Reduce the risk and energy of existing AI initiatives before proceeding and do not invest in AI use cases that could damage business value or the environment
Artificial intelligence (AI) holds the key to all kinds of economic, social, and environmental benefits, but it also poses a threat to natural resources. AI models are trained using power-hungry servers in data centres already notorious for their hefty carbon footprint and are now under attack for guzzling water. The trade-offs lie in combining “AI for sustainability” with the “sustainability of AI.”
Leveraging AI while Controlling its Appetite and Footprint
AI models and techniques can help drive a range of environmental goals. They can monitor and predict climate and weather-change trends such as global warming, manage waste and optimize recycling processes and operations, and make transportation, mobility and routes more efficient to enhance fuel efficiency and reduce carbon footprints.
But data centres that train AI already account for a lot of electricity consumption. One recent study argues that ChatGPT needs to “drink” a 500ml bottle of water for every simple 20-50 questions and answers (and GPT-4 is even thirstier).
Making AI itself more environmentally friendly is a key component of any sustainable technology program. Here are five ways to develop more sustainable AI.
No. 1: Make AI As Efficient as the Human Brain
Consider adopting so-called composite AI, which uses network structures to organize and learn similarly to the efficient human brain. Composite AI uses knowledge graphs, causal networks and other “symbolic” representations to solve a wider range of business problems in a more effective manner.
No. 2: Put Your AI On a Health Regimen
Monitor energy consumption during machine learning and stop training AI as soon as improvements flatten out and no longer justify the costs of continuing.
Keep data for model training local but share improvements at a central level. This type of “federated machine learning” reduces electricity consumption and bolsters data privacy.
Furthermore, reuse models that have already been trained, and contextualize them, if necessary, and use more energy-efficient hardware and networking equipment.
The trade-offs lie in combining ‘AI for sustainability’ with the ‘sustainability of AI.
No. 3: Run AI In the Right Place and At the Right Time
Manage when and where the AI workload happens. The carbon intensity of local energy supplies varies by country, generating authority, time of day, weather conditions, transfer agreements, fuel supply and other factors.
Balance follow-the-sun data centre workloads, which are better for clean energy production, with unfollow-the-sun measures, which are better for water efficiency. Additionally, use energy-aware job scheduling, along with carbon tracking and forecasting services to reduce related emissions.
No. 4: Buy New Clean Power Where You Plan to Consume It
Procure power purchase agreements (PPAs) when possible, or source renewable energy certificates (RECs) that reduce or offset greenhouse gas emissions and add new renewable energy to the grid where your organization will consume electricity.
In addition, prepare for future protocols. PPAs and RECs aren’t perfect or always available, so start building a detailed plan of clean power by location, time of day or both. This type of analysis can help you build a clean-power strategy, which regulators may require going forward.
No. 5: Make Environmental Impact a Key Factor in Considering AI Use Cases
Model environmental impacts, as well as business benefits, as you build AI strategy, and move forward with use cases that create more value than they destroy.
Reduce the risk and energy of existing AI initiatives before proceeding and do not invest in AI use cases that could damage business value or the environment.
