Minks are endangered.
Their biggest threat is poaching. There are only 1000 left.
And, if they become extinct,
the ecosystem in the Danube Delta will become
very damaged.
Since this is a classification problem
we will use an image recognition model.
Naturally, it is similar to the KNN model.
We will run the images the camera gets through our database
and if it finds a match
the user will be alerted.
Here we have a demo video.
And I'll run you through it.
So, this is what the camera sees right now.
It just detected a heat signature and it's tracking it.
The line at the bottom represents
the area it's not supposed to go in.
Now the tracker turned red, as you can see.
The AI detected that someone is inside.
And the user now sees this, sees a human
and alerts the authorities.
What will AI solve in your problem?
And how will it solve this problem?
In the areas where we will have the cameras
there aren't supposed to be any humans whatsoever.
None.
So, if there are any it means they're a poacher.
Or even if they aren't, they're not supposed to be there
and should be taken away.
But you were very clear about what the building blocks
were on the sequence.
Firstly, we need to train the AI to recognize hives.
And next find areas where bees live
or where they could be located
compared to dangerous areas.
Then we calculate the approximate number
of saved bees by our invention.
And take measures to preserve the bee populations.
I saw that in your presentation you showed that
you will classify the image
then give it to the decision tree
and then get an answer using the Machine Learning algorithm
and get the final prediction.
How does that system work?
You were systematic about how you would collect data.
You had an explicit link to the sustainable development goals.
The program of Artificial Intelligence will assist us in:
Comprehending which data values have a positive effect
on the species population.
Looking for possible locations in the country
which might fulfil the geographical requirements
and therefore favor the thriving of the particular species.
Inserting the data for its location
so that the neural network can pick out the best location
for the construction of a park.
Our main goal is, of course, saving millions of animals,
preventing fires that would cause damage to our environment
and also cause damage that we would have to financially fix.
We're going to use a mix of categorization
to help sort different species.
And then to analyze how many types
and what types of species there are,
in particular migratory paths.
As you can see on the graph the poacher
comes to the camera,
the camera detects it and the camera sends the data
to the network, in the background.
And the network receives the data and identifies the points
to send the data to the loudspeakers.
And the loudspeakers will catch the data
and emit gun sounds,
bullet sound to scare off the animals and move them
away from the poachers.
I would like to ask how would the AI identify poachers
from normal people?
Yeah, great clarity on the problem. I think you recognized
that image recognition will be useful.
And there was a very good breakdown of the hardware
and the software side of it.
So, that would certainly attract funding from
an organization maybe like the World Wildlife Fund.
Alright, that was absolutely brilliant. I love it.
So, the winner is Geoffrey Hinton with Princess,
Entrepreneur, Scientist, Mathematician, Tinfoil.
Yaay!