By: Parv Srivastava, Senior Consultant (New Ways of Working - Transformation), LTIMindtree

Artificial Intelligence – once a futuristic concept, is now seeing a lot of applications in both personal and industrial avenues. It refers to the capability of a computer to learn from examples, experiences and understand patterns from data to solve problems and make decisions. From simple voice processing to recommending complex policy decisions by computing loads of fragmented data, there is no doubt that AI technology can transform decision-making and operations. It does, however, require a lot of resources in terms of computation power, electricity, data for training and running new AI models. If these processes are not optimized – it can lead to tons of carbon emissions.

With global warming at an all-time high and climate change impacting the Earth severely, the world requires prompt action on making sustainable efforts. Therefore, emitting carbon emissions to develop AI models to achieve sustainable results seems to be a paradox. The question that arises is simple – How to optimize AI in a way where its models and training not only emit fewer emissions but also help in saving the planet by reducing carbon emissions in different industries.

Training AI models efficiently before reaping the benefits

One of the possible ways of training AI efficiently is to integrate the AI ecosystem with open-source communities like TensorFlow, PyTorch so that the developed models can be used by companies at fractional financial and environmental costs by leveraging on the expertise and the data. Secondly, running iterations on neural networks also results in a more power-efficient model by eliminating the weakest connections [1]. This process is also called model compression. The other simple yet effective way to reduce emissions is by reducing the number of experiments during training using more efficient hyper-parameter search methods.

There is no better way to save the planet than to use AI technologies to the advantage for the cause of sustainability, These systems are more efficient and their seamless integration across the planet can help achieve better results than any human or community ever can.

The two disruptive trends- decarbonization and digitalization can be made possible through the use of artificial intelligence. Decarbonization essentially reduces net carbon emissions. On the other hand, digitalization refers to the process where a traditional business approaches an enterprise to acquire specialized software solutions to transform their business model from analog to digital. Processing data can be useful in developing countries and regions where access to the internet is limited and have rudimentary privacy protection policies. Therefore, artificial intelligence can rectify redundancies in information by identifying patterns and relationships, which help to predict risks and opportunities. This results in contributing to enhancing knowledge and situational awareness.

Industries working towards net-zero with AI

It is important in industries such as energy and construction which use particularly complex machines. Artificial Intelligence can help such projects to process continuous information of approximately 3000 variables by predicting potential faults and breakdowns. This can result in economic and sustainable efficiency in high-scale projects. The use of machine learning and AI can significantly optimize energy generation through mapping it with the overall demand by using smart sensors and meters which can collect data for monitoring and analyzing energy usage in buildings. AI is already being used in applications like google maps which indirectly reduce emissions by optimizing navigation for efficient transportation. [2]

Satellite imagery can detect changing patterns in the usage of water, land and predict natural disasters by tracking past trends. It can also track ocean locations that are difficult and inaccessible to reach to save biodiversity by tracking illegal fishing and conditions such as the pH level, temperature, and pollution levels of the ocean. It can also forecast weather, soil, and water conditions below the surface to predict droughts to help make informed policy decisions. [3] Artificial Intelligence is also helpful in tracking pollution levels and environmental data in real-time and then providing warnings to people living in urban areas.

Turning the future into reality sustainably

Artificial intelligence has the power to bring high-scale advancements in several sectors by efficient optimization and utilization of resources. It is also really important to understand that AI and related technologies can be computationally expensive so if the technology is being powered by electricity which is being generated by coal, the achieved efficiency will be less than the actual pollution caused. Therefore, it is of utmost importance to build a renewable energy-based data center. All these innovations should be scalable to provide high-impact solutions rather than just small improvements.

References
https://www2.deloitte.com/uk/en/blog/experience-analytics/2020/green-ai-how-can-ai-solve-sustainability-challenges.html
https://www.forbes.com/sites/glenngow/2020/08/21/environmental-sustainability-and-ai/?sh=1b21883a7db3
https://www.greenbiz.com/article/what-artificial-intelligence-means-sustainability