[MUSIC].
>> Hey everyone. We are here in Paris,
where it's an amazing time to be a developer.
I'm Seth Juarez, Cloud advocate at Microsoft
focused on AI and Machine Learning.
Today, we're working from
the Microsoft Paris office and
the AI school classrooms where we'll learn about
an NGO here called Surfrider who's looking to
stop pollution in the ocean by getting it at the source,
the rivers. Let's go check it out.
[MUSIC].
>> We are here in
the Paris offices with my wonderful colleague, Chris.
So tell us about the cool projects you work on.
>> So one of the cool project we are working on is Surfrider.
Surfrider is an NGO who is trying to protect the oceans,
and we have them scale our efforts.
>> Fantastic. How does this project come about?
>> So we partner with the Microsoft AI school in
Paris to help create an AI to detect mapping pollution.
>> I'm here with the project manager
of Surfrider in Europe, Antoine Bruge.
How you doing? Where are you at my friend?
>> Really great. I'm here in Biarritz.
>> So tell me about all of the projects that Surfrider does.
>> So globally, we are working on three key metrics.
We are working on climate change,
we are working under water quality issues,
and also on plastic pollution.
So plastic pollution is really a global program.
Everyone knows that there is this plastic issue in the ocean,
but what people doesn't know is that most
of the plastic pollution comes from rivers,
from inside inland, from our cities.
For instance, if you lose a bottle in Paris,
this bottle might end up in the same river,
which in the river crossing Paris,
and the bottle might end up into the sea, into the ocean.
So the project I'm now working on,
our aim is to map the plastic pollution of rivers,
to know which rivers are the most polluted ones,
and to be able to know where we
should act first to have more impact.
I guess you know that scientists sometimes when they work at sea,
they used to put an observer at the front
of the boats, and this observer,
his role is to collect the GPS position of every,
can be liter items, can also be mambas,
can also be turtles,
and we do want to do the same in the rivers.
So we started to do the same on the riverbank using dedicated app.
Now we want to go faster and we want
to be more efficient in our data collection.
We decided to present the project I'm working on to
try to record the riverbanks on
video and to create and develop
that artificial intelligence algorithm that could
be able to detect any riverbanks liter items.
>> I see. So basically,
you're making the lockout that
scientists use to have at the front of the boat,
you're making that into an AI that can see these things for you.
>> Yes. Exactly. It is super important because
if we manage to map the plastic pollution of rivers,
we will know where we can act,
and that's super important to know if we have to do more
or if what we are doing is enough and if it's working.
>> How is Microsoft got involved in the project?
>> We took part into the ShareAI program,
which is a program from Microsoft,
to give the opportunity to NGOs and
not-for-profit organization to use and developed AI technologies.
Microsoft gave Cloud project
to a school working on artificial intelligence,
and we had about nine students working on
the project and did develop a first model.
[MUSIC]
>> All right. Evangelina, thanks for spending some time with us.
So tell us about yourself.
>> Okay. I'm an engineer.
I came from Argentina.
I came here to Paris to do studies in
master degree in social science substations climate change.
>> So what stuff do you work on during
the seven to eight months that you're at the AI school?
>> You learn everything you can about AI Machine Learning,
deep learning, how to create solutions.
Also, for companies in the industry sector,
also to social problems, environmental problems.
We did object detection,
we learn how to work in NLP.
The idea during the training shift was not also do some stuff,
it was to work in real projects with real people.
>> I see. So ShareAI brought some projects that were
actual real projects of
things you could work on and you chose Surfrider.
>> So for me was something connected
with things I used to do the best.
It is related to climate change also.
So that's why I found the place
where I can continue learning how to develop myself in AI,
develop the technical skill,
but also to create solutions to a real problematic.
[MUSIC].
>> Eighty percent of the pollution
from Seine come actually from the rivers.
>> I see. So that's why you want people
actually taking pictures of
what's on the rivers so you know where it's coming from?
>> Yes. That's why.
>> Yes. Just here is a part of the Seine riverbanks,
Seine was the biggest rivers we get in France.
So obviously, it's one of the most contributor
to ocean plastic pollution as well.
>> When you're in the city in Paris and
you trash cigarettes or you trash plastic bag,
it ultimately ends up in the Seine river
and then go all the way to the Atlantic ocean.
>> So usually people would come out
here and have to stain all the trash there,
but now with AI,
you'll be able to just snap
a picture and then I know where it is and where it's coming from.
>> If it's what kind of trash it is and count it,
and as we have the GPS position,
we can add it to the map.
>> That way, you start to see on the actual map where
all the trash is literally coming from
so you could send people to clean it up there.
>> Yes.
>> Well, I'm glad we're helping out with it.
>> We are trying.
>> I'm not going to stop seeing this.
>> No, you will never stop.
As soon as you're starting to pay
attention to exclude illegal things,
you can see trash everywhere in Paris city.
[MUSIC].
>> How is AI involved in all of these?
What kind of AI solutions would you make for Surfrider?
>> Again, the idea for Surfrider was to create
open source application to identify waste in the river.
So we had datasets with different images from the river,
waste from the river that they take while you were doing project.
They gave us that and say, "Okay.
We want to do application,
where we can map your mapping
of everything that we can find in the river."
We start researching about
different projects or different models that we can develop.
We say, "Okay, for sure,
we are going to work with ocean detection
because we want to find different objects in a photo,
in a video, indicating different objects say,
if it's a bottle, or it's a fragment, or something else."
So we decide to work with TensorFlow.
We picked faster R-CNN,
and for the model,
we tried resonate one and resonant 50.
Then we train the model and we have
the first outputs of the model.
>> So listen, you actually sent me the codes.
So I want to show you what I did because I wanted
to see if I can help out in any way with Azure.
Okay. Let me show you what I've done.
First thing I did is, because he wrote this code
all in Python using TensorFlow,
is I forced the GitHub repo that you sent.
What I've done is I've added just a couple of lines of
code so that I could make this run in Azure Machine Learning.
Now hopefully, I'm adding value generally,
and I don't know if this is the same in your case,
it's really hard for a bunch of
data scientists to work
together because they all have their own machines,
they all have their own environments,
they spend stuff up,
and it's hard for us to keep track of as
a team everything that we're doing.
So in this case,
you can see right here that I have
two runs inside of Azure Machine Learning,
and each of these runs represents you running your actual code.
So any data scientist on your team can
go in here and see any run that anybody ever did.
Here is the detail of one particular run that I did,
and here you can see it was
45 minutes to actually run through this code.
How long does it take for you to run generally?
>> About three hours.
>> Okay. So far we're doing a little bit better.
So notice that the compute environments inside of
Azure Machine Learning are designed to
help you run this a little bit faster.
I'm running things on these machines called NC-12,
which have two GPUs on each machine.
So you can do a lot more training.
There's two more things I want to show you.
The second thing is sometimes it's hard to know
if your Machine Learning model is doing well,
and one of the measures that we always use,
and you know this because you're a data scientists,
is the cost function.
Generally, I want the cost function to go down,
and so you can see on this Metrics page that we
have the chart showing the cost function going down.
You were probably using TensorBoard.
>> Yes.
>> This is also available because we save all the TF events,
which is a file that holds the charts.
You can still run TensorBoard if you like.
But then you also have this available inside
for other data scientists to visualize, which is really cool.
The last thing I want to show you, if that's okay,
is generally when you run these things in the Cloud,
it feels like you're losing
a little bit of the control because you'd like to see the output.
We actually store the output of
every single run you do of this training algorithm,
and you can see right here as I scroll down,
all of the output that you would
see from your machine learning training.
So you will not lose any of
the ability to see exactly what's happening.
So hopefully this is helpful.
>> Yes, it is.
>> So can I maybe be
part of the team and then help out a little bit?
>> Yes. Sure. You are welcome.
[MUSIC].
>> So what's the future for
Surfrider in Europe and maybe globally?
>> So the future for Surfrider will be able to map all rivers.
First, starting by France,
but then we want to move to Europe and then
we hope to expand to the world.
It will be a way to classify the rivers
from the less polluted ones to the most polluted ones,
and to really be able to say,
okay, we should work first on those figures.
>> So tell me more about how you're going to do that.
How would you solve that problem?
>> We need a lot of help.
First, we need help to collect and grow our datasets.
So we need people to take pictures,
send us the pictures,
and you will be soon able to do that from
a specific and dedicated app
that is going to be called plastic origins.
We are always looking for data scientists and
also people able to dig up API.
It is our big challenge now.
To be able to link every items from the app to the database,
to the data visualization.
We are also looking in the future for people
that can go into the field to record the riverbank,
and it's how we are going to get our data from.
>> Well, thank you so much for what you do.
I'm excited to participate.
Can I volunteer?
>> Yes. Of course, you can.
>> I love it.
>> Thanks a lot.
>> Thank you so much for the call,
I will see you soon my friend.
>> Thanks a lot. Bye.
>> Thank you so much for watching.
If you're interested in the AI school,
go to aischool.Microsoft.com.
If you want to learn more about how to make
your own custom Machine Learning models, go to ml.azure.com.
It was really awesome hanging out with the Surfrider folks.
If you want to learn more about them, go to Surfrider.edu.
Be sure to like and subscribe the code stories to see what else
we're up to here in Paris and where we go to next.
[MUSIC].