6 ways AI can help save the planet

From facial recognition technology that monitors brown bear populations, to intelligent robots sorting recycling, these initiatives are having a positive impact on the environment.

1. Conserving species

The Living Planet Index produced by WWF estimates that wildlife population sizes have dropped by 68 per cent since 1970. The charity advocates the use of artificial intelligence (AI) as a tool of conservation technology to monitor and curb this alarming rate of decline.
One of the most useful applications is in acoustic monitoring, recording the sounds of wildlife ecosystems on weatherproof sensors. Many animals, from birds and bats to mammals and even invertebrates, use sound for communication, navigation and territorial defence, providing reams of rich data on how a species population is doing. AI provides a fast and cost-effective way to analyse hours of recordings for patterns of behaviour.
Conservation Metrics, a California-based company, has used acoustic listening and machine-learning to monitor endangered populations of both red-legged frogs in Santa Cruz, diverting water to help them mate successfully, and the forest elephants of the Central African Republic, helping to protect them from poachers.
Facial recognition technology is another application of AI that could help track wildlife populations, when combined with camera traps in the wild. BearID, an open-source application, which was trained on brown bears in Canada and the United States, is a recent AI triumph as, unlike primates, zebras or giraffes, bears don’t have distinguishing features, so the deep-learning algorithm had to find patterns in their facial make-up instead. The researchers hope this AI will be used to monitor other species in the future.

2. Improving recycling

More than 2.1 billion tonnes of rubbish is generated in the world each year, yet only 16 per cent of it is recycled, according to research by Maplecroft. To make matters worse, a quarter of waste put into the recycling is not actually recyclable at all, hindering the whole process.
Several startups are now looking at how AI and sustainability goals can be combined to make recycling more efficient, even when dealing with mixed materials. Colorado-based AMP Robotics uses an AI-powered robot with optical sensors to quickly identify rubbish as it passes on a conveyor belt. It then sorts it with its robotic arms, using the company’s AMP Neuron AI platform, which can recognise different textures, colours, shapes, sizes and even brand labels.
The AI constantly updates itself and is designed to run 24/7. It has already been rolled out in the United States, Canada and Japan, and will soon be coming to Europe.
In Bali, Gringgo Tech has designed an image recognition tool to help informal waste collectors identify the different monetary values of various recyclable materials. In a pilot study, it improved recycling rates by 35 per cent. They’re now working with Google to build AI into the platform to help improve how quickly and efficiently the system can categorise waste.

3. Protecting forests

Forests are home to 80 per cent of the world’s terrestrial biodiversity, and they absorb and store a third of current carbon emissions. Halting the loss and degradation of forest ecosystems is essential to meeting the objectives of the Paris Agreement on climate change, according to the International Union for Conservation of Nature.
Rainforest Connection seeks to combat illegal logging using acoustic monitoring in forests on hidden solar-powered smartphones, which have been recycled from consumer use. The charity then uses AI to analyse this sound data in real time. If the AI detects the sounds of chainsaws, logging trucks or gunshots, an alert is sent to rangers. According to Rainforest Connection, research shows that if illegal loggers are interrupted once or twice, they leave and don’t return until the next
logging season.
Dryad Networks has secured seed funding to use the internet of things and AI to detect wildfires. Dryad uses AI-based solar-powered sensors to capture gases emitted at the smouldering stage of a wildfire which, combined with real-time analysis of temperature, humidity, air pressure and wind data, will alert forest rangers when a wildfire is imminent. They are also developing a long-range wireless environmental monitoring sensor network to cover large forest areas where there is no mobile-phone signal.

4. Cutting air pollution

Nine in ten of the world’s urban residents breathe polluted air, prompting the United Nations to make access to cycling, walking or public transportation one of its 17 Sustainable Development Goals.
To meet this challenge, London-based Vivacity uses AI technology to capture and classify live transport usage with the goal of enabling more environmentally sustainable transport use in cities. The company has been working with Transport for London since 2018 to determine where new cycling infrastructure should be targeted.
London’s Walking and Cycling Commissioner Dr Will Norman says: “By getting more people cycling and walking, we can help to tackle congestion and pollution in London and improve our health. Our Healthy Streets approach is based on evidence and data, and we welcome new technology that supports this.”
Vivacity’s AI has allowed local authorities across the UK to assess the effectiveness of their temporary street layouts to encourage physically active travel during the coronavirus crisis. The company has also helped Transport for Greater Manchester roll out smart junctions across the city, which prioritise pedestrians and cyclists over motor-vehicle traffic.

5. Minimising food waste

Some 9.5 million tonnes of food is wasted in the UK every year, according to the Waste and Resources Action Programme, 70 per cent of which could be avoided. The waste, which includes food from supermarkets, households and hospitality, generates 25 million tonnes of greenhouse gas emissions.
Winnow is working with HCL Technologies to use AI to tackle the problem in hospitality, where their data shows up to 15 per cent of purchased food is being wasted. Winnow Vision is an AI tool that takes pictures of food as it’s thrown into the bin, teaching itself to recognise what’s been discarded and tracking the data. IKEA has deployed Winnow Vision in its UK stores, cutting food waste by an average of 50 per cent.
Last year, UK supermarkets signed up to a government pledge to halve food waste by 2030. According to data from Blue Yonder, using AI in supermarket supply chains could help the UK’s eight largest retailers cut seven tonnes of food waste a year, saving £144 million. As Wayne Snyder, vice president of retail strategy, Europe, Middle East and Africa, at Blue Yonder says: “AI monitors goods from farm to fork, resulting in an increased understanding of the environmental impacts across the supply chain and identification of the areas that need improving.”

6. Reducing sewage pollution

Raw sewage was discharged onto beaches in the UK almost 3,000 times over the last year, according to a report by Surfers Against Sewage. The environmental charity advocates stricter monitoring of sea and river pollution, and operates an app called the Safer Seas Service, which warns swimmers, surfers and other water users when untreated sewage has been released at their beach.
But the app, which began in 2010 as a text alert system, relies on voluntary data provided by water companies, which isn’t always reliable. So, this year, Surfers Against Sewage added a health report function to the app, using a citizen science approach to warn others about beach cleanliness issues in real time, but also to hold water companies to account. Southern Water, for example, had released no notifications during 2020 due to reporting mechanism errors, yet over 20 per cent of health reports submitted to Surfers Against Sewage allegedly came from beaches within Southern Water’s jurisdiction.
In the future, application of AI will enable even more precise, live seawater quality assessments. Scientists working with the National Research Foundation of Korea have already shown that artificial neural network models can accurately predict microbial contamination at beaches, using variables including tides, temperatures, wind speed and direction, rainfall and recent sewage discharges. Southern Water has set a target of zero pollution incidents by 2040 and say they will use state-of-the-art machine-learning in that mission.

Source: www.raconteur.net – 10/12/2020 – Sam Haddad