Hi, I'm Cynthia Breazeal.
And in my research, my students and I, we design, we develop,
and we deploy social robots.
We deploy these robots in real human environments, places
like schools, homes, hospitals assisted living facilities
where these robots are designed to engage people
to help us learn, to help us stay healthy,
to help us maintain our well-being,
from children to seniors.
And what I hope you can appreciate from this video
is how deeply and richly interpersonal
the interactions are that people have
with this particular kind of technology, so much so
that, even after 20 years of developing the systems,
I'm still struck by the depth at which this kind of technology
can engage our social minds and even touch our hearts, so much
so that I also have a vision for AI.
And I envision an AI that, not only helps
us to be smarter and more productive,
but I want to see AI also help us to flourish.
The ability for people to deeply connect
with each other and even technology I think
makes this a compelling proposition and I
think with important contributions to society.
But to do that, to be able to promote our well-being,
AI needs to be able to engage our social and emotional selves
in addition to our cognitive selves.
So this morning we heard a lot about the significance
and importance and how fascinating
our social and emotional intelligence is.
Matthew Lieberman is a well-respected social
neuroscientist, has said that social thinking is
our human superpower, that our ability to feel
that other people are feeling, our ability
to appreciate the thoughts of others,
our ability to work as members of a team
is indeed a pinnacle of achievement of our brains.
And we've also learned this morning
that there are specialized structures that
support our social thinking and support our emotional thinking
in the limbic system.
But there's more.
It doesn't just stop at the brain.
It's not just restricted to the brain.
But emotion, for instance, is embodied.
Through the limbic system and the autonomic nervous system,
we experience emotion through our bodies,
and we can measure even activity of emotion in our brains
through things like our heart rate, our respiration, and even
electro dermal activity.
They give us windows into the emotional processing
of our brains.
But of course, there's still more
because our bodies are also situated
in a world with social others.
And the way we communicate and the way we interact
allows us to influence the emotions
of each other, how we think, how we behave, our attitudes.
So we want to think about, how can we
learn to understand the nature of our social emotional
intelligence.
And especially in the context of technology,
it really motivates new kinds of tools that can engage us
in all of these levels.
And in my lab, we've been exploring
the use of social robots as that kind of tool.
As social robots, they can stimulate
human interpersonal behaviors.
We can collect data sets that are much needed in order
to do that.
We can computationally model these processes.
I'll show you some video of this.
And we live in a world where we have
a lot of different kinds of the data,
but not a lot of data like this.
So we learned this morning about the importance
of how young children are biased to learn
from an attentive friendly other.
Well, if you want to design a robot to be able to do that,
that you need to understand, say, how children do this,
how do children learn from each other in a friendly context.
We can track behaviors such as facial expressions,
the way children speak, the way they take turns.
We can computationally model these processes
in order to try to understand and explain these behaviors.
But then we can embed those models
in a social robot who can interact with children
in the same kind of situation and see, not only, how well do
those models perform in the world, in real interaction,
but even what potentially is the impact for the person.
So one of the great things about robots
is they're not only a cool way for us to potentially learn
about our social emotional intelligence,
they can also be a tool or intervention
to help you grow our own intelligence.
So this is a slide highlighting some work
we've been doing on learning how to personalize learning
for children, for young children around their oral language
skills.
So here we've been applying reinforcement learning because,
again, as it turns out, there's a lot of data sets
that don't exist.
And there's a lot of situations where
robots have to actually learn from their own interaction
in the world with people in real time.
So in this particular model, we're
applying reinforcement learning by which a robot is trying
to learn a personalized policy to each child to figure out
what complexity of a story it should
tell a child at a particular moment
to maximize their learning.
The robot is looking at things such as facial expressions
variability to be able to predict what kinds of things,
what kind of stories are going to engage the child, as well
as the sophistication of the story in terms
of the vocabulary and the syntax structure.
And through this process, we can actually develop policies here.
You can see the differences in policies
that are being learned for each child that basically will then
help promote children's own oral language production,
as well as a vocabulary.
So we can see here, in a study we've done of our 12 week
period with the Boston public schools, schools that
have high English language learner populations,
that children, when they interact
with a personal social robot, demonstrate
new syntactic skills faster than with a non-personalized less
robot that follows a fixed curriculum.
And children interacting with the personalized robot
learn more vocabulary.
So this is really, really compelling.
And in fact, when we think about the future,
we are starting to live and work with AI
in a way that is far more pervasive than it
was even five years ago.
This question of who interacts with technology and AI
is changing.
You think about children today.
They're not only digital natives.
They're AI natives.
They're going up at a time when they've always
been able to interact with intelligent machines.
So the ability of these systems to live with us,
to be able to potentially collect data about us,
to be able to personalize us--
personalized to us-- opens up this possibility
of a future of scalable, affordable, effective,
personalized services and support
across many, many different domains.
So you think about personalized education, personalized health
care, personalized coaching.
And of course, it's important that these systems
be personalized because, over a very diverse population,
people have a lot of different needs from abilities
even to disabilities.
And even when we may be feeling our most vulnerable--
because it's when we're feeling vulnerable that we may need
help the most.
And this, of course, raises this question
of the ethical application of these technologies as well.
So we're living in a time where a lot of people
are feeling vulnerable.
We hear in the media about the decline in empathy.
We know that chronic loneliness and social isolation
is on the rise.
And for profoundly social species,
the pain associated with loneliness or isolation
is real pain.
The stress it causes on the body is real stress.
That can result in increased health risks
that are commensurate with obesity,
substance abuse, and smoking.
So we have this opportunity where,
can AI be designed to really support ourselves
in a social emotional capacity to promote and accelerate
our well-being, and how can it be applied in ways
that we may not anticipate.
These explorations are happening today.
I'm going to show you a quick video
to highlight this of the application
of a robot in aging.
A researcher and art therapist with Eldercare Alliance
takes Jibo with her when she meets with the seniors.
She said this is not a case of caretakers
being replaced by automation.
Instead they're using this cute piece of technology
to encourage their residents to connect with each other.
Jibo inspired some giggles and guffaws amongst this group
here.
When you get this age, you should
be able to laugh more and more.
What it does it, it brings that out of us.
That's part of the miracle of it.
And that's part of the miracle of living as far as that goes.
So I see tremendous reason for optimism
in our relationship with these socially emotional technologies
are really going to be able to benefit us.
I want to now look at another really critical area that's
facing society--
so mood disorders.
According to the World Health Organization,
right now depression is the leading cause of ill health
and disability worldwide.
If we look at this chart, we see two populations
and how they handle negative events in their environment.
We have a resilient population that's able to bounce back.
But then we have another population
that's unable to do that.
They're the ones who are susceptible to mood
disorders like depression.
The challenge, of course, is typically, in our health care
system, we intervene out here when these symptoms
have already set in.
We intervene through periodic assessments
done on standardized skills, like the Hamilton Depression
rating scale.
And the question then on the opportunity of technology
is, can you actually use technology
to sense when these things diverge much sooner, intervene
sooner so you might be able to even prevent
depression in the first place.
And this is a problem that's being
tackled by my colleague Rosalind Picard at the Media Lab.
So she applies regression based machine learning methods
to say, OK, so here we can work with clinicians.
We can have these HDRS scores.
We can also from the same population of people collect
data through mobile devices, through wearables--
things that track skin conductance and heart rate--
to be able to characterize things
like sleep, your communication patterns,
your physical activity, your locations,
and apply these machine learning methods to say,
can we now predict these scores based
on the continuous, real time data collected in daily life.
And this chart basically shows that she's
been able to successfully do that, which of course is
tremendously exciting.
She's also gone ahead to think about,
so what about predicting your mood or your wellness forecast.
Can we take similar kinds of data
from mobile devices and wearables to be able to say,
based on what the data says today
what you're going to be feeling or emotional well-being
tomorrow.
She with her students has applied
multitask learning, which is the form of transfer learning,
to be able to show that, not only are you able to do this,
but she shows the importance of personalization
because people have different behaviors that affect
their well-being differently.
So for instance, this person up here,
when they text the night before, it actually elevates their mood
the next day.
But this other person has the exact opposite effect.
And she can infer these differences
from her models, which means that the system should actually
give different kinds of advice.
So this is very intriguing.
So we can think about tackling all kinds of problems with AI.
The question is really what problems do we decide to solve,
which gives rise to an even bigger question for me, which
is who creates with AI.
In a society, there's only a few of us
who have the skills and ability, the
know how, to apply AI to solutions
that we risk a society where those benefits only address
the needs of those groups, of the few,
rather than of everyone.
So how do we address this?
Well, clearly AI education is the key.
And beyond AI education, the ability to create tool
sets that really empower anyone and everyone from kids
to seniors, say, to be able to create solutions
that matter to them with AI.
And my students and I've been doing this in the Media Lab,
building on the scratch programming language developed
by Mitch Wreslick also in the Media Lab,
and I want to show you a quick video of this vision.
So again, we mean empowering everyone, we mean everywhere.
--about controlling robots.
Yes.
As you can see, we're using a tablet.
Yes.
[INAUDIBLE] scientist sometimes.
So developing activities, toolkits
that allows kids to be able explore concepts and AI--
things like generative AI, rule based systems, classification.
So that was not the same as mine.
By interacting with these robots and playing around
with those algorithms, the ability
to use these intuitive blocks to be able to program a robot
to learn from demonstration in a really intuitive way,
and then also importantly to think about, how can
we think about tools that allow people
to design and orchestrate their own AI enabled experiences
in the context of their homes.
So we've been looking at adding extensions
to scratch about internet of things services,
about AI services that allow people now to create
sophisticated programs.
Hi, I had a very long day.
Oh, no.
Even things like sentiment analysis, person ID,
to be able to have these AIs collaborate.
Your favorite the Coffeehouse from Spotify.
To change your environment, your hue lights for instance.
And just think about how empowering that is.
So again, we really think about anyone and everyone,
from kids to seniors, being able to really customize
their world through these kinds of programming languages.
So I'm going to conclude now.
So again, my vision of AI is not just one
about brilliant machines, but humanistic machines.
And for us to be able to get there,
it's going to require a deep collaboration, not
only between science and engineering,
but design and ethics, real application, and many others.
And this is the reason why I think
the Bridge is so important and why I'm very
excited to be a part of it.
Thank you.