We have built with using only a camera and a watch,
or using only a smartphone and a watch,
the ability to monitor and forecast your stress,
mood, and health with 78-87% accuracy already.
Artificial intelligence
has more computational power than ever.
And that's enabling huge strides
in new sciences like affective computing.
Rosalind Picard at the MIT Media Lab
is at the cutting edge of this new technology.
Affective computing is defined to be computing
that relates to, arises from,
or deliberately influences human emotion.
It's actually intended to be an area
that puts affect on the same playing field as cognition.
We've had a lot of work on cognitive science,
computing that recognizes logic,
and reason and thinking.
It's not about making computers emotional and unpleasant,
and all these things that would be a nightmare.
It's very much about bringing together
a proper balance between affect and cognition.
An affective state
is maybe most simply described as an emotional state.
But often it's more subtle than an emotional state.
It could be simply that you're a little agitated,
a little wound up, a little mellow,
a little bit in an irritable mood,
or a little bit in a happy mood.
It may not look like anything outwardly.
Part of the complexity
is we have over simplified notions of emotion.
For example, we might think a smile means happy,
but it turns out that 90% of the time when people
are frustrated in our studies, they smile.
And in fact, they don't just smile with like a mean smile.
They actually smile with a "true smile"
of happiness that psychologists said means happy.
Reading emotions isn't easy,
especially if you're a computer.
The nuances of biometrics
have been just too difficult to analyze.
AI has changed that.
When we're training the machine learning,
we always say we need more data, we need more data.
And then my students were like,
oh, I want to build this mood recognizer.
I'm like, oh, I think that's too hard.
Well, it turned out,
it really is hard when you just try
to build a mood recognizer.
But it turns out that if you try
to build a mood and stress and health recognizer,
ironically, if you actually make it even harder,
if you give it three categories to learn,
instead of just one,
the machine learning actually does better.
Kind of counterintuitive,
but what it's actually doing inside
is it's kind of exploiting the similarities
and differences in these related tasks.
And that's helping it do better at stress,
at mood, and at health.
We've now realized that we've got
the ability to forecast and recognize tomorrow's,
not just stress level, but mood level.
Good bad mood, very coarse.
And health.
Are you going to be healthy?
Are you going to be likely calling in sick
and kind of on a zero to 100 scale estimating where you are,
with about 12 to 14 points of accuracy.
So, with affective computing,
AI can detect and predict emotions.
What else can it do?
I think one of the most exciting revelations
we've had lately is instead of just trying
to build an AI that empathizes and has all of
this emotional intelligence, I'm much more excited now,
by seeing how we can build AI's
that work with people helping people
be more emotionally intelligent.
There's kind of a sweet spot where
the AI is good at helping us detect maybe
where we're not doing what we want to be doing.
It's at that moment of stress that they need
the technology to help maybe give them
that little gentle nudge or reminder.
And then you're empowering the person
to actually take their learning to a whole new level.
It's absolutely a great way to think about
it as a life coach, as a just help you up
your game kind of coach.
And what we wind up with is people who are just more amazing
to work with and people who feel better at
the end of the day and people who accomplish a lot more
and have much greater opportunities tomorrow as well.
So thanks to affective computing,
AI can forecast your stress level,
your mood, and your health, just like your weather app.
Should you take on that big challenge
or stay home until the storm passes?
AI can help you decide.
But what does this mean for organizations
now that they can use AI to gather
and analyze real time data on teams' emotions?
In our next video,
Roz describes how some organizations
are already using affective computing
to improve team performance and employees' well-being.
That positive mood can lead their entire brain
to think differently.
But she also points out potential pitfalls
to avoid.
Even if you have the best of intentions
in observing their stress, this can backfire.