- [Instructor] Have you ever thought about
the internet on our devices?
We are so used to having access
to all information we need at the tip of our fingers.
Have you wondered about the process
of how something like artificial intelligence
makes its way into our devices?
I want you to join me to expand your thinking
of how artificial intelligence powers everything around us.
What about cars?
How are they processing information in real time
without always having access to the cloud?
In this course,
we will learn how that happens using edge AI
and how to build and create value
from the many different AI on the edge of a device,
anywhere from your home to industrial cameras
to connected and autonomous cars.
Edge AI is simply
AI running inference on the edge of any device.
This is about deploying AI to customers
who use internet connected devices, big and small,
and about getting customer data
to improve the AI to work for them from the edge.
It is not about AI being built on a mobile device.
The training of the AI can happen on the cloud or edge.
Autonomous vehicles have innovated farming
from manual toiling into precision farming.
Precision farming made farming digital
by calculating real-time data
from weather, soil, and historic data
to predict the right time to farm
with the right seeds, water, and fertilizers,
and the farmer gets to ride an autonomous tractor
that does tilling, sowing, de-weeding, and harvesting.
This is artificial intelligence at work
on the edge of devices,
such as autonomous vehicles.
Running inference on devices can come in many forms.
My favorite example is an autonomous warehouse robot,
or AMR, as they're known,
in a warehouse that loads items to be shipped faster
without any error,
that augments humans in warehouses.
When these robotic arms are making decisions autonomously,
that is edge AI,
using real-time data processing
to use sensors on the arm to complete its task.
So, you're not stuck monitoring every move.
Instead, you can trust the data that is uploaded
to this device to work for you
using the intelligence of AI to make decisions
to help with manufacturing, production, or mobility.
Edge AI can be found in mining,
collecting, and dumping minerals
that humans should not handle.
These are done by autonomous mobility vehicles,
also known as AMV.
We will talk more about AMVs
as we learn about deploying edge AI in future lessons.
All this is powered by AI,
AI that tracks sounds and listens to voices,
AI that does computer vision for cameras,
AI that combines sensors and haptics data
to work well with humans
in the workflow of factories, mines, farms, and roads.
Traditional AI, such as machine learning and deep learning,
is combined with generative AI
to understand and engage with humans.
Any of this AI is running in production as edge AI,
making sense of the environment and going past navigation
to support the very purpose of the devices.
AI is where we can innovate
by designing the human-centric AI
to fit into our workflow,
earn trust, and help us innovate.
All AI have common characteristics.
AI is trained by data,
AI makes predictions,
AI keeps learning,
and all AI works with humans.
There are many ways Edge AI finds its way to us.
We can find all of them in autonomous vehicles
or in autonomous mobility vehicles,
which we will use as example
to learn edge AI in this course.
In order for you to get the full experience of edge AI,
this is going to be a hands-on course.
We'll provide demos and challenges
for you to learn hands-on,
and you will find detailed instructions
in text files and handouts for reference.
I'm also going to teach you a framework
to understand the three components of edge AI
and the tech stack of autonomous vehicles.
You can apply the same to your favorite edge AI device.
Join me on this fun drive.