(dramatic violin)
- Public opinion is gradually changing,
but they're still, many of them are skeptical.
We did have issues in the 80's and early 90's
with diseases and the use of antibiotics.
And the public opinion is shaped by some of these problems.
The situation now is very different.
- [Werner Vogels] Our planet and our civilizations
are changing faster than ever before.
Join me as I travel the globe, talking to startup founders
using technologies to make our world more interesting,
accessible, and livable. These are the entrepreneurs
that are creating the future we will live in.
This is Now Go Build.
Norwegians have been fishing since before
the agricultural revolution, thousands of years.
Of course, it's not just Norwegians
with their stunning coastline,
who have turn to the ocean to feed themselves.
Put humans near any ocean
and they've always pulled fish out to survive.
And thanks to advances
in the technologies used to spot, track, and catch fish.
The world's fishing fleet has become very good
at pulling out food from the sea.
Too good, left unchecked, without any doubt,
they will catch the last wild fish.
Add hydroelectric dams destroying salmon habitat.
Climate change warming and altering all of our oceans.
And it's not surprising that wild fisheries around the globe
are under incredible pressure.
And many are in rapid decline.
Today, fishing supplies
seven percent of the world's animal based protein
and the world's fisheries are the only example,
where we've industrialized the hunting of wild animals.
Wild fish accounts for more than 90,000,000 tons of fish
brought in by the global fleet every year.
But, almost equal to that massive volume
is the amount of fish that is not caught in the wild.
But is farmed.
In large part, you can credit Norway
and the Atlantic salmon for that.
In the fjord that define this landscape,
a mix of Norwegian culture,
a whole new set of technologies
are being deployed to bring more farmed fish
to a ever growing population around the globe.
As it turns out, growing the ideal salmon
for your favorite sushi roll is a data problem.
And one that can be solved.
But agriculture has its own set
of ecological problems to overcome.
From the concentrated waste of penned fish
to crossbreeding and disease infecting wild salmon runs.
The broader question – is there a way
of solving those problems also solves a much bigger one?
Is fish farming in large pens at sea the answer
to supplying salmon and other fish
to an ever growing human population?
Can we really solve the issues associated with fish farming
with better technology?
And can we do it without it creating
a much larger problem along the way?
Fish farming generates one kilo
of consumable protein for every kilo of feet.
If sustainability is the byproduct of efficiency?
Then it may indeed be a technology solution,
we're looking for.
To get a clearer picture,
I decided to speak to Sigurd Stefansson,
a professor of biological sciences
at the University of Bergen.
We decided to meet at Bergen's Central Fish Market.
What is the history behind fish farming here in Norway?
- [Sigurd Stefansson] Well, it's all going back to the late 60's, early 70's.
They picked up some
of the technology from Japan, the sea cages.
They were quite primitive at first.
But they develop into the large cages we see now.
They brought in wild strains of salmon in the late 60's
early 70's as a founder population.
So, we are now looking at the 13th,
the 14th generation of selected salmon.
They have been selected for late maturation,
fast growth, disease resistance.
- What are the biggest challenges for the fish farmers here?
- One that has to be mentioned first are the salmon lice.
These are little parasites.
They've always been on the wild salmon.
But the number of salmon now along the coast is so huge,
they are thriving in these conditions.
They multiple, they re-infect the salmon in the cages.
But the biggest concern is that these larvae
of the salmon lice also infect the wild migrating smolts
as they leave the river and into the oceans.
So, this is a major challenge.
We have an obligation to take care
of the wild strains of salmon.
- What are the biggest challenges around data
and data collection with respect to salmon?
- So, in order to get an idea
of the salmon lice infestations,
a number of fish are actually removed from
the cages and the lice are counted.
If you could see a system
where the fish wouldn't have to be handled,
you would have a video of the fish.
And if they can spot the salmon,
either manually or automatically,
this could be a major improvement
in the surveillance of the sea lice population.
Near Bergen, one of the epicenters of agriculture,
the company Aquabyte is using machine learning
and high tech cameras
to begin solving some of these problems.
I wanted to meet the founder of Aquabyte
and see how they were approaching some of these challenges.
So, tell me what are going to see today?
- [Bryton Shang] Yeah, so, we're going to Blom Fiskeoppdrett
which is one of the farms that we work with.
It's in the middle of the fjord,
the three pens in the salmon farm and trout farm.
- So, if the pens are out there in the water?
Where's your technology?
Is it here on land or where is it?
- Yeah, it's in the barge, by the pens.
- Well, let go and have a look then.
So, is there anything particular
about the Norwegian workers
that makes it very good for fish farming?
- [Vidar Onarheim] Two things, we've got a lot of fjords,
which is perfect for sheltered locations.
So and of course, there's a pure and clear water.
- So, the waters are really calm here.
Is it always like this?
- No (chuckle)
So, tell me what we're seeing here?
- [Bryton] Yeah, so this is a typical salmon pen in Norway.
It's a hundred-sixty meters in circumference.
So, the diameter is about half a football.
Each pen has anywhere from a hundred
to two hundred thousand individuals at a given time.
So, in the middle of the pen
is the feeder and it's constantly dispensing feed.
So, the fish are coming up to eat.
They are feeding all day,
so you can see the pen
is actually spraying out feed right now.
- [Vidar] We're watching with camera from below,
so there's an operator remotely
that's watching the fish while it's eating.
So, if there's any pellets
that comes through the screw and such,
then they stop the feeding.
- [Werner] Why are they jumping above water?
- Well, they just enjoy it, I think.
- [Bryton] Here we are on a feed barge.
There's someone here all day
and their full-time job is to monitor the pens.
They're watching for pellets and dispensing the food.
Part of the vision for the company
is to ultimately get to a place
where we can take a lot of that farmer's intuition
and automate it using software.
- So, tell me where did you get the idea?
How did all this get started?
- I actually grew up in Ithaca, New York,
studied at Princeton,
studied operations research financial engineering.
After that, worked in Wall Street in algorithmic trading.
So, building models to buy and sell stocks.
After that, got into a series of startups.
The last of which was involved in,
cancer detection, using computer vision.
And so, this company kind of borrowed elements
of the previous two companies
and that a lot of what we're trying to do
relates to computer vision and optimal decision making.
- Okay, cool. What was your first engagement
with a Norwegian fish farmer?
How did you convince them
that this is the path that they needed to go?
- Well, obviously the technology itself is quite complex.
But from my perspective, it's what it does.
It's replacing manual, tedious work
with some kind of automized system.
- With most beginning data sets in machine learning
you need humans to sort of label it
or to indicate the kind of things that you're looking at.
I can imagine how to work with one fish,
but you have 200,000 fish in a pen.
And 50 meters deep and with different weather circumstances.
What were the particular challenges that you had to solve?
- So, first, I think is just the challenge
of being to be able to get a camera in the pen,
have the right optics,
there's a lot of challenges with protocols in the water
and light scattering and various things like that.
Once you're able to get the image of a single fish,
then it's being able to sample lots
of fish throughout the day.
- So, this is your camera?
- Yeah, so this is our first RND camera.
It's a stereo camera, which allows us to perceive depth
and we're using that depth information
to help us determine the size of the fish.
The feeding kind of goes in a circle
and so the fish are swimming
in a torus in the pen, around.
So, we're putting the camera facing parallel
to the fish as they swim by.
- [Werner] So how much software
is running on the device itself?
- [Bryton] Yeah, so there's a microprocessor
on the camera as well.
That's doing some light filtering.
Some of the images we capture,
the fish is partially in frame
or it's blurry or it's dark
and so, we try to filter the images
as close to the source as possible.
- [Werner] Solving a problem that affect millions of fish
at microbial level requires collecting data
far beyond the ability of the naked eye.
Collecting robust data sets from the middle
of a Norwegian fjord is hard enough.
But, the processing of that data happens in the Cloud,
which requires connectivity.
Every solution creates more problems to solve.
- [Bryton] So, these are antennas
and they provide connectivity from the pen to the barge.
- So, you sent your data over that.
Do you store it here?
- Right, so, we do some processing at the pen,
some processing on the barge.
- [Werner] Okay and then you bring it
out into the cloud as well.
So, you must have connectivity
from the barge to the mainland?
- Right, so the barge also has internet.
So, we can get a couple hundred megabytes per second.
- Very good, out here. - Yeah, pretty good.
- So, lice, I understand is one of the biggest challenges.
So, tell me about recognizing lice in the water or on the fish?
- Absolutely, so, the salmon lice
is a big problem in Norway.
It's actually 25% of the cost of running one of these farms.
So, the lice it's natural occurring,
but in these fish farms because the fish
are close together, they can often have infestations.
And so, we're being able to take a picture of the fish.
Understand when a farm needs to make a delicing decision.
- There's regulations around tracking for lice, as well.
So, how did it traditionally happen?
- So, this is regulated in Norway
by the Norwegian Food And Safety Organization.
They require each of the farms
to be able to sample every week,
10 or 20 fish from their pens.
Right now, they go out to the pens,
they net the fish, they anesthetized them
and then count the sea lice one by one.
And this is a pretty tedious process
for a farmer to go out and do this.
And it's also harmful for the fish
because you're handling them.
- But, it is also statistically insignificant.
I mean, 20 fish out of what is it per pen, 200,000.
It's not really a representative sample.
Have you guys been talking to the Norwegian Government
that actually your mechanisms are way more accurate
than the hand counting of sea lice.
- Absolutely, the government's quite positive
about the solution
because it reduces the handling of the fish.
It's more accurate
and we're on track to be the first device
approved for electronic replacement
and dispensation at this farm.
- Okay, so image recognition
plays an important role in all of this?
Are these algorithms that you've developed yourselves?
- Yeah, so, the basis for the computer vision,
image recognition is based on a convolutional neural network.
And so, we're using similar technology
that they've used at social media companies
and autonomous vehicle companies
and adapting that for the underwater domain.
And so, a lot of the same models
to recognize an animal or a human face.
We've adapted it to be able to identify fish in the image.
Identify various body parts,
being able to uniquely identify fish
and other things as well.
- Your secret sauce really here is,
in the processing of the data?
And the collection of data over time.
So, how did you get to the original data set?
- To start off for the sea lice counting
and for the weight estimation,
we use a human in the loop approach.
So, what happens is before we have any data set.
We'll go and we will capture an image.
We'll have one of my team members former fish farmers
go in and annotate the sea lice
or annotate various body parts.
And when they do they do that,
they're generating training data
that provides a training data set for the model.
And so over time, we're accumulating a large data set
that more and more of that can be automated.
- You were going to share
what is your weight distribution, as well.
- Yeah, so the second thing we focused on
was the weight distribution.
So, ultimately farms like this,
they're selling to the Whole Foods, Costcos of the world
and they have contracts in advance for certain sizes.
And so, we can see the number of fish in each weight bucket.
As well as how much the farm has already sold.
And so, here they know
if they're actually growing the right amount
or if they oversold or undersold.
- So, just over here, mind your head.
All right and this is where the fish food is coming from.
It's contained here in the silos.
It going down into the doser system
and through out to the pens.
- [Werner] Okay.
- And it will be like this.
Yeah, this is what it looks like.
- So, what is this made of?
- It's about 70% vegetables
and soy proteins, really, and it's 30% real.
So, you should try.
- I'm good. (chuckle)
- For us, it's part of a larger,
kind of initiative towards food sustainability.
So, if you think about the earth
is largely covered by water versus land,
and it's one of the most energy efficient ways
to produce protein in contrast to chicken or beef.
And so, it's almost one-to-one,
in terms of kilograms of food per kilogram of feed.
If you look at world's food supply
is generally growing one percent year over year.
Fish farming is growing six percent year over year
and this has kind of matched the growing protein deficit.
- The lack of protein and the deficit of protein
seems to be a rising problem.
- So a lot of it is driven by population growth
and also a change in consumption habits in countries
like China and India,
where you have the rising middle class.
It's in these places
where they just need a lot more high quality protein.
- And this is an extremely
efficient sustainable mechanism of creating protein.
- Yeah, the problem is you stand at the side of a pen,
you can only see maybe five percent.
The fish are relatively unobservable.
So, this is the first time
they've been able to see in the pens
and see how much they're actually producing.
- [Werner] How do we reconcile the environmental challenges
of industrialized fishing in agriculture?
How do we continue to supply a much needed source of protein
without wiping out salmon all together?
There's rarely one answer to a problem
and this problem is no exception.
As the world's population grows
and we try to make up for lost time
in learning how to manage our finite resources.
It is thinking like Aquabyte
That will help us to return
to being good stewards of earth and sea.