Introduction
Sustainability can be seen as both a mindset and a method to use the Earth’s natural resources in a way that allows for longevity of the Earth, and humanity as a whole. It is a symbiotic relationship between the Earth and its inhabitants – with great room for prosperity, abundance, and innovation. How interesting then, that advanced technologies such as Artificial Intelligence (AI), which may be flagged as negatively contributing towards issues in climate change, may be the great solution. AI for Sustainability holds great promise – in its present form, though, it does not always factor in the environmental impact of the development of AI, which must be used in a recursive way to reduce its own effects and the effects of other technologies onto the environment. There are many current initiatives, such as agricultural reshaping, algorithmically-based energy systems, and more unique solutions that we can celebrate today, and that look towards innovation for tomorrow’s benefit – in all sectors and coming fields – with a brand new precedent.
Sustainable AI
AI is undoubtedly helping companies in their day-to-day operations and goals, from data cleansing to predicting what a customer will purchase and when. Starting from the design stage all the way through to the implementation stage, there are impacts (with massive potential) on our society and environment, on a micro- and macro-scale. However, for all of its uses, AI and companies through their massive computing power are not always being held accountable for the energy and footprint created from AI. It is causing increasingly more damage to our environment and ecosystem at large – with AI training in some models costing nearly five times the carbon emissions of a single car’s lifetime. In its current form, AI is not contributing towards the solution. The environmental impact of AI training (and tuning) sits at the core drain of Sustainability of AI. As a first step, we need more transparency within AI and its processes and the impact it can have.
Top-level statistics around energy and water usage should start to be highlighted more within business operations. Sustainable data sources, power supplies, and infrastructure efficiency maximization are ways of measuring and reducing the carbon footprint from these AI systems, and digitizing our world will allow us to take advantage of these unseen opportunities and solutions for sustainability. Digitizing all solutions and creating transformation programmes enables us to gather even more information and metadata which in turn allows us to be more and more accurate when looking at both sustainability and AI. These are two giant fields. Addressing these aspects clearly gets to the heart of ensuring sustainable AI for the environment.
One concise case study exists in a large institution – by applying DeepMind’s machine learning models to Google data centers, energy costs were reduced by 40%, which is a remarkable achievement and an example of how we can use AI’s deep potential to save costs and the environment. One advantage of this process that accelerated the outcome was having access to good quality data. We can see here that the solution existed within the infrastructure – it was simply the recognition and optimization by the machine learning algorithm via DeepMind that saved energy from being wasted. This exists in many sectors and subsectors of industry – truly, untapped potential.
AI’s use in agriculture, for example, is another super subtopic within sustainability, with countless examples. Farmers in India are using AI to get 30% higher crop yields in groundnuts. The agriculture information management cycle at large is seeing a complete revolution in its end-to-end process – cost-savings, compensation for labor-shortages, better decision-making; you name it. Precision agriculture allows one to “monitor crop moisture, soil composition, and temperature in growing areas, enabling farmers to increase their yields by learning how to take care of their crops and determine the ideal amount of water or fertilizer to use.” These are all advanced techniques now available – thanks to AI. Seedo, an Israli-based tech company, has given empowerment to urban micro-farmers and those living in say, Latin America and the Caribbean Islands, by using hydroponics and aeroponics to commercialize indoor containers to grow all sorts of food in indoor and small spaces – produce such as fruits, vegetables, flowers, and herbs — up to five species at a time. Accessibility is the name of the game here, from individuals to small farming businesses, and beyond. Large-scale impact happens from a multitude of different sources, not only via large companies as a standalone example, but from small beginnings as well. This is one example of a game changer for environmentally vulnerable communities at large, and providing a reduced dependency on deforestation and other unsustainable methods.
Conclusion
AI for environmental applications could contribute up to $5.2 trillion to the global economy by 2030. The study of and outcomes from Sustainable AI are imminently needed for implementation, with more resources directed to its understanding and development. Transparent companies will thrive deeply in this regard, both in trust/reputation and long-term success. Companies are committing to restoring impacts from consumption from AI usage. Intel is committed to restoring 100% of its water use by 2025. Large companies are setting an important precedent, and existing initiatives are breaking new ground – Earthscan from Cervest is a platform that allows companies to assess and act on the climate risks that are associated with their assets. New initiatives emerge every day. American Airlines has partnered with Cool Down to fund various projects including AI in carbon reduction globally, with the opportunity given to customers to reduce their carbon footprint with every flight.
We will soon see an emergence and new precedents set for the future generation, with sustainability at every corner of artificial intelligence. Soon technology will empower and embrace the development of the natural world, preservation of our precious natural resources, and the sustainability of humanity.
Resources
https://education.nationalgeographic.org/resource/sustainability
https://www.deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40
https://intellias.com/artificial-intelligence-in-agriculture/
https://hir.harvard.edu/the-future-of-farming-artificial-intelligence-and-agriculture/
