- So this lights gonna go on
and that's gonna light up.
- The anticipation.
- There you go.
Triggering.
- I pushed a button.
- [Intercom] Triggering.
(beeping)
(loud metal popping)
- So what is that sound we just heard?
That's the sound of $150 million plasma collider,
blasting jets of super hot ionized gas
at hundreds of miles per second.
We're at TAE Technologies, a 20 year old start up,
that hopes to finally achieve nuclear fusion.
It's the same energy that fuels the stars
but harnessing it here on earth
has been a scientific dream for decades.
Fusion power plants would mean plenty of energy,
no carbon admissions, little long term radio active mess
and zero chance of a Fukushima-like meltdown.
Governments, universities and private companies
have been pursuing fusion since the 1940s.
We're still nowhere near adding a fusion power plant
to the grid,
but in February 2018, a panel of experts published a list
of game changers for fusion research
and at the top of their list were advanced algorithms
like artificial intelligence and machine learning.
One of the private companies chasing fusion is TAE,
which began collaborating with Google a few years ago.
Together they developed machine learning tools
that TAE thinks could finally bring fusion within reach.
So we headed to a nondescript stretch
of Foothill Ranch, California.
We wanted to see just how far along TAE Technologies is
and where, if anywhere, AI enters the picture.
- So right now we're looking out towards the main vessel,
the experiment.
See all the blue cabinets around,
they are our power supplies.
Primarily for the magnets
which help to control and shape the plasma
that we are heating.
- [Rachel] Our guide is Erik Trask,
a lead scientist at TAE Technologies.
He's taking us on a tour of Norman,
the company's plasma collider.
Inside magnetic fields propel and control
the super hot spinning clouds of ionized gas called plasma.
While particle beams heat it to temperatures hotter
than the sun's core.
- The goal of this machine is to show
that we can heat up our plasma
to high enough temperatures
to show that it is relevant
to make a fusion power plant out of.
- [Rachel] All attempts at fusion involve
smacking lightweight particles
into one another at temperatures hot enough
for the particles to fuse.
That produces a new element plus energy.
In the starts, fusion occurs when hydrogen nuclei fuse
to form helium.
There are a few different ways to try and replicate
this process on earth.
Some experiments control the plasma with magnetic fields
inside a massive metal donut called a tokamak.
TAE twirls the plasma inside a linear machine named Norman.
Sounds great but none of this has produced enough energy
to be useful for a power plant.
(loud metal popping)
A machine in the UK called Jet did set the world record
by generating 16 megawatts of fusion power
but it took 24 megawatts to heat up the plasma
and even more electricity to contain it.
So the whole thing was a net loss of energy.
TAE's Norman machine produces some fusion reactions
but not many according to the CEO Michl Binderbauer.
- So things get hot enough that yes occasionally
a few elements actually collide hard enough
and you get fusion.
And we actually use this for diagnostic purposes.
Now this isn't bulk fusion yet, right.
So this wouldn't be economic.
This makes a handful of events.
But as we go up the scale eventually we'll get there.
- [Rachel] That's the goal for the future,
a fusion reactor that runs for months at a go.
For now, the goal is to produce a plasma
that's 30 million degrees Celsius for 30 milliseconds.
To get there the team fires Norman 50 times in a day
for just milliseconds at a time.
Each shot they may tweak thousands of variables
like the timing, the shape of the magnetic field,
the voltage of different power supplies
or the amount of plasma.
Since the machine came online,
it's fired over 11,000 shots,
each one a bit of a blind stab at the optimal variables.
- And we have a fault on N1N.
Do we know what happened there?
Stopping shot.
- So what happened?
- So there's potentially a resistor that's gone bad
or a board that is reaching an over voltage
and it's tripping something inadvertently.
- [Rachel] It's impossible for a person
to keep all those variable in their head
or to change them one at a time.
That's why the team is collaborating with Google
using a system called the Optometrist Algorithm.
- The advantage of one variable at a time
is you know exactly what happened and why.
Which makes prediction and model building very easy.
You can do that as long as the number of variable you have
is not very large.
How do you do that in four dimensions?
Or 20 dimensions?
- [Rachel] The algorithm is what lets them change scores
of variables in each shot.
If the team thinks the new combination produced
better results than the last one they pick one path
of pre-programmed shots.
If it's worse, they pick another,
hence the name optometrist.
Is shot one or shot two closer to viable fusion?
Three or four?
654 or 655?
According to Erik, what's happening here
is really machine learning rather than AI.
Right now, it's still the humans making the call
about what's better and what's worse.
- We've gone through countless classes
in undergrad and graduate school,
time in the lab, so our knowledge is being used
to produce data.
At some point, an AI system could be used,
could be trained on the knowledge we're gathering.
We're not quite at that stage yet.
- [Rachel] We asked some other fusion researchers
what they thought of TAE's approach, Julian Kates-Harbeck,
who worked with Princeton's physicals laboratory,
and Christina Rea at MIT.
They use machine learning a little differently
but they're intrigued by TAE and the optometrist.
- This algorithm supports human decision making
but doesn't take away the human out of the loop.
So it's sort of this beautiful synergy
between the human and the machine.
- [Rachel] Julian and Christina
have each developed algorithms
to predict when the plasma might misbehave
in order to keep it under control.
The methods differ but Christina says fusion experiments
are just so data heavy
that it's natural for machine learning
to help here and there within reason.
- There are many low hanging fruits in our field
that can really benefit from using this advanced statistics
that lies under the hood of AI in general.
Of course, we are not getting to the point
where we are building a self driving tokamak.
- [Rachel] These teams all see machine learning
and AI the same way,
as important tools just not as a panacea
to all of fusion's problems.
Right now there is no such thing.
The TE team is still thinking in milliseconds
and looking for one good day at a time.
- If we can get there in one day, great.
It probably will take more than one day.
- [Rachel] Is that a good result,
the plasma 20 million degrees for 10 milliseconds?
- That would be terrific, yeah.
Champaign would be flowing by now.
- Hey everyone, this video is actually part
of the Real World AI, a special issue here at The Verge.
This week we're bringing you all sorts of stories
about how AI's being used in the world today.
So if you want more go to The Verge's YouTube channel
or go to theverge.com/AI.