00:00.310 --> 00:00:03.630
We have built with using only a camera and a watch,

2
00:00:03.630 --> 00:00:05.740
or using only a smartphone and a watch,

3
00:00:05.740 --> 00:00:09.810
the ability to monitor and forecast your stress,

4
00:00:09.810 --> 00:00:14.810
mood, and health with 78-87% accuracy already.

5
00:00:15.030 --> 00:00:16.340
Artificial intelligence

6
00:00:16.340 --> 00:00:19.160
has more computational power than ever.

7
00:00:19.160 --> 00:00:21.110
And that's enabling huge strides

8
00:00:21.110 --> 00:00:24.210
in new sciences like affective computing.

9
00:00:24.210 --> 00:00:26.770
Rosalind Picard at the MIT Media Lab

10
00:00:26.770 --> 00:00:29.740
is at the cutting edge of this new technology.

11
00:00:29.740 --> 00:00:32.890 line:15% 
Affective computing is defined to be computing

12
00:00:32.890 --> 00:00:35.500 line:15% 
that relates to, arises from,

13
00:00:35.500 --> 00:00:39.190 line:15% 
or deliberately influences human emotion.

14
00:00:39.190 --> 00:00:42.890
It's actually intended to be an area

15
00:00:42.890 --> 00:00:47.830
that puts affect on the same playing field as cognition.

16
00:00:47.830 --> 00:00:50.340
We've had a lot of work on cognitive science,

17
00:00:50.340 --> 00:00:52.990
computing that recognizes logic,

18
00:00:52.990 --> 00:00:54.493
and reason and thinking.

19
00:00:55.330 --> 00:01:00.020
It's not about making computers emotional and unpleasant,

20
00:01:00.020 --> 00:01:02.470
and all these things that would be a nightmare.

21
00:01:02.470 --> 00:01:04.300
It's very much about bringing together

22
00:01:04.300 --> 00:01:07.570
a proper balance between affect and cognition.

23
00:01:07.570 --> 00:01:09.110
An affective state

24
00:01:09.110 --> 00:01:13.000
is maybe most simply described as an emotional state.

25
00:01:13.000 --> 00:01:16.420
But often it's more subtle than an emotional state.

26
00:01:16.420 --> 00:01:20.370
It could be simply that you're a little agitated,

27
00:01:20.370 --> 00:01:23.090
a little wound up, a little mellow,

28
00:01:23.090 --> 00:01:25.160
a little bit in an irritable mood,

29
00:01:25.160 --> 00:01:26.890
or a little bit in a happy mood.

30
00:01:26.890 --> 00:01:30.060
It may not look like anything outwardly.

31
00:01:30.060 --> 00:01:31.400
Part of the complexity

32
00:01:31.400 --> 00:01:34.460
is we have over simplified notions of emotion.

33
00:01:34.460 --> 00:01:36.870
For example, we might think a smile means happy,

34
00:01:36.870 --> 00:01:39.690
but it turns out that 90% of the time when people

35
00:01:39.690 --> 00:01:42.700
are frustrated in our studies, they smile.

36
00:01:42.700 --> 00:01:45.160
And in fact, they don't just smile with like a mean smile.

37
00:01:45.160 --> 00:01:47.380
They actually smile with a "true smile"

38
00:01:47.380 --> 00:01:49.903
of happiness that psychologists said means happy.

39
00:01:52.010 --> 00:01:53.980
Reading emotions isn't easy,

40
00:01:53.980 --> 00:01:56.270
especially if you're a computer.

41
00:01:56.270 --> 00:01:57.870
The nuances of biometrics

42
00:01:57.870 --> 00:02:00.650
have been just too difficult to analyze.

43
00:02:00.650 --> 00:02:02.760
AI has changed that.

44
00:02:02.760 --> 00:02:04.620
When we're training the machine learning,

45
00:02:04.620 --> 00:02:06.900
we always say we need more data, we need more data.

46
00:02:06.900 --> 00:02:08.170
And then my students were like,

47
00:02:08.170 --> 00:02:09.670
oh, I want to build this mood recognizer.

48
00:02:09.670 --> 00:02:11.880
I'm like, oh, I think that's too hard.

49
00:02:11.880 --> 00:02:12.713
Well, it turned out,

50
00:02:12.713 --> 00:02:14.180
it really is hard when you just try

51
00:02:14.180 --> 00:02:15.560
to build a mood recognizer.

52
00:02:15.560 --> 00:02:16.660
But it turns out that if you try

53
00:02:16.660 --> 00:02:20.350
to build a mood and stress and health recognizer,

54
00:02:20.350 --> 00:02:22.900
ironically, if you actually make it even harder,

55
00:02:22.900 --> 00:02:25.110
if you give it three categories to learn,

56
00:02:25.110 --> 00:02:26.570
instead of just one,

57
00:02:26.570 --> 00:02:28.703
the machine learning actually does better.

58
00:02:29.720 --> 00:02:30.950
Kind of counterintuitive,

59
00:02:30.950 --> 00:02:32.600
but what it's actually doing inside

60
00:02:32.600 --> 00:02:34.500
is it's kind of exploiting the similarities

61
00:02:34.500 --> 00:02:37.560
and differences in these related tasks.

62
00:02:37.560 --> 00:02:39.680
And that's helping it do better at stress,

63
00:02:39.680 --> 00:02:41.420
at mood, and at health.

64
00:02:41.420 --> 00:02:44.090
We've now realized that we've got

65
00:02:44.090 --> 00:02:47.520
the ability to forecast and recognize tomorrow's,

66
00:02:47.520 --> 00:02:50.460
not just stress level, but mood level.

67
00:02:50.460 --> 00:02:52.483
Good bad mood, very coarse.

68
00:02:53.951 --> 00:02:54.784
And health.

69
00:02:54.784 --> 00:02:55.617
Are you going to be healthy?

70
00:02:55.617 --> 00:02:57.890
Are you going to be likely calling in sick

71
00:02:57.890 --> 00:03:01.069
and kind of on a zero to 100 scale estimating where you are,

72
00:03:01.069 --> 00:03:04.053
with about 12 to 14 points of accuracy.

73
00:03:04.920 --> 00:03:07.640
So, with affective computing,

74
00:03:07.640 --> 00:03:10.530
AI can detect and predict emotions.

75
00:03:10.530 --> 00:03:12.410
What else can it do?

76
00:03:12.410 --> 00:03:15.170
I think one of the most exciting revelations

77
00:03:15.170 --> 00:03:18.040
we've had lately is instead of just trying

78
00:03:18.040 --> 00:03:21.460
to build an AI that empathizes and has all of

79
00:03:21.460 --> 00:03:25.620
this emotional intelligence, I'm much more excited now,

80
00:03:25.620 --> 00:03:27.640
by seeing how we can build AI's

81
00:03:27.640 --> 00:03:30.010
that work with people helping people

82
00:03:30.010 --> 00:03:32.230
be more emotionally intelligent.

83
00:03:32.230 --> 00:03:35.070
There's kind of a sweet spot where

84
00:03:35.070 --> 00:03:39.890
the AI is good at helping us detect maybe

85
00:03:39.890 --> 00:03:42.490
where we're not doing what we want to be doing.

86
00:03:42.490 --> 00:03:44.880
It's at that moment of stress that they need

87
00:03:44.880 --> 00:03:47.320
the technology to help maybe give them

88
00:03:47.320 --> 00:03:50.470
that little gentle nudge or reminder.

89
00:03:50.470 --> 00:03:53.580
And then you're empowering the person

90
00:03:53.580 --> 00:03:56.260
to actually take their learning to a whole new level.

91
00:03:56.260 --> 00:03:58.260
It's absolutely a great way to think about

92
00:03:58.260 --> 00:04:02.530
it as a life coach, as a just help you up

93
00:04:02.530 --> 00:04:04.110
your game kind of coach.

94
00:04:04.110 --> 00:04:08.160
And what we wind up with is people who are just more amazing

95
00:04:08.160 --> 00:04:11.160
to work with and people who feel better at

96
00:04:11.160 --> 00:04:14.360
the end of the day and people who accomplish a lot more

97
00:04:14.360 --> 00:04:18.623
and have much greater opportunities tomorrow as well.

98
00:04:23.640 --> 00:04:25.850
So thanks to affective computing,

99
00:04:25.850 --> 00:04:28.080
AI can forecast your stress level,

100
00:04:28.080 --> 00:04:31.890
your mood, and your health, just like your weather app.

101
00:04:31.890 --> 00:04:34.080
Should you take on that big challenge

102
00:04:34.080 --> 00:04:36.960
or stay home until the storm passes?

103
00:04:36.960 --> 00:04:39.310
AI can help you decide.

104
00:04:39.310 --> 00:04:42.120
But what does this mean for organizations

105
00:04:42.120 --> 00:04:44.050
now that they can use AI to gather

106
00:04:44.050 --> 00:04:47.630
and analyze real time data on teams' emotions?

107
00:04:47.630 --> 00:04:48.890
In our next video,

108
00:04:48.890 --> 00:04:50.957
Roz describes how some organizations

109
00:04:50.957 --> 00:04:53.340
are already using affective computing

110
00:04:53.340 --> 00:04:56.600
to improve team performance and employees' well-being.

111
00:04:56.600 --> 00:05:00.100
That positive mood can lead their entire brain

112
00:05:00.100 --> 00:05:02.200
to think differently.

113
00:05:02.200 --> 00:05:04.700
But she also points out potential pitfalls

114
00:05:04.700 --> 00:05:05.700
to avoid.

115
00:05:05.700 --> 00:05:07.440
Even if you have the best of intentions

116
00:05:07.440 --> 00:05:09.690
in observing their stress, this can backfire.