Translator: Kevin S Reviewer: Denise RQ
Bang! Bang! Bang!
"You have to get out."
It's the pre-dawn darkness,
and my neighbor is banging on my front door,
warning me that the largest wildfire in New Mexico's history is approaching
a nuclear waste containment facility
perched above the only road out of Los Alamos, New Mexico.
Within minutes, I'm in my car with a few possessions,
one of thousands of refugees on the road to Albuquerque.
But this event, and leaving it all behind, can be a liberating experience.
And in my case, it changed me
from an academic scientist studying space
to an applied scientist building artificial intelligence technology
that can warn us of dangers anywhere here on Earth.
Danger comes in many forms.
As humans, we are really good at recognizing and reacting
to short-term crisis like that wildfire,
but we tend to be really bad at recognizing the slow changes
that indicate other types of threat.
And while we've been worried about terrorism, and war, and the economy,
the fact is
the threat of resource depletion and global climate change
are real and growing,
and the consequences are going to be profound.
And so to keep ourselves and our families safe,
we need to know the answers to some really hard questions
like "Is that forest near my house going to burn down?"
"Is the city in which I live going to run out of water?"
"Is there actually risk the regional food supply could collapse?"
"Do we need to get out?"
So, our basic technology for understanding the world around us is the map.
And we've been making maps for a very long time.
The United States is the most advanced country in the world for making maps,
but even we only update these maps every five years,
and we all have a sense that is far too slow a rate of change,
given the rate of change of society.
In the last six years, we've started to make maps
of the food supply here in the United States,
but just for the United States, only once a year,
and there's nothing like this for the whole rest of the world,
where the food production is now--
we're required to produce food for 7 billion human beings.
So what we need is a living, breathing atlas for the world
that's continually updated, and not just a map for now,
but a map that also includes the past, that goes back years and decades,
and allows us to detect those early warning signs of change.
And for the first time in history,
we actually have the right combination of technology
to allow us to make this type of map.
Some of the pieces for this technology have actually been around for a while.
In 1972, we launched a satellite called Landsat
which takes digital pictures, radios them back to the ground,
and the decedents of this satellite are still up there,
taking pictures of the world every day, every week.
But in 1972, the world's best supercomputer looked like this
- that's how people used to dress at national labs -
(Laughter)
and that computer is less powerful
than the smartphone that you probably have in your pocket.
So all we could do back then was to preserve the data for the future.
So the digital photos were converted to film,
and put in a fridge at a government lab,
and kept there for years and decades while the computing technology caught up.
And it was only a few years ago
that the U.S. government
in combination with some of the largest Internet companies
transferred all of this data back to the web,
where we can now all share it.
But the photos are not enough.
You need to turn the photos into maps
and unfortunately, there are not enough humans in the world
that know how to do that map making.
But, here is the second big technology:
we no longer need to do this by ourselves.
In the last few years, there has been a fundamental breakthrough
in our ability to teach computers to see.
Called machine learning systems, they are based on how our brains work,
and conceptually, they are very simple.
You start with a piece of software that knows how to modify itself,
you show it examples of what you want and what you don't want,
and you ask it to make decisions.
And if it makes the right decision,
you give the computer the equivalent of a cookie.
(Laughter)
And if it makes the wrong decision,
you give the computer the equivalent of a small electric shock.
(Laughter)
Yes; and over many cycles of training,
the system evolves to do what we want.
And once you've taught one program how to do this task,
you can clone that program
to thousands of other computers on the Internet
on what we call the cloud.
And that combination of machine learning algorithms
on thousands of computers
is finally able to process all the satellite data coming down every day.
So what does that look like?
If you look at the raw satellite data coming back every day it looks like this,
and it's a mess.
There are big holes because of clouds, and all sorts of noise, hard to interpret.
But when you show enough data to the computer,
we can start to turn it into this.
So what we're looking at here
is what we believe to be the first true real-time cloud-free view of the world.
Here we are looking at all of Iowa,
some of the most productive farmland across the United States,
and we can see the cycle of bare soil becoming plants,
which grow to maturity, are harvested, and return to bare soil.
And when you show it at this rate,
where each second corresponds to a bunch of days,
we're starting to look at the world as a single organism.
And the satellites have the potential to go beyond human biology,
so here we are actually looking at the world in infrared,
where the plants, and soils, and the building materials that we use,
have characteristic colors which allow us
to start to separate out things and not just look at the video,
but start to, for example, map out things.
So here we've taught the computer
to map out where the fields are all across Kansas.
You can go a step further,
and you can actually have the computer learn
how to estimate how much food is coming out of each patch of ground.
And so for the first time,
we've built a system that is faster, cheaper, and more accurate
than the traditional manual techniques
of estimating food crop across the United States.
And if you can understand the regular changes like this,
then you can also start to understand some of the unusual changes.
So very large hail storms
that cause hundreds of millions of dollars of crop damage,
or very large floodings
that cause the evacuation of tens of thousands of people.
And while all that agriculture that I just showed you
looks very impressive,
the thing to know about it
is the most productive farmland in the US is not being used for food for humans.
It's actually going to ethanol for our vehicles,
and to feed for our factory farmed animals.
And meanwhile, our food production has been shifted
to the Prairie States and to the dry Southwest,
where this scale of agriculture is only feasible
because of the existence of ancient underground aquifers
that filled up through rainfall over thousands of years,
but which we are now drinking down
at a rate where this whole system could start to collapse within 20 years.
And so machine learning systems that are in the cloud
that are able to process all of the satellite data in real time
will be looking for these patterns
of use and misuse of natural resources all over the world
which are the indicators of serious change
that triggers mass migration of human population,
famine, and local ecological extinction.
So that giant wildfire that happened back in 2000
was the biggest fire of its kind in at least 50 years of history,
but in the 10 years since that fire,
it doesn't even make the top 10 list anymore,
just for the state of New Mexico.
And in fact, when an even bigger fire
threatened the town of Los Alamos again in 2011,
I'm happy to say I wasn't a refugee a second time.
Between the two fires,
we'd built an earlier form of our machine learning system
that assessed the risk to the forest
and gave me the information I needed to make a decision
to move my family to Santa Fe where we were safe,
and where we were able to help others.
So someday soon,
- what I'd like to leave you with -
when you hear a bang on the door warning you of danger
and encouraging you to take action,
don't be surprised if that Good Samaritan is an artificial intelligent system.
(Applause)
Thank you.