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How AI can save elephants

Deep in the rainforest in a northern corner of the Republic of Congo, some of the most sophisticated monitoring of animal sounds on earth is taking place. Acoustic sensors are collecting large amounts of data around the clock for the Elephant Listening Project.

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These sensors capture the soundscape in Nouabalé-Ndoki National Park and adjacent logging areas: chimpanzees, gorillas, forest buffalo, endangered African grey parrots, fruit hitting the ground, blood-sucking insects, chainsaws, engines, human voices, gunshots. But researchers and local land managers who placed them there are listening for one sound in particular — the calls of elusive forest elephants.

Forest elephants are in steep decline; scientists estimate two-thirds of Africa’s population has likely been lost to ivory poaching in recent decades. Africa’s savannah elephants have also declined by 30 percent over a recent seven-year period, primarily because of poaching, according to results released in 2016 from Paul G. Allen’s Great Elephant Census.

But those working to save these species, which are critical to keeping ecosystems in balance and that also draw wildlife tourists, have a powerful new tool at their disposal: artificial intelligence.

Conservation Metrics, a Microsoft AI for Earth grantee based in Santa Cruz, California, uses machine learning to monitor wildlife and evaluate conservation efforts. It is applying its sophisticated algorithms to help the Elephant Listening Project, based at Cornell University’s Lab of Ornithology, distinguish between forest elephant calls and the other sounds in a noisy tropical rainforest. It’s a perfect job for AI — looking for these rare patterns in terabytes of data that would take humans years.

Researchers use the elephant call data to build more accurate and frequent population estimates, track their movements, provide better security and potentially to identify individual animals, which can’t be easily seen from the air.

It is one of many ways biologists, conservation groups and Microsoft data scientists are enlisting artificial intelligence to prevent the illegal killing of elephants across Africa, stop the global trade in their parts and preserve critical habitat. Efforts include using machine learning to detect real-time movement patterns that could alert rangers to poaching and blocking online ads that attempt to sell illegal ivory or elephant parts.

Scientists with the Elephant Listening Project estimate that Africa’s population of forest elephants has dropped from roughly 100,000 animals in 2011 to fewer than 40,000 animals today. But those numbers are largely based on indirect evidence: ivory seizures, signs of poaching and labor-intensive surveys that are too expensive to be done regularly.

The Elephant Listening Project has spent more than three decades researching how elephants use low-frequency rumbling sounds to communicate with one another. More recently, those scientists began to use acoustic sensors at research sites to build population estimates and, ultimately, to track and protect forest elephants across their ranges in Central and West Africa.

If scientists find, for example, that at specific times of year elephants are using clearings in an unprotected logging concession to access scarce minerals or find mates, scientists can work with the loggers to schedule their work to minimize disturbance and reduce conflicts.

But there has been a bottleneck in getting data out of these remote African forests and analyzing information quickly, says Peter Wrege, a senior research associate at Cornell who directs the Elephant Listening Project.

“Right now, when we come out of the field with our data, the managers of these protected areas are asking right away, ‘What have you found? Are there fewer elephants? Is there a crisis we need to address immediately?’ And sometimes it takes me months and months before I can give them an answer,” says Wrege.

Conservation Metrics began collaborating with the Elephant Listening Project in 2017 to help boost that efficiency. Its machine learning algorithms have been able to identify elephant calls more accurately and will hopefully begin to shortcut the need for human review. But the volume of data from the acoustic monitors is taxing the company’s local servers and computational capacity.

Microsoft’s AI for Earth program has given a two-year grant to Conservation Metrics to build a cloud-based workflow in Microsoft Azure for analyzing and processing wildlife metrics. It has also donated Azure computing resources to the Elephant Listening Project to support its data-processing costs for the project. The computational power of Azure will speed processing time dramatically, says Matthew McKown, the CEO of Conservation Metrics. The platform also offers new opportunities for clients to upload and interact with their data directly.

It takes about three weeks for computers to process a few months of sound data from this landscape-scale study, says McKown. Once the Azure migration is complete later this year, that same job may take a single day.

“It’s a huge improvement. We’re really interested in speeding up that loop between having equipment monitoring things out in the field and going through this magic process to convert those signals into information you can send into the field where someone can take action,” says McKown. “Right now, that process can take a really long time.”

‘We’ve only scratched the surface’

Across the continent in East Africa, Jake Wall, a research scientist with Save the Elephants who collaborates with the Mara Elephant Project and other conservation groups, typically has more immediate access to data about the savannah elephants he studies in Kenya and seven other countries. That’s because animals in those populations have been outfitted with GPS tracking collars that transmit location data via satellites and cell networks.

That information is uploaded to the Domain Awareness System (DAS), a real-time data visualization and analysis platform now used in protected areas across Africa. It integrates data from about 15 different sources today, including ranger vehicle and radios, animal trackers, camera traps, drones, weather monitors, field reports, snare locations and satellite imagery. The tool was developed by Paul G. Allen’s Great Elephant Census, another AI for Earth partner that is moving the DAS system and its data onto the Azure cloud, to give managers a real-time dashboard that can inform tactical decisions for interdiction against suspected illegal activity or apparent threats to endangered wildlife.

In some areas, DAS also powers a Save the Elephants tracking app that can alert rangers when an animal has slowed or stopped moving via email or text message. The app can also warn when animals are heading toward human settlements where they might raid a farmer’s crops. Reserve managers or the farmer can then help herd the animals back to safety. From Gabon to Mozambique to the Congo, some 463 animal tracking devices are deployed, of which 358 are on elephants.

In other projects, Microsoft has worked with the Peace Parks Foundation, which combats rhino and other wildlife poaching in South Africa, to create remote sensing systems that can detect and evaluate poaching risks. Microsoft, through a NetHope Azure Showcase grant, is also helping move the open-source SMART (Spatial Monitoring and Reporting Tool) Connect to the Azure cloud. It is used in dozens of conservation sites across Africa to improve the effectiveness of wildlife patrols.

AI for Earth has also provided grants to researchers at the USC Center for AI in Society (CAIS) and Carnegie Mellon University, who have created and are continuing to improve Protection Assistant for Wildlife Security (PAWS). It uses machine learning to create patrol routes based on where poaching activity is most likely to occur. USC CAIS has also created and is continuing to improve the Systematic Poacher Detector, which detects poachers and wildlife in nighttime drone footage, now being used by organizations including Air Shepherd.

Even with advances in radio collar technology, sensors and imagery collection, a lot of additional work is needed to turn that data into scientific insights or actionable intelligence, says Wall.

“I think we’ve only scratched the surface of what’s possible,” says Wall. “We’re really excited because the expertise that Microsoft and AI for Earth can bring to the table includes skillsets that field biologists don’t typically have.”

“Machine learning could be applied to seven or eight immediate things that I would love to know more about, whether it’s recognizing individual elephants or picking up on changes in movement behavior or figuring out what’s happening on a landscape level with human expansion and deforestation,” says Wall.

Wall has been collaborating with Dan Morris, a Microsoft researcher working with AI for Earth, on a half dozen project ideas. One examines how to use machine learning to identify streaking behaviors — when elephants run fast and in an unusually straight line — that can be a sign of poaching or other threats.

Morris has also been working to apply machine learning algorithms to camera traps, which are remote field cameras that are triggered by motion and photograph anything that crosses their path. But finding an animal of interest can be like looking for a needle in a haystack.

“Sometimes no one has time to look through these images and they end up sitting on a grad student’s shelf somewhere,” says Morris. “The potential for machine learning to rapidly accelerate that progress is huge. Right now there is some really solid work being done by computer scientists in this space, and I would guess that we’re less than a year away from having a tool that biologists can actually use.”

Wall and Morris are also beginning to work on using AI to distinguish between elephants and other animals like buffalo or giraffes in aerial photography. Knowing when and where elephants are coming into contact with other wildlife — and particularly domesticated animals like cattle — can help rangers minimize conflicts with humans and help scientists better understand disease vectors.

These insights can also inform land-management decisions, such as where to lobby for protected areas and where to locate human infrastructure like roads and pipelines. That’s one of the most significant yet least understood threats to elephant survival, says Wall. With access to the right imagery data, AI tools could help begin to keep tabs on, and draw useful insights into, human encroachment into their habitat.

“We’re always focused on poaching and these acute problems, but really it’s the expansion of human settlements and the advancements of roads and railways and pipelines that are going to affect African elephant populations going forward,” says Wall.

‘AI is really the key piece’

Saving elephants isn’t just about stopping poachers where they hunt. Disrupting the global marketplace that rewards them economically is equally important.

Microsoft and other tech companies have joined the Global Coalition to End Wildlife Trafficking Online, organized by the World Wildlife Fund (WWF) and partners TRAFFIC and the International Fund for Animal Welfare. After observing that trafficking in wildlife parts like elephant ivory, animal skins and live pets had largely moved from physical marketplaces to the internet, they convened companies from across the online landscape to combine forces to stop it.

Along with targeting the illegal trade in elephant products, the coalition partners target criminal transactions such as the sale of tiger cubs for pets and the trade in pangolin scales and illegal coral.

“Previously cybercriminals were able to operate pretty freely on the internet because there wasn’t much risk,” says Giavanna Grein, a wildlife crime program officer at WWF. “But now we’re creating deterrents and consistency across all the different platforms — if every time a criminal creates a new account and puts up a new post, it’s taken down immediately, that’s going to be really frustrating for that criminal.”

The coalition has since worked with search engines like Bing, e-commerce sites and social media companies to adopt strong and consistent policies about what products are prohibited on their platforms. WWF also provides training to help companies recognize and shut down advertisements and customer accounts that traffic in illegal wildlife.

That involves some mix of human detective work and algorithms that search for keywords associated with wildlife trafficking. In September, Microsoft’s AI for Earth team will host an AI-focused workshop for tech companies and academics working to enhance automation to detect illegal wildlife and their products online. The goal is to advance technologies to identify and root out endangered species posts before anyone has a chance to see and purchase them.

“AI is really the key piece in combating wildlife trafficking online. While it’s not the only solution needed, automating the review of posts containing illegal wildlife and their products would drastically increase the barrier to entry for wildlife cybercriminals,” says Grein.

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Seedstars seeks tech to reverse land degradation in Africa

A new partnership is offering prizes to young entrepreneurs for coming up with innovations that tackle the loss of arable land in Africa.

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The DOEN Foundation has joined forces with Seedstars, an emerging market startup community, to launch the DOEN Land Restoration Prize, which showcases solutions to environmental, social and financial challenges that focus on land restoration activities in Africa. Stichting DOEN is a Dutch fund that supports green, socially-inclusive and creative initiatives that contribute to a better and cleaner world.

While land degradation and deforestation date back millennia, industrialization and a rising population have dramatically accelerated the process. Today we are seeing unprecedented land degradation, and the loss of arable land at 30 to 35 times the historical rate.

Currently, nearly two-thirds of Africa’s land is degraded, which hinders sustainable economic development and resilience to climate change. As a result, Africa has the largest restoration opportunity of any continent: more than 700 million hectares (1.7 billion acres) of degraded forest landscapes that can be restored. The potential benefits include improved food and water security, biodiversity protection, climate change resilience, and economic growth. Recognizing this opportunity, the African Union set an ambitious target to restore 100 million hectares of degraded land by 2030.

Land restoration is an urgent response to the poor management of land. Forest and landscape restoration is the process of reversing the degradation of soils, agricultural areas, forests, and watersheds thereby regaining their ecological functionality. According to the World Resources Institute, for every $1 invested in land restoration it can yield $7-$30 in benefits, and now is the time to prove it.

The winner of the challenge will be awarded 9 months access to the Seedstars Investment Readiness Program, the hybrid program challenging traditional acceleration models by creating a unique mix to improve startup performance and get them ready to secure investment. They will also access a 10K USD grant.

“Our current economic system does not meet the growing need to improve our society ecologically and socially,” says Saskia Werther, Program Manager at the DOEN Foundation. “The problems arising from this can be tackled only if a different economic system is considered. DOEN sees opportunities to contribute to this necessary change. After all, the world is changing rapidly and the outlines of a new economy are becoming increasingly clear. This new economy is circular and regenerative. Landscape restoration is a vital part of this regenerative economy and social entrepreneurs play an important role to establish innovative business models to counter land degradation and deforestation. Through this challenge, DOEN wants to highlight the work of early-stage restoration enterprises and inspire other frontrunners to follow suit.”

Applications are open now and will be accepted until October 15th. Startups can apply here: http://seedsta.rs/doen

To enter the competition, startups should meet the following criteria:

  • Existing startups/young companies with less than 4 years of existence
  • Startups that can adapt their current solution to the land restoration space
  • The startup must have a demonstrable product or service (Minimum Viable Product, MVP)
  • The startup needs to be scalable or have the potential to reach scalability in low resource areas.
  • The startup can show clear environmental impact (either by reducing a negative impact or creating a positive one)
  • The startup can show a clear social impact
  • Technology startups, tech-enabled startups and/or businesses that can show a clear innovation component (e.g. in their business model)

Also, a specific emphasis is laid, but not limited to: Finance the restoration of degraded land for production and/or conservation purposes; big data and technology to reverse land degradation; resource efficiency optimization technologies, ecosystems impacts reduction and lower carbon emissions; water-saving soil technologies; technologies focused on improving livelihoods and communities ; planning, management and education tools for land restoration; agriculture (with a focus on precision conservation) and agroforestry; clean Energy solutions that aid in the combat of land degradation; and responsible ecotourism that aids in the support of land restoration.

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The dark side of apps

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Mobile device security threats are on the rise and it’s not hard to see why. In 2019 the number of worldwide mobile phone users is forecast to reach 4.68 billion of which 2.7 billion are smartphone users. So, if you are looking for a target, it certainly makes sense to go where the numbers are. Think about it, unsecured Wi-Fi connections, network spoofing, phishing attacks, ransomware, spyware and improper session handling – mobile devices make for the perfect easy target. In fact, according to Kaspersky, mobile apps are often the cause of unintentional data leakage.

“Apps pose a real problem for mobile users, who give them sweeping permissions, but don’t always check security,” says Riaan Badenhorst, General Manager for Kaspersky in Africa. “These are typically free apps found in official app stores that perform as advertised, but also send personal – and potentially corporate – data to a remote server, where it is mined by advertisers or even cybercriminals. Data leakage can also happen through hostile enterprise-signed mobile apps. Here, mobile malware uses distribution code native to popular mobile operating systems like iOS and Android to spread valuable data across corporate networks without raising red flags.”

In fact, according to recent reports, 6 Android apps that were downloaded a staggering 90 million times from the Google Play Store were found to have been loaded with the PreAMo malware, while another recent threat saw 50 malware-filled apps on the Google Play Store infect over 30 million Android devices. Surveillance malware was also loaded onto fake versions of Android apps such as Evernote, Google Play and Skype.

Considering that as of 2019, Android users were able to choose between 2.46 million apps, while Apple users have almost 1.96 million app options to select from, and that the average person has 60-90 apps installed on their phone, using around 30 of them each month and launching 9 per day – it’s easy to see how viral apps take several social media channels by storm.

“In this age where users jump onto a bandwagon because it’s fun or trendy, the Fear of Missing Out (FOMO) can overshadow basic security habits – like being vigilant on granting app permissions,” says Bethwel Opil, Enterprise Sales Manager at Kaspersky in Africa. “In fact, accordingly to a previous Kaspersky study, the majority (63%) of consumers do not read license agreements and 43% just tick all privacy permissions when they are installing new apps on their phone. And this is exactly where the danger lies – as there is certainly ‘no harm’ in joining online challenges or installing new apps.”

However, it is dangerous when users just grant these apps limitless permissions into their contacts, photos, private messages, and more. “Doing so allows the app makers possible, and even legal, access to what should remain confidential data. When this sensitive data is hacked or misused, a viral app can turn a source into a loophole which hackers can exploit to spread malicious viruses or ransomware,” adds Badenhorst. 

As such, online users should always have their thinking caps on and be more careful when it comes to the internet and their app habits including:

  • Only download apps from trusted sources. Read the reviews and ratings of the apps as well
  • Select apps you wish to install on your devices wisely
  • Read the license agreement carefully
  • Pay attention to the list of permissions your apps are requesting. Only give apps permissions they absolutely insist on, and forgo any programme that asks for more than necessary
  • Avoid simply clicking “next” during an app installation
  • For an additional security layer, be sure to have a security solution installed on your device

“While the app market shows no signs of slowing down, it is changing,” says Opil. “Consumers download the apps they love on their devices which in turn gives them access to content that is relevant and useful. The future of apps will be in real-world attribution, influenced by local content and this type of tailored in-app experience will lead consumers to share their data more willing in a trusted, premium app environment in exchange for more personalised experiences. But until then, proceed with caution.”

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