Artificial Intelligence is one of the main technological innovation drivers today and it is revolutionising every sector, from healthcare to transport, from finance to commerce.
In addition to the countless benefits, the increase in the AI’s capabilities and fields of application generates numerous debates on the possible negative effects of this technology. From a regulatory point of view, this has led to the implementation of the AI ACT, but another issue that has emerged and needs proper analysis is the energy impact of AI.
What is the energy impact of Artificial Intelligence?
Every online interaction relies on data stored in data centers that consume energy to function. According to data from the International Energy Agency, data centers currently consume between 1 and 1.5% of global electricity.
This percentage could rise with the boom in Artificial Intelligence: Large Language Models (the language models on which Generative AI solutions are based) are being trained with ever larger text datasets and this requires the use of ever more powerful servers. Considering the benefits that Artificial Intelligence produces in every field in which it is introduced, the challenge today is therefore how to reconcile the development of AI with environmental sustainability.
Central to the energy impact of AI is the process of training AI models. This involves feeding large amounts of data into algorithms, which learn to recognise patterns, make predictions or perform other tasks. Training these models typically requires enormous computing resources, often in the form of high-performance computing clusters or specialized hardware such as graphics processing units (GPUs) or tensor processing units (TPUs).
Deep learning models have a particular need for computing power: training a single large-scale model can consume energy equivalent to the power consumption of five cars. Moreover, as AI applications become more complex and data-intensive, energy demand is likely to increase further.
Beyond the training phase, the operational energy consumption of AI systems also deserves attention. The AI applications used, such as those powering recommendation engines, autonomous vehicles or smart infrastructures, require continuous computing power to process data and make decisions in real time. Although the power consumption of individual AI applications may seem modest, the cumulative effect on millions of devices and servers worldwide can be significant.
Moreover, as AI is incorporated into everyday devices and infrastructure through the Internet of Things (IoT), the energy demand of AI-powered systems is expected to increase. This proliferation of AI-driven devices, coupled with the increasing digitisation of society, underlines the importance of addressing the energy efficiency of AI algorithms and implementations.
How can Hybrid AI reduce the costs of Artificial Intelligence?
Based on predictions, it makes sense for the tech industry to work now to reduce the energy consumption of Artificial Intelligence, making it more sustainable.
Hybrid AI, an innovative combination of symbolic and non-symbolic AI, is emerging as a game-changing force in Artificial Intelligence. It balances the strengths of two distinct facets of AI, ushering in a new wave of transformative solutions that enable real-time decision making and enhanced creativity. This synergy creates user-friendly and authentic systems that promote seamless interaction between people and their digital environments.
Hybrid AI can help reduce the costs related with implementing and running AI systems and, so, minimize the impact of Artificial Intelligence for what concerns energy use.
Organizations that want to innovate their business models and spread digital trust have to apply an Hybrid Artificial Intelligence strategy that takes into account two crucial aspects: satisfying the needs of the application of AI, but managing the flexibility of AI-related costs and the impact of the use of this technology in terms of energy sustainability.
Here are some ways that can make this process accomplished:
- Maximizing resources: Hybrid AI enables more efficient use of heterogeneous resources, offloading less intensive workloads to edge devices or less powerful hardware, saving on computing and infrastructure costs;
- Flexibility: the hybrid approach allows the infrastructure to be flexibly scaled on demand, so it’s possible, for example, to assign more cloud resources during peak periods and release them when demand falls, ensuring more dynamic and efficient cost management;
- Use lean models: improved AI models help reduce computational requirements and associated costs; this is essential in cases where the most basic models still fully satisfy the application’s needs;
- Edge computing: by shifting some of the decision making to edge devices, it is possible to decrease the need to transfer large amounts of data across networks, saving data transfer resources and leveraging local computing capacity;
- Tailoring to available resources: Hybrid AI makes it possible to adapt computing resources based on the particular features of the workload and reduce infrastructure costs; in cases where it’s possible to use cheaper local resources instead of expensive cloud services, this is the best way to reduce the energy impact;
- Use managed services: the use of managed cloud computing services to deliver AI systems cuts operational costs and makes infrastructure management lean and simple;
- Model lifecycle optimization: checking and optimizing AI models over time helps maintain good performance while keeping costs low; this includes recurring training, model compression, and parameter optimization.
Not only an environmental issue…
As we seek to realize the transformative potential of AI for economic growth, social progress and scientific advancement, it is essential to consider sustainability and responsible energy use. Reconciling innovation and environmental sustainability requires a collective effort to develop and deploy AI technologies in a way that minimizes their carbon footprint and maximizes their positive contribution to society.
Moreover, addressing the energy implications of AI is not only an environmental issue, but also an equity and access issue. Energy-intensive AI systems can exacerbate existing gaps in resource allocation and access to technology, particularly in regions with limited energy infrastructure or high energy costs. Ensuring equitable access to energy-efficient AI solutions is essential to promote inclusive growth and reduce socio-economic inequalities.
In conclusion, the energy implications of Artificial Intelligence present significant challenges and opportunities for sustainable development. By embracing innovation, fostering collaboration and adopting a more integrated approach to energy management, we can both harness the power of AI and protect our planet for future generations. Through concerted action, responsible stewardship, and the adoption of Green IT best practices, we can create a future where AI drives progress in harmony with the environment.
