You’ve probably heard about the plague of plastic trash in the oceans. You’ve seen YouTube videos of sea turtles with drinking straws in their noses, or whales with stomachs full of marine litter. But how much plastic is out there? Where is it coming from? We don’t really know, because we haven’t measured it. “There’s a paucity of data,” says Marcus Eriksen, cofounder of the 5 Gyres Institute, a nonprofit focused on ending plastic pollution.
Marine litter isn’t the only hazard whose contours we can’t fully see. The United Nations has 93 indicators to measure the environmental dimensions of “sustainable development,” and amazingly, the UN found that we have little to no data on 68 percent of them—like how rapidly land is being degraded, the rate of ocean acidification, or the trade in poached wildlife. Sometimes this is because we haven’t collected it; in other cases some data exists but hasn’t been shared globally, or it’s in a myriad of incompatible formats. No matter what, we’re flying blind. “And you can’t manage something if you can’t measure it,” says David Jensen, the UN’s head of environmental peacebuilding.
In other words, if we’re going to help the planet heal and adapt, we need a data revolution. We need to build a “digital ecosystem for the environment,” as Jensen puts it.
The good news is that we’ve got the tools. If there’s one thing tech excels at (for good and ill), it’s surveillance, right? We live in a world filled with cameras and pocket computers, titanic cloud computing, and the eerily sharp insights of machine learning. And this stuff can be used for something truly worthwhile: studying the planet.
There are already some remarkable cases of tech helping to break through the fog. Consider Global Fishing Watch, a nonprofit that tracks the world’s fishing vessels, looking for overfishing. They use everything from GPS-like signals emitted by ships to satellite infrared imaging of ship lighting, plugged into neural networks. (It’s massive, cloud-scale data: over 60 million data points per day, making the AI more than 90 percent accurate at classifying what type of fishing activity a boat is engaged in.)
“If a vessel is spending its time in an area that has little tuna and a lot of sharks, that’s questionable,” says Brian Sullivan, cofounder of the project and a senior program manager at Google Earth Outreach. Crucially, Global Fishing Watch makes its data open to anyone—so now the National Geographic Society is using it to lobby for new marine preserves, and governments and nonprofits use it to target illicit fishing.
If we want better environmental data, we’ll need for-profit companies with the expertise and high-end sensors to pitch in too. Planet, a firm with an array of 140 satellites, takes daily snapshots of the entire Earth. Customers like insurance and financial firms love that sort of data. (It helps them understand weather and climate risk.) But Planet also offers it to services like Global Forest Watch, which maps deforestation and makes the information available to anyone (like activists who help bust illegal loggers). Meanwhile, Google’s skill in cloud-based data crunching helps illuminate the state of surface water: Google digitized 30 years of measurements from around the globe—extracting some from ancient magnetic tapes—then created an easy-to-use online tool that lets resource-poor countries figure out where their water needs protecting.
Tech can empower ordinary people too. To tackle the marine litter mystery, Eriksen and other antipollution groups built an app that hundreds of volunteers used to map the banks of the Los Angeles River, where trash was entering the marine ecosystem. Now cities can use that data to do surgical interventions, like identifying hot spots that need more trash cans or more frequent cleanup.
“It’s totally scalable,” Eriksen says, and groups from Ecuador to Hawaii plan to use the app for their own surveys. The citizen-involvement angle has serious legs: In China, 300 million people use an app made by Alipay that lets them donate money to plant forests and then monitor their growth via satellite and land-camera imagery. (They’ve planted over 13 million trees already.) This participation by everyday folks, as Jensen argues, builds crucial political support for environmental action.
Detecting Wildlife Crime in Real Time
March 7th, 2022
Via Medium, an article on three innovations that deliver real-time data to official combatting wildlife crime:
Wildlife crime is a multifaceted threat. It not only endangers thousands of species, it also threatens global security and robs vulnerable populations of income and food sources. Driven by consumer demand and corruption, wildlife crime is enabled by complex, hard-to-monitor transit routes and weak on-site species detection at border crossings.
Such complex challenges require innovative solutions. In 2016, through the Wildlife Crime Tech Challenge, USAID and partners awarded prize funding and technical assistance to 16 innovators developing technology to tackle the illegal wildlife trade. More than five years later, see what they have achieved in this story map.
Three of the prize winners — the Zoological Society of London, the University of Technology Sydney, and the University of Leicester — created affordable, accessible, and scalable products that deliver real-time data to authorities who combat wildlife crime. Read on to learn more about these innovations.
Catching Poachers in Real Time
Wildlife patrols often struggle to monitor vast and remote protected areas with limited technology and information on the activity of poachers. The Zoological Society of London, an international conservation organization, is developing a technology that combines motion-sensing cameras with military-grade sensors that can detect lightweight metal. The goal is to overcome a shortcoming of traditional motion detection systems, which cannot discern between animals passing by and poachers armed with weapons. The Instant Detect 2.0 could save wildlife patrols time and resources by quickly locating likely threats.
“Before, you had no idea what was triggering that alert, if it was 10 poachers or someone walking by with a machete,” said Sam Seccombe, technical project manager for the Zoological Society of London’s Monitoring and Technology Programme.
With this wireless, battery-operated, and camouflaged device, data collected through the cameras and motion-detecting sensors would automatically be transferred to secure cloud storage for sharing among authorities.
Since winning the Tech Challenge, the Zoological Society of London has worked to refine its product to make it accessible and affordable to those who need it most. Despite delays in field testing due to COVID-19, the product is scheduled to be deployed by the end of this year. In the future, the team hopes to add artificial intelligence for even faster image processing and greater accuracy in detecting specific species. They are currently assessing different business models to lower the price for consumers and encourage uptake of the product.
Identifying Illegal Wildlife Meat at Border Crossings
While the Zoological Society of London’s innovation aimed to help authorities catch poachers in protected areas, the University of Technology Sydney saw the need for detection at another juncture for wildlife crime: border crossings. With illegal wildlife often hidden in legal shipments, enforcement authorities, even those with sniffer dogs, struggle to rapidly distinguish legally-traded wildlife meat from illegal wildlife meat, highlighting a need for on-site species identification.
Thus, a team at the University of Technology Sydney developed a portable, electronic “nose” that customs officials can use to smell “fingerprints” of trafficked wildlife and wildlife parts. With four rounds of prototypes completed, the nose’s new sensor has shown high identification reliability in testing and the team is nearing commercialization.
The original schematic drawing for the University of Technology Sydney’s electronic ‘nose.’ / University of Technology Sydney
Once on the market, the portable “nose” will enable authorities to rapidly identify and confiscate illegal wildlife products, and quickly build evidence to prosecute offenders — without the need for laboratory analysis or the expertise required to operate similar equipment.Through the Tech Challenge’s networking opportunities, the university developed a partnership with the Australia National Museum to use the technology in addressing the illegal reptile trade. The University of Technology Sydney team is developing a business plan and will seek additional support to scale-up the electronic nose.
“The Tech Challenge has led to a lot of really good collaborations with people that we might not necessarily have crossed paths with otherwise,” said Maiken Ueland, deputy director of the The Australian Facility for Taphonomic Experimental Research and ARC DECRA fellow at the University of Technology Sydney.
Automating DNA-Based Species Identification
Another innovation that benefited from support through the Tech Challenge and could transform how authorities respond to wildlife crime is the MinION DNA sequencer. Recognizing that enforcement authorities are unable to run rapid, field-level DNA-based species identification to use as evidence of a crime, a team at the University of Leicester in the United Kingdom built the sequencer to analyze wildlife samples on the spot and deliver results in one hour, avoiding expensive equipment and lengthy testing processes.
“We can pretty much identify any vertebrate species.” said Dr. Jon Wetton, co-director of the Alec Jeffreys Forensic Genomics Unit at the University of Leicester.
MinION can fully automate DNA sequencing and species identification at the crime scene, providing authorities with evidence that would allow them to detain and arrest traffickers on the spot.
The Leicester team is using their handheld technology in the lab to identify the origins of traded birds of prey and determine if they are legally captive-bred or illegally poached from the wild in the United Kingdom and destined for the Middle East. They are still testing accuracy of the field-based tool. In the meantime, the MinION also enabled the first DNA sequencing in space on the International Space Station.
Harnessing Technology to Empower People and Fight Wildlife Crime
These three Tech Challenge winners showcase the potential for technology to transform wildlife crime detection and enforcement. By delivering information to authorities on the spot, these innovations would allow authorities to address crimes in real-time and build an evidence base to prosecute offenders. However, they also illustrate the challenges of developing a technology that is accurate, affordable, and scalable.
As demonstrated through the Wildlife Crime Tech Challenge, USAID is committed to supporting innovative approaches to combat wildlife crime, including empowering people with technology that allows them to work more efficiently and effectively.
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Read More »How AI Is Helping Combat Poaching in Africa and Asia
December 19th, 2021
Via Men’s Journal, an article on the use of AI to combat poaching in Africa and Asia:
Early in August, movement in a remote part of Kenya’s Maasai Mara National Reserve woke a sleeping trail camera. In seconds a processor chip took four photos, ran an artificial intelligence program that recognized a human shape and sent the highest-quality image via satellite to park headquarters. Rangers saw it was one of their own on patrol, not a poacher on an illegal hunt, dismissed the alert and got back to work. False alarm, sure, but for biologist Eric Dinerstein it was proof a new technology called TrailGuard AI works.
“Nothing beats a photo for telling if it’s a cattle herder or someone carrying an AK-47,” says Dinerstein, who helped develop the system for RESOLVE, a conservation nonprofit. “TrailGuard is a camera, but I think of it more as an AI-supported poacher alarm.”
Now being distributed to parks across Africa and Asia, the devices are part of a growing pool of innovative technology aimed at protecting habitat and wildlife. Rates of poaching are increasing, particularly in Africa where poachers kill an elephant every 15 minutes, according to the World Animal Foundation.
“Technology is going to play a critical roll in saving wildlife,” says Eric Becker, a conservation engineer with the WWF, a wildlife-focused NGO. “The right tools can be a force multiplier for rangers.”
Rangers typically hunt for signs of poaching during the day and conduct stakeouts at night. Efforts are often futile, wasting scarce resources, and, when successful, dangerous. At least 1,000 rangers have died at work in the last decade, as many as half those deaths at the hands of poachers.
New tech is helping save wildlife and make the ranger’s job safer. In Kenya, Becker, a former Air Force Research lab engineer, helped deploy thermal-imaging cameras to spot poachers from afar. “Rangers no longer patrol randomly,” he says. “They can set up to completely overwhelm the poachers and make arrests.”
Other groups are working on different solutions: traceable chips embedded in rhino horns and elephant tusks to bust trading networks; tracking collars that monitor wildlife movement for signs of stress; analytical tools that identify patterns, like poaching hot spots; and the CAKE Kalk AP, an electric dirt bike designed to help rangers sneak up on poachers. “We usually have to hack or custom develop conservation tools,” Becker says. “Off-the-shelf solutions aren’t usually baboon-proof.”
Preventative and noninvasive measures, like TrailGuard, are the most valuable, says Dinerstein. An early version of the device alerted rangers to a group of poachers entering Serengeti National Park in Tanzania. With the help of tracking dogs, the rangers arrested 30 poachers.
“Several million years of canine olfactory evolution and the latest tech and AI,” says Dinerstein. “It’s a killer combination.”
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Read More »Reimagining Environmental Data To Upgrade Conservation
September 21st, 2021
Via Sentinel, an interesting initiative to reimagine and democratize environmental data:
The Sentinel is Conservation X Labs’ new artificial intelligence device that addresses emerging extinction threats. The tool retrofits existing devices, such as trail cameras and acoustic recorders, to enhance how efficiently conservationists can act on important events.
The device instantly runs machine learning models on data as it is captured and sends notifications to users in real-time. This allows users to know if something critical, like the presence of a poacher or endangered species, is detected so they can take immediate action.
Henrik Cox, Product Management Engineer, deploys a Sentinel to monitor wildlife in Virginia.“The Sentinel democratizes creating, running, and deploying machine learning models by providing leverage to conservationists everywhere” said Alex Dehgan, Co-Founder and CEO of Conservation X Labs. “The Sentinel will fundamentally change how we monitor and protect the environment – from catching poachers before they can get away, monitoring endangered species in real time, and detecting new diseases before it’s too late.”
Leveraging advances in artificial intelligence through tools like the Sentinel can profoundly increase the scale and efficiency of conservation efforts to better understand and respond to environmental challenges and will allow us to better protect animals around the world.
The Sentinel has been selected for use in a variety of unique projects around the world capturing wide-ranging information, including identifying rare jaguars in Costa Rica, informing wildlife crime officers of suspicious behavior in South Africa, and monitoring gorilla behavior in the Congo. It was a grand prize winner at the 2021 ASME ISHOW, an international accelerator for hardware-led social innovations.
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Read More »Digitized Data Conserves Africa’s Great Lake Fisheries
February 13th, 2021
Via Nature, a look at how digitized data is helping conserves Africa’s Great Lake fisheries:
Africa’s Great Lakes stretch in an 1,800-mile curve of 11 countries from Ethiopia in the north to Malawi in the south, encompassing a region rich in biodiversity with nearly a third of the world’s fresh surface water and supporting the livelihoods of 80 million Africans.
The natural bounty the seven lakes provide to both people and planet is under threat. Increasing human populations eking a living from the land in areas of deep poverty are driving rapid and often unplanned development that has major environmental impacts on the lakes, their habitat and resources.
At the heart of the lakes’ economies are their fisheries. They hold the highest diversity of freshwater species of ecological, economic, and scientific importance on Earth, with at least 2,500 fish species, predominantly endemic cichlids.
On Lake Victoria, the largest by surface area, some 200,000 fishers catch in the region of one million tonnes of fish a year, driving businesses that support a further four million people to earn a living.
Already, stocks of many large commercially-important species have declined or even collapsed from over-fishing and environmental degradation. For example, due to historic commercial fishing, the three largest Lates (perch) species are rarely caught today on Lake Tanganyika.
Villagers bring in their catch of daaga, a kind of sardine on the shores of Lake Tanganyika. © Ami Vitale / TNC
Written on the Beach
To stop the same happening to the stocks that remain, authorities need affordable ways to keep track of what is happening with fish catches across all seven lakes: Albert, Edward, Kivu, Tanganyika, Turkana, Victoria, and Malawi, also known as Lake Nyasa and Lake Niassa.Historically, gathering and analysing data from the lakes was a costly, time-consuming process fraught with risks of human error warping results, says Innocent Sailale, senior data systems analyst for the Tanzania Fisheries Research Institute (TAFIRI). The Institute produces regular reports on the country’s fisheries sector to support policy-making by the national Fisheries Department in the Ministry of Natural Resources and Tourism.
“It was all manual, all on paper,” Mr. Sailale says. “Data from the fishers was written down at the beach, it would get wet and you struggle to read it later. The papers piled up and were stored somewhere until the scientists came from the Institute or the Ministry to collect them, and they sometimes couldn’t be found. People could even fill out fake data sitting at their home not ever going to the beach. Data entry errors were common.”
If scientists or the government wanted new research, a team would need to travel to the lake to update the enumerators on the information they would need to collect. These trips cost too much for strapped ministry budgets, meaning new science that could have informed smart policies never got off the ground in time.
Lake Tanganyika holds nearly one-fifth of the world’s freshwater and is home to 250 endemic species of fish. © Ami Vitale / TNC
Even problems identified in the data that were captured were sometimes only noticed months later when the manual forms were collected and transported to the labs, painstakingly entered into computer spreadsheets, and analysed.“You come to understand the issue when it’s already too late, or become something else,” Mr. Sailale says.
What this all meant was that the authorities tasked with sustaining the economic and ecological resources of Africa’s Great Lakes were in many cases flying blind.
“If you inadequately monitor these fisheries resources, successful and sustainable management cannot take place in a timely manner,” says Dr. Richard Ogutu-Ohwayo, who has studied and worked on Africa’s Great Lakes for over 40 years and is currently the Executive Director of AFLANET, the African Lakes Network.
“Inadequate monitoring has partly contributed to the decline in fish stocks. Availability of catch and effort data is critical in sustainable fisheries management, and it requires good, timely, and affordable. E-CAS was developed to achieve that.”
TNC’s Peter Limbu, James Anton and Sadoki Nfukamo look at and measure fish. © Ami Vitale / TNC
Capture the Data
E-CAS stands for Electronic Catch Assessment Survey. It is a digital data capture and analysis system run through an Android app loaded to mobile phones where data is collected and sent in real time to a cloud database accessible by stakeholders for instant analysis and reporting.It was developed to monitor the fisheries in the Tanzanian waters of Lake Tanganyika with a $110,000 grant from the African Great Lakes Conservation Fund, administered by The Nature Conservancy (TNC) with a $500,000 donation from the John D. and Katherine T. MacArthur Foundation.
The Fund was launched in 2017 at the African Great Lakes Conference in Entebbe, Uganda, the first major technical, scientific, political, and development gathering for the region’s freshwater lakes in more than 20 years. TNC was the lead organizer for this conference.
The MacArthur Foundation grant provided catalytic support under the African Great Lakes conservation fund to implement some of the key recommendations from the Conference, following on its support for the conference itself.
Alongside e-CAS, the Fund supported a regional online information platform; regional policy and research coordination workshops through the African Center for Aquatic Research and Education (ACARE); a compendium of best management practices for in-lake caged aquaculture; and a program to reduce sedimentation through sustainable land use management in Rwanda and Burundi.
Lake Tanganyika provides 40% of all protein for lakeshore villages. © Ami Vitale / TNC
“The success of the African Great Lakes Conference demonstrated an enthusiasm for regional coordination and problem solving, but there was a clear gap in facilitating this to happen,” says Colin Apse, Africa Freshwater Conservation Director for TNC.“The African Great Lakes Conservation Fund helped to fill some of the gap in sharing and developing best practice to benefit the environment and developing economies in and around the lakes, and the resources the lakes hold.”
The electronic fish catch assessment system is already having a significant impact, and is set to be expanded to other great lakes, says Dr. Ogutu-Ohwayo, who has worked closely with TNC’s Africa Great Lakes Initiative.
“E-CAS has really simplified how we do business in the fisheries sector, and it’s helping us a lot,” he says. “It speeds up the availability of data. It leaves scientists in the laboratory analysing data and advising managers, not travelling all the time to and from the field. It cleans the data as it’s generated, removing unnecessary errors. It’s instantly updated as improvements are developed or new research requirements are identified.”
Already, the authorities using e-CAS report the cost of monitoring fish catches has dropped by 70% since the system was introduced. Managers are able to plan amendments to policies or guidelines using the most up-to-date information.
© Hillary Mrosso
Those data can also be shared with the national authorities or partners including donors to help refine development funding to the fisheries sector so it is as effective as it can be.Just as important is that e-CAS involves people at all levels, from the central government all the way down the chain to the fishers on the lakes, says Dr Hillary Mrosso, Senior Research Officer at TAFIRI who worked with TNC to pilot e-CAS in Tanzania.
“It solves problems the scientists faced, but it also helps the communities,” says Dr. Mrosso. “The data comes right from the hands of the fishermen, and the system can feed back to them and authorised people at different levels. Analysis can be produced without having to wait for a national or regional report, even eventually to the village level so they would be able to tell what they’re catching and set up their plans on the basis of such kinds of data.”
The system works like this. Enumerators are picked from the fishing communities – usually fishers with a role in the local Beach Management Unit (BMU) – or from the district authorities. The e-CAS app is loaded to mobile phones running the Android operating system. Its simple interface encourages easy data entry.
Four groups of data are collected. First, where the fish were caught and where the catch was brought ashore. The handsets are GPS-enabled, and automatically record the location where data was collected making this information precise and impossible to fake.
Second, details of the fishing boat: What shape and size is it? Is it oar-powered or does it have an engine? Inboard or outboard? What size engine? How many crew are onboard?
Then, data on the fishing gear and equipment: what number and type of nets? What lines, and hooks? Was the gear used in a stationary fishing site or was it drifting?
Finally, measurements on the catch itself: what species of fish and what weight of each, and what was their estimated value either if they were sold where they were landed, or were due to be transported and sold elsewhere?
Kibuyi eCAS enumerators at work © Hillary Mrosso
Fishers feel a greater sense of ‘owning’ their fisheries, Dr. Mrosso says, when they are actively engaged in collecting the data and then see the results of the analysis.“Take an example of the fisher at the grass roots, they may not have understood, ‘Why should I not go out beach seining, for example, or use a monofilament’, because they don’t see the impact in tangible terms,” he says.
“But with this system they can look at the trends in their own village, and see, ‘when we changed our behaviour in this direction or the other, what happened’? They can see, ‘in the years when we had so many under-sized gill nets, or so many monofilaments, which are destructive, what was the pattern in our catches?’. It has quite a huge impact.”
Using the African Great Lakes Conservation Fund grant and working with TNC through the Tuungane Project, e-CAS was first launched in Tanzania on Lake Tanganyika and the country’s marine coast. The German government’s international development agency GIZ then expanded e-CAS to Lake Victoria, encompassing Tanzania, Kenya, and Uganda.
In Tanzania, 299 people have been trained to collect, store, and analyse e-CAS data. In Kenya and Uganda, 28 system administrators, managers, and supervisors have been trained. The system is being introduced to other African Great lakes such as Albert and Edward.
The goal now, Dr. Ogutu-Ohwayo says, is to expand the system even further. “The issues facing the Great Lakes are essentially the same,” he says. “Here we have a system that is very flexible, it is digital, it can be updated how we want, and it has already expanded from where it started on Lake Tanganyika and now is in Lake Victoria and other lakes such as Albert and Edward.
“It can also be adapted in other fisheries data collections systems such as frame surveys. Next it should go to all the Great Lakes to bring its benefits to each of them as well and developed for other fisheries management data collection.”
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Read More »Climate Intelligence
October 8th, 2019
Via the Harvard Business School, a look at how the gap between technology and science is bridged by machine learning and artificial intelligence:
A decade on from the financial crisis that saw stock markets crash, oil prices collapse, and clean technology venture funding dwindle, we’re seeing signs that “cleantech” and sustainability-focused technologies are back in focus — with the effects of climate change ever more apparent and increasingly urgent. As noted in a recent podcast by The Interchange from Greentech Media, this renewed interest comes from a variety of players ranging from startup entrepreneurs to established companies and new venture funds.
While climate change is portrayed as a divisive political issue, the scientific evidence is hard to ignore and extends across a range of quantifiable metrics including: the number of extreme weather events, rate of sea level rise, extent of coral reef damage linked to ocean acidification, and warming of ocean temperatures. Climate change is the reality that individuals and companies are navigating and have to account for.
The complexity of the climate crisis is often exacerbated by the sheer volume of data, which makes manual analysis intractable. Machine learning and artificial intelligence (AI) have made waves in many software technologies because of their ability to analyze and draw inferences from large amounts of granular data. Researchers, entrepreneurs, and businesses have responded to the immediacy of climate change by combining domain expertise, various data sources, and machine learning and AI to not only better understand the science behind climate change, but also innovate across a variety of sectors including energy, food and agriculture, transportation, and infrastructure.
Algorithms, such as convolutional neural networks originally designed for classifying images, allow researchers and companies to analyze high resolution satellite imagery provided by government agencies and private companies. This spatio-temporal information can provide insights on poverty, vegetation and land cover changes, and even infrastructure quality. When monitored over time, the sequences of these satellite images and the algorithmically extracted insights paint a picture of how climate change has impacted society.
In the energy sector, several startups have gained attention and investments from corporate venture funds like Shell Ventures and National Grid Partners, as well as seed accelerators like Y Combinator and even the billionaire-backed Breakthrough Energy Ventures (BEV):
- Autogrid – Machine learning and analytics to help utilities, electricity retailers, and renewable energy developers flexibly extract capacity from distributed energy resources (Shell Ventures, National Grid Partners)
- Traverse Technologies – AI-driven prospecting for optimal hydropower and wind generation sites (Y Combinator)
- KoBold Metals – Computer vision and machine learning-enabled digital prospecting to search for likely sources of cobalt (BEV, Andreessen Horowitz)
Technology companies have also applied their AI research to help address climate change — first internally and increasingly for external use. Google DeepMind, known for defeating a Go world champion with their program AlphaGo, first applied their technology to reduce Google’s data center cooling energy usage in 2016. The team was able to reduce energy usage by 40% through applying machine learning programs to help improve operational efficiency. Additionally, DeepMind has applied deep learning to improve renewable energy generation by forecasting wind energy generation in order to provide more reliable estimates of when power would actually be generated. This helped boost the value of the wind energy generation by 20% and inform the hourly commitments that were promised to the power grid.
Meanwhile, Microsoft has applied machine learning research to its services and calculated, in a recent study, that its cloud services are up to 93% more energy efficient and up to 98% more carbon efficient than traditional enterprise data centers. Externally, Microsoft has pledged $50 million over 5 years to support organizations and researchers looking to tackle environmental challenges through their AI for Earth program.
While deep learning has helped startups and technology companies tackle climate problems, it needs to be balanced with a critical look at the resources used in model training. Researchers at the University of Massachusetts, Amherst have recently found that training large AI systems, given the existing mix of energy sources, emits large amounts of carbon dioxide. These results illustrate the importance of relying on renewable energy sources to fuel the development of these machine learning technologies.
As climate change’s effects continue to necessitate immediate action, companies have started to turn to machine learning and AI to help navigate vast amounts of complex data to help improve decision making. In many cases, the combination of better technology in improved algorithms and better data have allowed businesses to not only reduce their impact on the environment but also operate more efficiently. While the magnitude of climate change requires a variety of technologies and solutions, machine learning and AI are powerful new tools in the fight against climate change. The confluence of these technologies with the explosion of large quantities of accessible data presents a critical new opportunity for humanity to pave a more sustainable path forward.
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Read More »Whole Earth, Cataloged.
September 26th, 2019
Via Wired, an interesting look at how – if we’re going to save Earth, we need a clear picture of all the forces that are destroying it – which means capturing more data:
Now, I don’t want to soft-pedal the task at hand. We’re way behind where we should be on nearly every environmental goal. But for once, tech offers a rare all-good-news story. When you’re fumbling around in the dark, the first step is to turn on the lights.
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