5 Ways Artificial Intelligence
Can Help Save The Planet
From smarter electric grids to automated monitoring of at-risk
environments, there are many areas where technology could have exponential
effects on sustainability.
If the world’s natural resources are
increasingly stressed and depleted, the silver lining may be that we’re
becoming better equipped at tracking that destruction and potentially doing
something about it. Cheap, widespread sensor networks, the internet of things,
magnitude-improvements in computing power, open source algorithms–these all
allow us to manage oceans and forests more effectively, if we want the
opportunity. Artificial
intelligence systems that can sense, think, learn, and act
on their own could allow a major upgrade in conservation efforts, in dealing
with climate change, and living in a more energy-efficient manner.
A report released during the recent Davos World Economic
Forum meeting laid more than 80 potential environmental applications for AI,
ranging from the mundane to the futuristic. We spoke with Celine
Herweijer, a partner at consultants PwC and one of the authors of the report. She
argues that AI is now going mainstream: Algorithms and supercomputers that once
were limited to specialist researchers at universities and government labs are
now open to startups and everyday corporations. New ways of managing
ecologically relevant systems are opening up as never before.
AUTONOMOUS ENERGY AND WATER
NETWORKS
Solar, wind, and other renewables have the
advantage of being carbon-free and ubiquitous. They can be situated in villages
and towns and out-of-the-way places, bringing energy closer to everyone who
needs it. The challenge is stitching these disparate sources together into
a coherent, functional whole. That’s where autonomous systems come in. They can
deal with the intermittency of renewables and react to the ebb and flow: when
one source of power is coming online or going down, or when one user is ramping
up demand and another is clocking off for the night. AI systems are flexible
and they can do more work, and be in more places, than human grid managers.
“When you have a complex system with so many
sources of renewables, you need them to talk to one another, so you can do
storage and optimize the load,” Herweijer tells me. “That can’t happen
without artificial intelligence enabling all these new sources to come
together. They will enable these future systems where we have peer-to-peer
energy trading and community exchange. They are what
we need for a decentralized, autonomous grid.”
Similarly, AI will allow for a more
decentralized water system, driven by sensors and new technologies like
blockchain, Herweijer says. Smart contracts–legal arrangements
automated with code–can enable swift trading of assets, including water
rights. “Blockchain is vital for recording provenance, then you can have
smart contracts and have people trading between parts of the decentralized
network,” Herweijer says. “Utilities of the future, whether water or
energy, will be more decentralized because that improves
productivity.” The Department of Energy has some early-stage
AI-based grid systems in development.
OPENING UP CLIMATE MODELING
Modeling future weather events and climate
patterns means processing complicated physical equations, like the fluid
dynamics of the atmosphere and oceans. Climate scientists have relied on
supercomputers, like the one at the Argonne National Laboratory, outside
Chicago, to do their calculations. But there are only a few dozen true
supercomputers around the world, meaning that access is limited: Many other
scientific fields also require big computational capacity.
Deep-learning techniques, inspired by the way
the human brain processes information, incorporate some of the complexity of
the real world in climate modeling, allowing computers to run faster and do
more calculations within a given period. “We’ll do simulations and
modeling on home computers than we do now on supercomputers,” Herweijer
says. “We can model small-scale features like wind storms that we struggled
with in the past. Once you put AI in the system, you’ve got more people doing
simulations and they’re doing it quickly. Forecasting of weather and climate
impacts is going to get better rapidly over the next 10 years.”
REAL-TIME DATA DASHBOARDS
Problems like illegal logging and illegal
fishing require better monitoring systems. Data from satellites and unmanned
underwater vessels can help bring greater visibility to such resources, but AI
can help crunch the data to make it useful. New processing capabilities
could provide close-to-real-time transparency by enabling authorities, and even
the general public, to monitor fishing, shipping, ocean mining, and other
activities,” the report says. “Vessel algorithmic patterns could identify
illegal fishing, biological sensors could monitor the health of coral reefs,
and ocean current patterns could improve weather forecasting.”
Global Forest
Watch, a multi-group alliance convened by the
World Resources Institute, uses satellite data to map illegal logging and
offers a sort of early template for what Herweijer means. The Ocean Data
Allianceis a similar public-private partnership for
ocean monitoring involving groups like IBM and UC Santa Barbara’s Benioff
Ocean Initiative. Its “approach could allow decision-makers to use machine
learning to monitor, predict, and respond to changing conditions such as
illegal fishing, a disease outbreak, or a coral-bleaching event,” the report
says. Such systems need to involve industry to remain relevant, Herweijer
says. For example, they can help companies prove they are abiding by
commitments to avoid certain fish or trees.
DISASTER RESILIENCY AND RESPONSE
Decision-making in the wake of natural
disasters is limited by the information available to government agencies and
aid groups. It’s hampered by a lack of coordination. “Losses of life and
property are multiplied when first responders can’t prioritize and target
resources. Herweijer sees a role for automated systems that can
analyze real-time data, like social media. “We don’t have a data-smart way
of responding in real time to natural disasters,” she says. “We need
public-private partnerships that bring together humanitarian agencies and big
satellite companies to pinpoint where to start,” she says.
Emerging forms of AI don’t just crunch
petabytes of “big data.” Techniques like “deep reinforcement” are
self-learning and require little or no initial data; instead they learn, like a
child, through trial and error and by being rewarded for success. “Deep
reinforcement learning may one day be integrated into disaster simulations to
determine optimal response strategies, similar to the way AI is currently being
used to identify the best move
in games like AlphaGo,” says Herweijer.
EARTH BANK OF CODES
The natural world contains reservoirs of
innovative capacity that remain largely untapped. AI and systems analytics
can help unbundle the biological and biomimetic
possibilities. Scientists have begun work on the natural world equivalent
of the Human Genome Project, with the aim of mapping the DNA sequences of
all living things. The Amazon Third Way initiative, for instance, is
developing a project called the Earth Bank of Codes, with two main intents. One, to open up potential
discoveries, like blood pressure
medicine derived from viper venom. And,
two, to record the provenance of biological IP assets, so local people can
benefit from follow-on discoveries.
“It’s not only about mapping genetic
codes, but also how you change decisions around those codes. Tracking assets
may be useful for a pharmaceutical company, but you are also starting to make
sure that when a transaction happens, the value goes back to the community that
grew the species,” says Herweijer.
https://www.fastcompany.com/40528469/5-ways-artificial-intelligence-can-help-save-the-planet?utm_source=postup&utm_medium=email&utm_campaign=Fast%20Company%20Daily&position=3&partner=newsletter&campaign_date=02122018
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