(upbeat music)
- [Speaker] I have the solution for the challenge.
I'm curious to see what did you come up with?
What does your list look like?
So it is important to see an image holistically
for all the information it's capturing.
So I had two questions for you in the challenge.
One is to see what information is it collecting
that is privacy-preserving and privacy-invading.
And other question is what information is it collecting
that you should not be sharing in the cloud
and sending around and might be against compliance laws
related to privacy.
Okay, so let us look at the picture.
I'm going to open the same picture.
So it's loaded the picture here
and it is doing the object detection.
That's pretty quick.
So it is recognizing five objects,
all people from the bounding box.
And in this case, Azure is not saying that "Hey,
I see a helmet or I see a board
or lights or anything else."
It seems to actually focus only on the six people.
And so it's easy to think we have a HJI,
we have a camera that's running this HJI,
it's recognizing five, six people.
And that's what this image is about.
This is where you take a step back
and you say, "This is what I want for my use case."
But what is being collected?
What other information is in this image
that the AI is collecting?
So think about this.
AI is not a static thing.
AI is constantly learning.
So today, you feed this information,
especially computer vision is a very hot area
of AI development
where vision algorithms are developing by the day.
And so what we are looking at is what it is seeing today.
The same model could be updated
to see more things in the future.
The same data that is stored could carry more insights
than it is actually showing outwardly.
So the things I'm seeing is I'm definitely seeing
the board and in the board, I'm seeing a lot
of different stickies and notes.
So it is possible that there is sensitive information
that should not leave this particular building
and should not go out into the cloud.
There's also personal information about people,
about the outfits they're wearing.
Two people in here seem to wear suits.
The others I think are in some kind of T-shirt.
It looks like a uniform.
I don't know if there is some kind of class divide
that maybe their managers
or different levels of work that they are doing.
Some people are wearing helmets,
they're all not wearing helmet in their head,
so it is showing their hair and glasses.
So it's giving a lot more information
and then it is actually giving their faces.
But GDPR we do not save person's face
because you can do facial recognition
and these days, it's becoming a digital identifier
to get you into the airport, to get you into buildings.
And so it is important
that this information doesn't get stored
and passed around in the cloud for the privacy
of these people and also compliance
of the company, which is doing that.
I'm also curious about these little rectangle things
behind this person below the board.
What are those?
So there might be more things than we are actually seeing.
And then if you take a step back
and say it's not just visually what I'm seeing,
the timestamp of this image of when it was captured
and the lighting is actually telling me
it's the night shift.
Are these all night shift people?
There must be something blurred in the background.
We could zoom in maybe
and it can show something else
that should not be in this picture.
So that kind of thing.
So you capture the people,
you capture the information about these people.
It actually shows their eye color.
Now I see this, it shows little bit more profile information
that definitely should not be passed around
and stored beyond identifying, you know,
six people are standing here.
So I hope you were able to capture this.
I hope this was insightful.
I want you to not think of this as, "Okay, I did a class,
I did an exercise and I learned about data privacy.
I learned about Edge AI and building cool stuff."
I want you to go past that
to develop a way of thinking
when you look at Edge ai, be it audio sound
and voice AI or images
and videos, what AI is seeing is our environment.
AI is trying to make decisions on her behalf.
So it is our responsibility
to actually be very responsible about
what we are leaving in this data, what we are allowing
for AI to capture and share and process and analyze.
And whether it is kept in the cloud
or whether it is kept in the edge
or it is going to be shared across businesses,
across partnerships.
So think about that and I want you to develop this thinking.
When you look at your environment
and you're looking at use cases,
not just think about the data that the AI is expected
to operate on, but all that the AI can glean.
And it's a very valuable skill
because it's definitely good for ethics and data privacy,
but also a valuable business skill where you can come up
with different ways by which you can create from the data
and then you stop yourself
and say, "Is this good value
that these people in the picture are going to allow me
and gimme permission to go
and create a business on top of it
or go make money out of it?
Or is this something
that is unethical and I should not be doing?"
So it's a way of thinking about value creation from data.
That's a lifelong lesson.
So I'm curious to see how you're going
to apply this entire course learning.
So beyond just this course, if you scroll down,
you can see a link to Sudha Live
that is my monthly live stream
where I meet my students and answer questions.
I would love for you to participate
and come tell us what kind
of things are you building, what have you learned?
What are you applying, what challenges are you facing?
Because I like to help my students.
And outside of that, I think there is also inside LinkedIn,
there is a business school of AI,
which is my learner community.
You can follow that and come and ask questions
and share the projects that you're posting.
We like to showcase our students and their work.
So remember, I'm here to help.