Embracing Green AI: Balancing Innovation and Sustainability
October 2024, by Vitalija Narstyte
Since the debut of ChatGPT in November 2022, the use of Generative AI has surged across industries. New AI-powered services and products are emerging daily, integrating into our everyday lives and transforming the way we do business. The potential of AI seems limitless. The promise of generative AI to revolutionise industries has generated an unprecedented level of excitement, with businesses eager to leverage its capabilities.
Amidst this enthusiasm, there is growing interest in how AI can help tackle climate change. AI excels at detecting patterns in data, identifying anomalies and similarities, and using historical knowledge to predict future outcomes. This capability makes AI invaluable for monitoring the environment and aiding governments, businesses, and individuals in making more eco-friendly decisions. For example, AI can detect when oil and gas facilities emit methane, a potent greenhouse gas contributing to climate change. Additionally, AI algorithms are being used to optimise energy consumption in buildings, predict weather patterns for renewable energy sources, and even monitor deforestation through satellite imagery.
However, the conversation about the environmental cost of large AI models often gets overshadowed. The more powerful and sophisticated the AI, the more resources it consumes. While it can be challenging to calculate the exact carbon footprint of a single AI model, factors include the energy required to manufacture computing hardware, develop the model, and run it. Training a single GPT-3-like model has been estimated to produce significant CO2 emissions – comparable to the annual emissions of over 100 petrol-powered cars. And that is just for training; deploying large AI models can have an even greater environmental impact. Data centres that house these AI models consume vast amounts of electricity, often generated from non-renewable sources, contributing further to greenhouse gas emissions.
These concerns have led to the emergence of Green AI. Green AI focuses on developing and applying AI technologies that emphasise energy efficiency and environmental sustainability. It aims to reduce CO2 emissions by using and training large models. Researchers and tech companies increasingly prioritise the ecological impact of their AI solutions, seeking ways to make AI more sustainable without sacrificing performance.
Green AI solutions fall into two categories: Mitigation and Oversight.
Mitigation involves creating more efficient models that require less energy. Researchers are exploring ways to compress large models into smaller ones that maintain accuracy but consume less energy. Google’s TensorFlow Lite is an example of a framework designed for deploying lightweight models on mobile and embedded devices, reducing the computational load and energy consumption.
Oversight relates to monitoring and predicting AI’s carbon footprint. This includes developing tools to estimate the energy consumption and emissions associated with AI workloads. Tools like carbon footprint calculators are becoming essential for increasing awareness of AI’s environmental impact. For example, Google’s Cloud Sustainability Calculator helps estimate the carbon footprint associated with using the Google Cloud Platform, allowing businesses to track and manage their emissions. Similarly, the Green Algorithms project provides an open-source tool to estimate the carbon emissions of computational tasks, promoting transparency and accountability.
Balancing energy efficiency with model accuracy can be challenging. However, the trade-off is often worth it when considering the environmental benefits. By focusing on algorithmic efficiency and leveraging hardware designed for low power consumption, It is possible to develop AI solutions that are both effective and eco-friendly.
Embracing Green AI also has significant business advantages. Companies that adopt sustainable practices can reduce operational costs by lowering energy consumption. Moreover, they can enhance their brand reputation by demonstrating a commitment to environmental responsibility, which is increasingly important to consumers.
While AI offers immense innovation potential, it is crucial to consider its environmental impact. By adopting Green AI practices, businesses can continue to leverage the benefits of AI while contributing to a more sustainable future. The transition to Green AI is not just about technology; it is about reshaping business models and strategies to prioritise sustainability.
At MM-Eye, we know that harnessing the power of AI is about making smart, data-driven decisions that align with consumer expectations. Our Say Do Sustainability Study (SDSS) has shown that consumers are increasingly scrutinising the environmental impact of the brands they support. This is why integrating Green AI practices is so important, and we help businesses identify emerging sustainability trends and consumer demands.
If you are interested in learning more about Green AI and how it can benefit your business, contact us today at info@mm-eye.com or use the form below to book a consultation.