- When I think about the future of AI,
one of the deepest hopes I have is
that it helps each one of us overcome the limitations
of the constraints of our circumstances
and truly accomplish the aspirations
that we have for ourselves.
When I start relying on AI,
without thinking of it as AI,
when it is just natural,
it is something that I call the age of self.
My name is Prem Natarajan.
I'm a vice president in the Alexa AI Organization
and I lead the Natural Understanding area of work.
Every day I come to work with the anticipation
that we are going to do something awesome today
that advances conversational AI.
So what is conversational AI?
One level, the answer to that question is a simple one.
Conversational AI or conversational artificial intelligence
is the ability for a machine to engage
in natural conversations with a human interlocutor.
If AI is Mount Everest, then we are at first base camp.
The peak is now in sight.
We can start plotting some paths and say,
here are all the challenges that lie in our way
from here to there
or the opportunities I see for moving forward.
That's what the age of self in AI is all about for me.
The three pillars of the age of self in AI
are self-awareness, self-learning, and self-service.
Self-awareness is the first pillar.
When you have a multi-party conversation,
maybe you and your family want to order dinner together.
And so, you ask Alexa to join the conversation.
Say, "Hey, can you show me
the menu at this local pizzeria?"
In an ideal world, I would turn
around to my wife or my daughters,
or they would turn to me and say,
"What about these set of toppings?
What about those sets of toppings?"
At that time, you would like Alexa to simply be listening
and not participating in the conversation
because the conversation is actually happening
between the other parties who are part of that experience.
Now, this kind
of participation is a pretty demanding cognitive challenge.
When is it my turn to speak in a conversation?
When is it natural for me to participate in a conversation?
So, all of that kind of experience is only possible
with an AI that has a sense of self-awareness,
an awareness of its visual context,
of its ambient context, of historical context.
Self-learning is the second pillar of the age of self.
Self-learning simply put,
is the ability of an AI to continuously learn on its own
once it's been deployed out into the real world.
There will be a lot of such things that users want to do
to customize that are not possible
to configure Alexa in advance.
One crosscutting challenge for all kinds of AI is context.
There is personal context.
If I make an ambiguous request to Alexa, "Play music."
Knowing my personal preferences for music
might help Alexa choose jazz music.
A second type of context is ambient context,
which device is being spoken to?
Who is the person speaking to the device?
A third kind of context is dialogue or session context.
If I said, "What's the weather today?"
Alexa comes back and gives me an update
on the weather today.
And then I say, "What's it like in Boston?"
Based on that context, Alexa knows
that I'm asking about the weather today in Boston.
One area where context gets really challenging
is long-term context,
especially when the context shifts with who is interacting.
That is a particularly hard thing for an AI to do.
Affect is another important element
of the future of conversational AI.
A simple way of describing affect is it's
about how you say something, not what you say.
One of the ways
in which affect is already embodied in Alexa
is that when you whisper to Alexa,
Alexa whispers back to you.
It's picking up the style of your speech
and reflecting it back
in the way that it renders information back to you.
But one of the most exciting, new,
self-learning capabilities we have deployed
is this thing that we call teachable Alexa.
When you ask Alexa to do something,
that generally falls within the kinds of things
that Alexa's able to do, but there is something
about the way that this request was phrased
that falls beyond Alexa's ability to understand.
When you say, "Alexa, set the thermostat to 72 degrees."
No problem.
But now when you say, "Alexa, set the thermostat
to vacation mode,"
you get into an interesting outer distribution case
where nothing in the training data
that defined what vacation mode means.
If somebody asked us to do that, we would say,
"I don't know what vacation mode means.
"Can you tell me, can you teach me?"
And that's what teachable Alexa does.
Self-service is the third pillar.
Self-service is about how to make the AI more accessible.
I find this particularly exciting
because I think self-service is the way
to bring in broad engagement into the advancing of AI.
We launched this development framework
called Alexa conversations.
Alexa conversations is another important element
of our self-service strategy.
Alexa conversations is a developer framework
that dramatically reduces the amount
of data that a developer needs to bring.
Where we make it easier
for people to develop experiences that are powered
by AI without requiring them to be experts in AI,
without requiring them to know how to configure the AI
to get the best performance out of it.
Users can go set a routine of a sequence
of things where you gonna say, "Alexa, good morning."
Alexa can start your favorite news podcast,
tea kettle, coffee maker.
It can turn on the lights.
As we look out into the future,
we certainly want to keep pushing on the self-service aspect
so that the capability, the full power of AI,
becomes more and more accessible to people around the world.
When I think about the age of self,
these capabilities come together to enable a whole bunch
of daily conveniences for everyone else.
That to me is one of the most complete AI problems.
It's not just about intelligence.
It's not just about reasoning.
It's not just about knowledge,
but it's also about coherence.
It's also about affect.
That's what truly excites me about conversational AI.
It's not going to be solved by developing one algorithm,
but there is this overall system aspect
to it where many different functions have to be performed
and they have to be performed
in a way where they're all coordinated with each other
so that the end result is natural and seamless.
The challenges and opportunities that lay in front
of us are limited only by our aspirations.
And every time we make new advances in AI,
we recalibrate our aspirations.
They keep going higher and higher.
So, when I think of it as this trek
to the peak of Mount Everest,
and I feel like we're at the first base camp today,
when I look where we'll be in five years from now,
I think we'll have AI that is much more generalizable,
much more able to adapt
to its environment without human intervention,
much more capable of learning about the world,
maintaining a current knowledge of the world.
All of those together,
I think will take us to the second base camp.
10 years from now, who knows?
I mean, the sky's the limit.
(light music)