Hi, i'm Detlef Nauck,
I work at BT and I run BT's research
programme on artificial intelligence.
Today I want to talk to you about how we can use
artificial intelligence, or AI, to see the world.
You may have heard in the news over the last few years that people are working on autonomous cars.
These are cars that can drive through traffic without actually having a driver.
In order for this to work,
they need to be able to see the world.
They need to be able to see the road.
They need to be able to see other people in traffic.
They need to be able to see cars, pedestrians, cyclists.
They also need to be able to see traffic signs,
interpret them and understand what they mean.
So how can we actually do this?
How can we make AI see? How can we give eyes to AI?
In order to understand this, Let's look at cucumbers.
Cucumbers are sorted into different categories
depending on their quality.
So what could that be?
Is it like the size of the cucumber,
the shape, the colour?
It's actually quite a difficult problem.
There's a cucumber farm in Japan,
and this is run by a family and the son of the family,
he wanted to help his family to do this job.
It's actually so difficult that you can't train somebody up very quickly during
harvest time to help you sort cucumbers into nine different quality categories.
Only his mum was really able to do this properly.
So what he thought is, he wanted to use a computer to do that.
So he went on the Internet,
he got himself free AI software.
He took 7,000 pictures of cucumbers that his mum
had sorted into nine different categories.
He used some of these pictures to train the system,
and then he tested the system on the other remaining pictures,
and it turned out that his system was 95% accurate in
categorising cucumbers.
When he then built a system using a Raspberry Pi and a little
conveyor belt and a camera, to actually sort cucumbers into
different categories,
he found the system was only 70% correct.
This is a typical effect that happens when you train an AI system in the lab and
then you go out into the real world and try to make it work,
You might find that there's a discrepancy,
but eventually it worked out.
So he now has a computer system that can sort cucumbers
into nine different quality categories.
But there are a lot of other applications for using AI
to see the world.
Let's have a look at power plants.
AI that can see the world might also help us to
save the planet from climate change.
You know that power plants give off CO2,
but it's not always clear how much and the reporting about the amount out of
power plants is not always reliable.
These days we have satellites that go around the Earth all the time taking pictures of the planet,
and there's a team that has looked at is it possible, by looking
at power plants from space to train an AI system
to see how much CO2 they give off and it turns out that it is
possible.
So what you do is you take pictures of power plants from space, where you
know how much CO2 they give off,
and based on the exhaust fumes and
the way the power plants look,
they can then train an AI system to learn how much is being given off
by this power plant.
And then,
over time they can assemble an AI system that can be used
to monitor all the power plants on the planet and get a better
understanding of how much CO2 is being given off and then obviously use this information to
manage power plants and to shut down the ones that are giving too much CO2.
Another area where AI with eyes can help us save the
environment is if we use cameras to see what sort of
pollution we can find in the environment and then clean it up.
There's a team at the German Research Institute for
AI that uses drones with cameras,
and they have trained an AI system to recognise plastic
pollution in the environment.
For example,
they have a drone flying along the river.
The drone takes pictures of the river,
and when it finds plastic pollution,
plastic rubbish in the river,
it detects that, can even categorise it in different types of plastic,
and then teams can be notified to go retrieve it from the environment.
I'm in front of one of BT's broadband cabinets.
You can see these cabinets everywhere in the streets.
They contain the circuits that deliver broadband to your home.
This particular cabinet,
It still has some space left for more circuits,
so we could deliver more broadband to more homes using this cabinet.
The records we have about these cabinets are not
always up to date to the current day.
So if an engineer has come and installed some extra circuits,
we might not know how full the Cabinet is just now.
But here's an idea, we can use AI that can see,
we can take a picture of the Cabinet and then have an AI system tell us how
much space is left in the Cabinet.
So let's take a picture.
I've uploaded the picture that I took with my phone into our server,
and the server has now received the picture.
You can recognise the Cabinet that we have shown earlier,
and it has automatically detected the free space in this cabinet.
You can see these kind of lighter coloured stripes here,
the computer tells us that there are 133 millimetres of free
space in this cabinet every time an engineer goes to a cabinet they can take a picture
upload it,
And our data is always up to date.
I hope you found the examples about AI with eyes interesting.
And if you like,
you can try it out yourself.
you can go to this website teachable machine,
You can take pictures with your phone
and you can upload them and you can train
an AI system to recognise these pictures.
So go and have some fun with it.