sustainable AI

What is sustainable AI?

Sustainable AI is the use of artificial intelligence (AI) systems that operate in ways contingent with sustainable business practices. Many current enterprise AI systems have a reputation for being detrimental to the environment, but doable practices can mitigate this.

Those with firsthand knowledge of developing and implementing AI models understand that the arduous process of training AI models requires enormous amounts of energy, leading to unsustainable emissions and air pollution. Certain studies over the years have shown that the process of training just one machine learning (ML) model can match the carbon emissions of multiple cars combined.

Therefore, AI models must be made efficient so that the training process won't require large amounts of energy for computing power. Also, all components within an AI system should be optimized as much as possible to limit power consumption. In so doing, businesses will achieve new AI modeling and optimization practices that reduce the negative environmental impacts of their AI systems.

Among the foremost techniques to accomplish this is called sparse modeling, which is growing in popularity due to its speed and energy efficiency. Traditionally, ML algorithms attempt to parse through every insight they gather from huge data sets. However, sparse models can operate on small amounts of data with a narrower focus and are easier to train due to the need for less data.

When optimizing the hardware components used for developing and training AI models, enterprises should note that developers and researchers have worked on emerging devices that deliver faster computing with lower energy consumption rates. For instance, researchers at MIT are developing burgeoning technology to accomplish this.

Current and future use cases of sustainable AI

In addition to sustainable operations, the power of AI systems can also be harnessed within many sustainability initiatives relating to pollution, natural disasters and climate change. These applications include the following:

• Precision agriculture driven by AI.
• Weather predictions.
• Natural disaster response.
• Preparedness and strengthening infrastructure, such as power suppliers.

AI analytics can be used along with drones and other similar technologies to monitor and reduce the effects of natural disasters such as floods. Similarly, climate and risk assessments can be done with these AI tools, which work together with internet of things devices to protect crops as well as other critical assets and infrastructure. Also, predictive AI is crucial in forecasting future emissions and air pollution based on existing data and trends.

In addition, many of these AI-based technologies are used in reforestation efforts in the Amazon. AI systems can analyze data from sensors, drones and satellite imagery to monitor forests and how they evolve or devolve over time. With their predictive capabilities, ML algorithms can examine past data to determine likely future deforestation scenarios.

When implemented and executed correctly, sustainable AI will mitigate the negative effects that traditional AI and ML model training and optimization practices have on the environment without sacrificing the quality of its predictions and outputs. In addition, it is applied to various use cases to help people protect their communities and natural resources. Sustainable AI is considered synonymous with the term green AI, which also describes efforts to improve these negative effects and ensure the future of AI brings with it more benefits than drawbacks.