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18.12.2020 Feature Article

Artificial Intelligence and Environment the Pros and Cons

Artificial Intelligence and Environment the Pros and Cons
18.12.2020 LISTEN

Artificial Intelligence was first invented at the mathematics department of Dartmouth University in 1956 by John MarCarthy, who defined AI as ‘the science and engineering of making intelligent machines. The term is now used both for intelligent machines that are the goal and for the science and technology that driving at that goal.

Artificial Intelligence (AI) offers huge potential to transform our ability to understand, monitor and predict environmental risks, providing direct societal benefit as well as potential commercial opportunities.

AI continues to advance exponentially. Environmental issues are one of the concerns that scientist is turning to AI to find sustainable solutions, however the technology also presents some risks. Example Technology has not always been the climate's best friend. From the industrial revolution that forever changed our relationship to the planet, to modern devices that mean we are constantly consuming energy and adding to mountains of electronic waste, every human step into the future seems to put Earth in greater danger. Our digital activity has a clear impact on the physical world and comes with a real environmental cost. The carbon footprints of Amazon, Facebook, and Google have been increasingly scrutinised in recent years.

What are the Environment benefits?

Using AI for environmental sustainability can help maximize our current efforts for environmental protection. According to a 2018 report by Intel , 74% of 200 business decision-makers in environmental sustainability agreed that AI would help solve environmental problems. Examples are

Better climate forecasts

As the climate changes, accurate forecasts are increasingly vital. However, climate models often produce very different predictions, largely because of how data is processed into isolated parts, how processes and systems are paired, and because of the large variety of spatial and temporal scales. The Intergovernmental Panel on Climate Change (IPCC) reports are based on many climate models and show the range of predictions, which are then averaged out.

Averaging them out, however, means that each climate model is given equal weight. AI is helping to determine which models are more reliable by giving added weight to those whose predictions eventually prove to be more accurate, and less weight to those performing poorly. This will help improve the accuracy of climate change projections.

AI and deep learning are also improving weather forecasting and the prediction of extreme events. That’s because they can incorporate much more of the real-world complexity of the climate system, such as atmospheric and ocean dynamics and ocean and atmospheric chemistry, into their calculations. This sharpens the precision of weather and climate modelling, making simulations more useful for decision-makers.

More sustainable transport on land

As vehicles become able to communicate with each other and with the infrastructure, artificial intelligence will help drivers avoid hazards and traffic jams. In Pittsburgh, an artificial intelligence system incorporating sensors and cameras that monitors traffic flow adjusts traffic lights when needed. The systems are functioning at 50 intersections with plans for 150 more and have already reduced travel time by 25 percent and idling by more than 40 percent. Less idling, of course, means fewer greenhouse gas emissions.

Eventually, autonomous AI-driven shared transportation systems may replace personal vehicles.

Smart agriculture

Hotter temperatures will have significant impacts on agriculture as well.

Data from sensors in the field that monitor crop moisture, soil composition and temperature help AI improve production and know when crops need watering. Incorporating this information with that from drones, which are also used to monitor conditions, can help increasingly automatic AI systems know the best times to plant, spray and harvest crops, and when to head off diseases and other problems. This will result in increased efficiency, enhanced yields, and lower use of water, fertilizer and pesticides.

How AI is used for energy

AI is increasingly used to manage the intermittency of renewable energy so that more can be incorporated into the grid; it can handle power fluctuations and improve energy storage as well.

Artificial intelligence can enhance energy efficiency, too. Google used machine learning to help predict when its data centres’ energy was most in demand. The system analysed and predicted when users were most likely to watch data-consuming Youtube videos, for example, and could then optimise the cooling needed. As a result, Google reduced its energy use by 40 percent.

Making cities more liveable and sustainable

AI can also improve energy efficiency on the city scale by incorporating data from smart meters and the Internet of Things (the internet of computing devices that are embedded in everyday objects, enabling them to send and receive data) to forecast energy demand. In addition, artificial intelligence systems can simulate potential zoning laws, building ordinances, and flood plains to help with urban planning and disaster preparedness. One vision for a sustainable city is to create an “urban dashboard” consisting of real-time data on energy and water use and availability, traffic and weather to make cities more energy efficient and liveable.

In China, IBM’s Green Horizon project is using an AI system that can forecast air pollution, track pollution sources and produce potential strategies to deal with it. It can determine if, for example, it would be more effective to restrict the number of drivers or close certain power plants in order to reduce pollution in an area.

Another IBM system in development could help cities plan for future heat waves. AI would simulate the climate at the urban scale and explore different strategies to test how well they ease heat waves. For example, if a city wanted to plant new trees, machine learning models

could determine the best places to plant them to get optimal tree cover and reduce heat from pavement.

Protecting the oceans

The Ocean Data Alliance is working with machine learning to provide data from satellites and ocean exploration so that decision-makers can monitor shipping, ocean mining, fishing, coral bleaching or the outbreak of a marine disease. With almost real time data, decision-makers and authorities will be able to respond to problems more quickly. Artificial intelligence can also help predict the spread of invasive species, follow marine litter, monitor ocean currents, keep track of dead zones and measure pollution levels.

The Nature Conservancy is partnering with Microsoft on using AI to map ocean wealth. Evaluating the economic value of ocean ecosystem services—such as seafood harvesting, carbon storage, tourism and more—will make better conservation and planning decisions possible. The data will be used to build models that consider food security, job creation and fishing yields to show the value of ecosystem services under differing conditions. This can help decision-makers determine the most important areas for fish productivity and conservation efforts, as well as the trade-offs of potential decisions. The project already has maps and models for Micronesia, the Caribbean, Florida, and is expanding to Australia, Haiti, and Jamaica.

AI has many other environmental benefits

AI can help to monitor ecosystems and wildlife and their interactions. Its fast processing speeds can offer almost real-time satellite data to track illegal logging in forests. AI can monitor drinking water quality, manage residential water use, detect underground leaks in drinking water supply systems, and predict when water plants need maintenance. It can also simulate weather events and natural disasters to find vulnerabilities in disaster planning, determine which strategies for disaster response are most effective, and provide real-time disaster response coordination.

The waste management processes typically involve numerous technical, climatic, environmental, demographic, socio-economic, and legislative parameters. Such complex nonlinear processes are challenging to model, predict and optimize using conventional methods. Recently, artificial intelligence (AI) techniques have gained momentum in offering alternative computational approaches to solve solid waste management (SWM) problems. New AI-powered technology helps recycling plants intelligently categorize different waste. The tech uses smart sensors and machine learning to rapidly identify what's on a conveyor belt. ... AI sensors can separate out plastics used with chemicals from those that are clean, even if they're the same material.

What are the environment risks?

Although AI presents transformative opportunities to address the Earth’s environmental challenges, left unguided, it also has the capability to accelerate the environment’s degradation.

Training artificial intelligence is an energy intensive process. New estimates suggest that the carbon footprint of training a single AI is as much as 284 tonnes of carbon dioxide equivalent – five times the lifetime emissions of an average car.

A study released last year by MIT Technology Review found that training a "regular" AI using a single high-performance graphics card has the same carbon footprint as a flight across the United States. Training a more sophisticated AI was even worse , pumping five times more CO2 into the atmosphere than the entire life cycle of an American car, including its manufacturing.

Whether it's the latest AI or machine learning algorithm that's active on a system, a new 5G network deployed at a commercial building or people streaming the latest Twitch gaming video, people generate and consume a lot of data. All that data must be captured, stored, analysed and sent back out, which requires significant amounts of processing power. How can the tech industry deal with the increasing environmental cost of AI and its supporting systems while still providing the same service consumers demand?

Conclusion.

Learning more about the environmental impact of AI and other emerging technologies is the responsibility of the technology industry. We all have to understand how we are affecting more than our target customers and learn about ways we can minimize our impact. It's one thing to say we are committed to the environment and donate to climate change causes. It's quite another to take proactive steps to ensure our technology keeps both people and the planet in mind. We're all in this together.

Ref

  1. Harnessing_Artificial_Intelligence_for_the_Earth_report_2018.pdf (weforum.org)
  2. AI could help us protect the environment — or destroy it | Environment| All topics from climate change to conservation | DW | 16.07.2018
  3. Beyond AI: The Need for Solutionists In Creating A Sustainable World (forbes.com)
  4. Training a single AI model can emit as much carbon as five cars in their lifetimes | MIT Technology Review
  5. AI Applications in Waste Management Industry and Demand for Data in 2020 | by David Yakobovitch | Data Driven Investor | Medium
  6. Charting the Course to a Renewable Energy Future (columbia.edu)
  7. How AI is Helping Us Better Understand the Environment (intel.com)
  8. New Predictions of Climate Change's Impact on Agriculture (columbia.edu)
  9. https://arxiv.org/pdf/1906.02243.pdf

Written by Philip Kwabena Kyeremanteng BSc MSc CEnv CSci

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