As artificial intelligence (AI) technologies become increasingly part of our daily lives and business operations, their high energy demands and environmental impact call for a more sustainable approach to building algorithms—the sets of instructions used to shape this technology.
Training large AI models can require enormous amounts of energy. For example, training an AI platform called GPT-3 required 1.287 MWh of electricity – that’s equivalent to the annual emissions of more than 100 gasoline cars.
Sustainable AI practices can reduce environmental impact, improve user experience, and enhance system reliability and performance, reducing the risk of potentially catastrophic failures. Global incidents such as the recent Microsoft-Crowdstrike IT outage highlight the need for more reliable, efficient, and resilient digital infrastructure.
Here are four ways AI algorithms can become both energy efficient and consumer-friendly:
1. Balancing the need for speed
The rapid growth of digital technologies has brought unprecedented efficiency and convenience, making instant responses and seamless online experiences the new standard for tech consumers. However, this increase in digital activity has placed enormous energy demands on data processing and transmission.
AI offers a promising solution. By figuring out how to shorten the steps required to solve a problem, AI can identify and eliminate redundant tasks, reducing the computing power needed to complete them. This improves energy efficiency and reduces the carbon footprint of digital systems and data processing tasks.
While more environmentally friendly, there is a risk that overly streamlined processes can reduce the functionality of certain technologies, such as voice assistants, recommendation algorithms or complex data analysis software. So designing AI to be more efficient has both pros and cons for consumers.
On the positive side, it means faster response times and smoother interactions, making our digital experiences more enjoyable. Smartphones and laptops perform better, batteries last longer, and devices are less likely to overheat. Lower energy consumption can reduce costs, potentially leading to cheaper services for consumers. More reliable services with fewer interruptions, especially during busy times, are another bonus.
There are some potential downsides. If AI becomes too streamlined, we may lose certain features or functions of certain technology. Users may feel like they have less control over how they use services, such as personalized streaming platforms, smart home systems, or customizable software applications. There may be an adjustment period as people get used to the new, faster ways that AI works. This can be frustrating for users at first.
As AI systems become more efficient and complex, people may find it harder to understand how their data is being used – raising privacy and security concerns. And overreliance on efficient AI could leave us more vulnerable to system failures if processes aren’t regularly checked by humans.
2. Dynamic workload management
AI changes the way systems perform by dynamically managing workloads. This means AI can intelligently adjust resources based on real-time demand, making systems run better and improving the user experience.
In today’s world, where digital platforms are crucial, especially with the rise of social commerce, strong network connectivity is essential.
During busy times, AI increases its capacity to keep everything running smoothly. Peak demand times often occur during business hours, especially in the middle of the workday when many people are online at the same time for work-related tasks. Demand is also high in the evenings when people stream more videos, play online games, and use social media.
Accurately predicting peak moments and identifying bottlenecks during high loads is challenging, but essential for continuous improvement.
AI enables dynamic workload management. It also improves the battery life of devices by using energy more efficiently and helps people stay connected, even during power outages. Network performance improves too, with AI preventing delays and disruptions by effectively managing peak loads. This means faster internet, fewer dropped connections, and a smoother online experience.
3. Optimize hardware
AI is ushering in a new era of power-efficient hardware for computers and smartphones, including energy-efficient processors like Apple’s M1 chip in MacBooks and Google’s custom TPU chips for AI workloads.
Environmentally friendly technology can reduce energy consumption, lower operating costs and ultimately lower prices for consumers.
Energy-efficient hardware is often synonymous with reliability. These devices are designed to function optimally within their power limitations and are less susceptible to overheating and hardware failure, resulting in fewer service interruptions and higher user satisfaction.
4. Integration of sustainability
AI is at the forefront of sustainable innovation. By optimizing its own operations, AI can significantly reduce its environmental footprint. For example, AI can monitor energy consumption, identify inefficiencies, and be powered by renewable energy sources such as solar and wind. This proactive approach to energy management minimizes AI’s carbon footprint and sets a precedent for sustainable technological development.
Devices made with energy-efficient components and recyclable materials offer a sustainable alternative without sacrificing performance. By choosing these eco-friendly technologies, consumers can enjoy their favorite apps and services while actively reducing their carbon footprint.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The authors are not employees of, consultants to, own shares in, or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.