How can artificial intelligence help build a greener future?


In recent decades, artificial intelligence (AI) has gone from the stuff of science fiction to much of scientific fact. It is now an integral part of our present and we are beginning to see the impact it is having 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 favorite subject in fiction, be it books, graphic novels or movies – think 2001: A Space Odyssey, The Matrix or even The Terminator. In the interest of drama and tension, the AI ​​depicted is more often malevolent, with the machines 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 titled “The Machine Are Taking Over: Computers Outdo Man At His Work – And Soon May Outthink Him,” which described machines that were capable of more than analytical and decision-making tasks in business and industry, but also to learn on their own.

© 123RF

Fast forward to 2023 and these machines are already hard at work among us. However, far from being the existential threat to humanity that they were thought to be 60 years ago, they now make an invaluable contribution to our daily lives. So where exactly can AI contribute to environmental issues?

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

Clearer skies

Thales’ PureFlyt flight control system is an excellent example of what can be done. It uses AI to optimize aircraft trajectories and thereby reduce both fuel consumption and noise pollution, with the target of a 10% reduction in CO2 emissions from aircraft by 2023. The flight tests also used AI to simulate around two billion scenarios and reach the equivalent of 100 million flight hours.

See also  Your next negotiating partner: Artificial Intelligence

In addition, research is being done on how to use these AI-assisted trajectories to reduce the environmental impact of aircraft wakes by having aircraft fly at slightly lower altitudes, as well as having aircraft perform continuous descents to landing as opposed to descending levels. Both measures can help reduce fuel consumption and air pollution.

AI is also having a positive impact on air traffic control, which will have to deal not only with traditional aircraft, but also with the increase in the number of unmanned aerial vehicles in the sky. Instead of AI replacing human beings, Thales believes it will enhance their capabilities by freeing them from repetitive, low-value tasks and allowing them to focus on more critical areas where human intervention is vital.

This emphasis on the human will create safer and more efficient airspace management; in addition, AI’s advanced predictive capabilities will lead to fewer delays in the air and on the ground, which in turn means less fuel burned and a reduced carbon footprint for airlines.

© 123RF

Climate monitoring

AI also plays a vital role in monitoring and understanding climate phenomena, which is key to combating climate change. Its most obvious application is in increasing the image processing capabilities of satellites, which will be better able to analyze and predict climate phenomena.

Thales is contributing to this effort, for example, within the European Earth observation and climate monitoring program Copernicus, which will help to better understand the impact of human activity on the environment.

Green mobility

However, it is not only in and above the sky that AI is making significant contributions to environmental issues. Thales has also developed sophisticated, environmentally responsible rail transport systems that use learning and knowledge-based AI and consume less energy.

See also  Limitless possibilities – AI technology generates original proteins from scratch

Driver advisory and traffic management systems, along with automated operation management systems for both metro and autonomous trains, optimize energy consumption through carefully defined driving strategiesand by calculating optimal acceleration and deceleration profiles in real time.

These systems also make it possible to predict incidents on the network, thus reducing unexpected train stops caused by, for example, obstacles on the tracks. Station monitoring systems also analyze energy consumption in real time, with sensors that determine the exact energy needs according to the passenger flowensuring that energy consumption meets the requirements.

Energy consumption and IoT

One of the areas where artificial intelligence really excels is in the face of what Gregor Pavlin, program manager and senior scientist at Thales Research & Technology, calls “rogue data.” Modern systems, for example in the field of IoT (Internet of Things), collect huge amounts of different types of data. Traditionally, this data is processed in a central computer or cloud and then the results are sent back to the on-premise application. However, this not only raises security issues, but is also neither cost nor energy efficient.

Thales’ expertise has allowed it to develop a platform for simple integration of distributed AI algorithms in the field that process most of the data close to the application – the so-called edge computation – and transfer only the processing results, keeping the data behind a secure firewall and significantly reducing of energy consumption.

Lateral Thinking: Ecodesign and Lean AI

However, sustainable technologies are not just about visible impacts, such as reducing energy consumption or carbon emissions. It also involves looking at entire processes – from concept to operation – to see where real change can occur. According to Pavlin: “We are also part of the shift to more economical, more sustainable technologies. Eco-design is a key principle in all our developments.”

See also  China's AI Chatbots get angry when asked about Xi Jinping's leadership

© 123RF

As an expert in critical decision support solutions, Thales is the first company to develop “frugal” AI based on algorithms that require only small amounts of energy. Thales researchers are prioritizing knowledge-based symbolic or hybrid AI, which is much more energy efficient. Attention is shifting from big data to smart data, favoring quality over quantity and to improving electronics design and performance to offer electronic circuits that consume very little power.

The AI ​​of the Future: Neuromorphic Computing

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

Thales is at the forefront of research in this area, thanks to pioneering work carried out by teams at the CNRS/Thales Joint Physics Unit in Palaiseau. Director of Research Julie Grolier explains: “Most people working in AI today focus on algorithms that help achieve a specific result. AI algorithms may be inspired by the workings of the human brain and its neural networks, 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 cerebral cortex – to develop processors that can run these algorithms very efficiently and with much less energy.”


Leave a Comment