- [Narrator] Today's car comes with voice assist features.
You can add an Alexa voice assistant to some cars,
or you can use the voice assistant available in the car.
You can talk to the car.
Talk to the car?
Can you think of some examples
where you can talk to the car?
Those are great ideas.
You sure know how to talk to your car.
We can use voice to talk to any voice assistant.
They all have an invocation term like "Hey, Siri"
or "Okay, Ford"
or "Hey, Mercedes".
Can you think why we need an invocation word
when interacting with the voice interface?
The invocation word wakes up the AI to listen.
This ensures that the voice AI is not listening
to us all the time simply to protect our privacy voice.
AI also uses data and comes under machine learning
and it works with sound and audio.
Some of the things you can ask are looking
for driving directions.
Play your favorite music
or ask the car to change the inside temperature.
Mercedes allows you to start the car
and warm up the car remotely in cold weather.
Voice is an interface
to do something you can do with a click
or turning a knob,
something that lends itself
to a human-centric interface.
So think of voice as data being utilized.
Then think about the AI.
The voice AI, what does it do for the users?
And how to make this experience
delight the user in the product design
and to earn the user's trust?
Finally, it's important to know that AI has
to reside somewhere.
This is where it becomes edge AI.
AI can run like software from a web or mobile app,
or a device or a car.
Voice AI running on a car interface
or a device interface becomes edge AI.
Imagine this scenario, if the user wants to go
to Rambla Square in Spain and they ask the car
and it doesn't understand it,
and it wonders if it is Rambla Square in Timbuktu.
It is better for the AI application to ask respectfully
and be aware of the user's time urgency,
being respectful and clarifying
their ask might be a better decision instead
of driving them to Timbuktu.
This is why the data input being filtered correctly matters.
There may be a need for edge computing to process the data
to understand different accents
or to filter out background sounds
of multiple people talking all at once.
Machine learning models can do this very easily.
I gave you voice AI as an example
of edge AI that uses sound.
In the next lesson, we will learn to build an edge AI
that can be trained using voice or any audio sound,
and you can expand it past the car to more edge AI devices.
Let's go.