The Ultimate Optimization Problem: How to Best
Use Every Square Meter of the Earth's Surface
Lucas Joppa, founder of Microsoft's AI for Earth program, is taking an
engineering approach to environmental issues.
Lucas Joppa thinks big. Even while gazing down into his cup of tea in
his modest office on Microsoft’s campus in Redmond, Washington, he seems
to see the entire planet bobbing in there like a spherical tea bag.
As Microsoft’s first chief environmental officer, Joppa came up with the
company’s AI for Earth program, a five-year effort that’s spending US
$50 million on AI-powered solutions to global environmental challenges.
The program is not just about specific deliverables, though. It’s also
about mindset, Joppa told IEEE Spectrum in an interview in July. “It’s a
plea for people to think about the Earth in the same way they think
about the technologies they’re developing,” he says. “You start with an
objective. So what’s our objective function for Earth?” (In computer
science, an objective function describes the parameter or parameters you
are trying to maximize or minimize for optimal results.)
AI for Earth launched in December 2017, and Joppa’s team has since given
grants to more than 400 organizations around the world. In addition to
receiving funding, some grantees get help from Microsoft’s data
scientists and access to the company’s computing resources.
In a wide-ranging interview about the program, Joppa described his
vision of the “ultimate optimization problem”—figuring out which parts
of the planet should be used for farming, cities, wilderness reserves,
energy production, and so on.
Every square meter of land and water on Earth has an infinite number of
possible utility functions. It’s the job of Homo sapiens to describe our
overall objective for the Earth. Then it’s the job of computers to
produce optimization results that are aligned with the human-defined
objective.
I don’t think we’re close at all to being able to do this. I think we’re
closer from a technology perspective—being able to run the model—than we
are from a social perspective—being able to make decisions about what
the objective should be. What do we want to do with the Earth’s surface?
Such questions are increasingly urgent, as climate change has already
begun reshaping our planet and our societies. Global sea and air surface
temperatures have already risen by an average of 1 degree Celsius above
preindustrial levels, according to the Intergovernmental Panel on
Climate Change.
Today, people all around the world participated in a “climate strike,”
with young people leading the charge and demanding a global transition
to renewable energy. On Monday, world leaders will gather in New York
for the United Nations Climate Action Summit, where they’re expected to
present plans to limit warming to 1.5 degrees Celsius.
Joppa says such summit discussions should aim for a truly holistic
solution.
We talk about how to solve climate change. There’s a higher-order
question for society: What climate do we want? What output from nature
do we want and desire? If we could agree on those things, we could put
systems in place for optimizing our environment accordingly. Instead we
have this scattered approach, where we try for local optimization. But
the sum of local optimizations is never a global optimization.
There’s increasing interest in using artificial intelligence to tackle
global environmental problems. New sensing technologies enable
scientists to collect unprecedented amounts of data about the planet and
its denizens, and AI tools are becoming vital for interpreting all that
data.
The 2018 report “Harnessing AI for the Earth,” produced by the World
Economic Forum and the consulting company PwC, discusses ways that AI
can be used to address six of the world’s most pressing environmental
challenges (climate change, biodiversity, and healthy oceans, water
security, clean air, and disaster resilience).
Many of the proposed applications involve better monitoring of human and
natural systems, as well as modeling applications that would enable
better predictions and more efficient use of natural resources.
Joppa says that AI for Earth is taking a two-pronged approach, funding
efforts to collect and interpret vast amounts of data alongside efforts
that use that data to help humans make better decisions. And that’s
where the global optimization engine would really come in handy.
For any location on earth, you should be able to go and ask: What’s
there, how much is there, and how is it changing? And more importantly:
What should be there?
On land, the data is really only interesting for the first few hundred
feet. Whereas in the ocean, the depth dimension is really important.
We need a planet with sensors, with roving agents, with remote sensing.
Otherwise our decisions aren’t going to be any good.
AI for Earth isn’t going to create such an online portal within five
years, Joppa stresses. But he hopes the projects that he’s funding will
contribute to making such a portal possible—eventually.
We’re asking ourselves: What are the fundamental missing layers in the
tech stack that would allow people to build a global optimization
engine? Some of them are clear, some are still opaque to me.
By the end of five years, I’d like to have identified these missing
layers, and have at least one example of each of the components.
Some of the projects that AI for Earth has funded seem to fit that
desire. Examples include SilviaTerra, which used satellite imagery and
AI to create a map of the 92 billion trees in forested areas across the
United States. There’s also OceanMind, a non-profit that detects illegal
fishing and helps marine authorities enforce compliance. Platforms like
Wildbook and iNaturalistenable citizen scientists to upload pictures of
animals and plants, aiding conservation efforts and research on
biodiversity. And FarmBeats aims to enable data-driven agriculture with
low-cost sensors, drones, and cloud services.
It’s not impossible to imagine putting such services together into an
optimization engine that knows everything about the land, the water, and
the creatures who live on planet Earth. Then we’ll just have to tell
that engine what we want to do about it.
IEEE Spectrum
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