How can artificial intelligence help build a greener future?

In the last few decades, artificial intelligence (AI) has gone from something out of science fiction to a part of scientific fact. It’s now an integral part of our present, and we’re beginning to see its impact on the workplace, the economy, and the technology we use every day. But what about its impact on the environment? How can we use AI to build a greener and more sustainable future?

AI has long been a popular topic in fiction, be it books, graphic novels or films – think 2001: A Space Odyssey, The Matrix or even The Terminator. In the interests of drama and suspense, the AI ​​portrayed is mostly malevolent, with machines having half an eye on taking over the world (or in the case of The Matrix, the machine actually being the world).

Although the AI ​​was fictional, the fears expressed were very real. In 1961, Life magazine published an article entitled “The Machine Are Taking Over: Computers Outdo Man At His Work – And Soon May Outthink Him”, which described machines that not only perform analytical and decision-making tasks in business and industry could, but also to learn for themselves.

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Fast forward to 2023 and these machines are now hard at work among us. But far from threatening the very existence of humanity as they were thought 60 years ago, today they make an invaluable contribution to our daily lives. So where exactly can AI contribute to environmental issues?

We take a look at six areas where artificial intelligence can have a direct and positive impact on the environment.

Clear sky

Thales’ PureFlyt flight management system is a prime example of what’s possible. It uses AI to optimize aircraft trajectories to reduce both fuel burn and noise pollution, with the goal of reducing aircraft CO2 emissions by 10% by 2023. In flight tests, AI was also used to simulate about two billion scenarios and achieve the equivalent of 100 million flight hours.

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In addition, research is being conducted into how these AI-assisted trajectories can be used to reduce the environmental impact of aircraft contrails by allowing aircraft to fly at a slightly lower altitude and allowing aircraft to perform a continuous descent to landing, as opposed to descending by plane. Both measures could help reduce fuel consumption and air pollution.

AI is also having a positive impact on air traffic management, which not only has to deal with traditional aircraft, but also with an increasing number of unmanned aerial vehicles in the skies. Rather than AI replacing humans, Thales believes it will enhance their capabilities, freeing them from repetitive, low-value tasks and allowing them to focus on more critical areas where human intervention is vital.

This human-centred focus will create safer and more efficient airspace management; Additionally, the advanced prediction capabilities of AI will result in fewer air and ground delays, which in turn means less fuel burn and a lower carbon footprint for the aviation industry.

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climate monitoring

AI also plays a crucial role in observing and understanding climate phenomena, which are key to combating climate change. Its most obvious application is in improving the image processing capabilities of satellites that can better analyze and predict climate phenomena.

Thales is contributing to these efforts, for example as part of the European earth observation and climate monitoring program Copernicus, which will help to better understand the impact of human activities on the environment.

Green mobility

But AI is not only making a significant contribution to environmental issues in and above the air. Thales has also developed complex, environmentally conscious systems for rail transport that use AI based learning and knowledge and consume less energy.

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Driver advisory and traffic management systems, as well as systems for managing the automated operation of subways and autonomous trains, optimize energy consumption through carefully defined driving strategies and by calculating optimal acceleration and braking profiles in real time.

These systems also make it possible to anticipate disruptions on the network and thus reduce unexpected train disruptions caused, for example, by obstacles on the tracks. Station monitoring systems also analyze energy usage in real-time, with sensors determining exact energy needs according to passenger flows and ensuring energy usage meets requirements.

Energy Consumption and the IoT

One of the areas where AI really comes into its own is in the face of what Gregor Pavlin, program manager and principal scientist at Thales Research & Technology, calls “bad data.” Modern systems, for example in the field of IoT (Internet of Things), collect huge amounts of different data. Traditionally, this data is processed in a central computer or cloud and the results are then sent back to the application in the field. However, this not only poses safety issues, but is also neither cost nor energy efficient.

Thales’ expertise has made it possible to develop a platform for the simple integration of distributed AI algorithms in the field, which process a large part of the data in an application-oriented manner – so-called edge computing – and only transmit the processing results and leave the data behind a secure firewall and a significant reduction in energy consumption.

Lateral thinking: eco-design and frugal AI

However, sustainable technology is not only about visible effects such as reducing energy consumption or CO2 emissions. It’s also about looking at entire processes – from conception to operation – to see where real change can take place. Pavlin: “We are also part of the shift towards more economical, more sustainable technologies. Ecodesign is a key principle in all our developments.”

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As an expert in solutions to support critical decisions, Thales is the first company to develop “frugal” AI based on algorithms that consume only small amounts of energy. Thales researchers prioritize knowledge-based symbolic or hybrid AI, which is far more energy efficient. Attention is shifting from Big Data to Smart Data, emphasizing quality over quantity, and to improving electronics design and implementation to offer electronic circuits that consume very little power.

The AI ​​of the future: neuromorphic computing

What about the next big thing? Although still in its infancy, neuromorphic computing could be the technology that completely revolutionizes AI.

Thanks to the pioneering work of the teams at the CNRS-Thales joint physics unit in Palaiseau, Thales is at the forefront of research in this field. Research director Julie Grollier explains: “Most AI people today focus on algorithms that help achieve a specific result. AI algorithms may be inspired by how the human brain and its neural networks work, but they are based on virtual neural networks running on conventional computer chips.”

“A neuromorphic chip, on the other hand, is a physical reproduction of a neural network. In my work, we take inspiration from the organization and structure of the brain – and in particular the way neural networks are organized as successive layers of neurons in the cortex – to design processors that run these algorithms very efficiently and with much less Effort can perform energy.”