- [Instructor] I'm going to teach you how
to understand Edge AI in an autonomous vehicle
through layers of tech.
We'll continue our learning so that you can apply this
to AI anywhere on the web, mobile,
or any Edge device beyond cars.
In this lesson, we will talk about warehouse robots
or autonomous mobility vehicles, AMVs,
and autonomous mobility robots, AMRs,
as they're commonly known in the industry.
So what is Edge AI?
The device can be any IoT device
or an autonomous vehicle, or robotica.
Edge AI means that the device has the processing power
to run the AI in inference on the device,
using the data from the edge device sensors.
This allows for edge computing
or more actions to process the data
by sending to the cloud back and forth
to improve the Edge AI.
The tech stack of an autonomous vehicle
is similar to the layers of Edge AI.
We will soon learn one important distinction.
The autonomous vehicle has many sensors.
These produce data about what it sees
and sensors in the environment.
Sensor fusion is the technology process
of combining the data from multiple sensors to make sense
of the road or the warehouse,
or what the autonomous vehicle is sensing.
This becomes the data layer.
The AI is computer vision
that is the core cognition layers.
This does object detection.
We have learned about this and done a demo,
and we will continue to learn this throughout this course.
The OS layer has more intelligence to use the data
and AI to make the car operate autonomously
with intelligence by utilizing the drive
by wire capability of the vehicle.
Let us look at warehouse robots
or autonomous mobility robots, the AMRs.
We can understand the tech stack
of an autonomous vehicle from this.
Just like Alexa making quick decisions at home,
warehouse robots are making quick decisions on the go,
all without the cloud.
Think about what a warehouse looks like.
When I think of one, I see endless shelves that are sky high
with dozens of boxes or items stacked, aisle numbers
and bright lights.
Every corner we turn that is inventory.
These are the things I notice with my eyes.
For warehouse robots, everything I mentioned before
is picked up by cameras.
These are the cameras that comes with the support
of additional sensors.
The AMR has lidars
and motion sensors to help the AMR navigate
by making sense of its surrounding.
Think of what these sensors are
for your favorite edge device.
These are not your average cameras.
These cameras are connected cameras,
and they work well with connected sensors around them.
These sensors pick up everything in its path
from shells to humans, and every corner
they're collecting and acting on data in real time.
The data from these sensors are able to analyze based
on built-in AI models to recognize shapes,
movement, and distance for example.
This helps the robot understand
its surrounding before moving.
Remember the software layer we discussed?
The software layer for Alexa or Google Home,
what do you think it would be for a robot arm?
Write down your thoughts.
Awesome ideas.
In a robotic arm,
the software layer is acting on the decision of the AI.
They manage all movement or hardware action.
Every wheel turn, arm lift, putting boxes
or packages in a spot.
This is all done by the OS layer.
The OS layer also makes sense of the data
to decide whether the data should stay in the edge
for edge computing,
or should be sent to the cloud to improve the AI.
We don't see the other layers,
but this is the layer which makes sure
that everything is running smoothly and safely.
That is the Edge AI.
The inference layer where the crucial AI decisions are made.
The data that has been cleaned and collected gets analyzed.
Where the model determines if the shelf is
around the corner, or is that a human crossing its path.
This layer is the key to making the AI work.
The three layers of Edge AI, AI, OS,
and data layer works seamlessly to help you make
the most of the device's capability
to solve real business problems using Edge AI.