Check out Artificial Intelligence stories in the media, and you’ll probably see most of them talking about the amazing breakthroughs this technology will bring us in coming years. But few articles and videos discuss the darker side of AI and its impact on the planet’s ecosystems.
From drying up natural water bodies to pressuring energy reserves to the tipping point of crisis, AI's relentless demand for the planets resources continues as the tech evolves. Can we find a solution to the ever-growing demand when we live in an environment with finite resources?
Let’s discuss the ecological footprint of AI, and specifically the impact the AI music industry has on the environment.
How AI Music Production Affects the Environment
AI music creation requires substantial computational power: generating new compositions, processing audio effects, and training large machine learning models. This unfortunately also comes at a cost to the environment: data centers supporting AI runs on vast amounts of electricity and cooling. Some areas where AI music creation impacts the environment include the following.
Energy Consumption by Data Centers
The AI music relies on such complex algorithms and deep learning models that require a great deal of computational power. The data centers behind the computing use electricity day in and out to keep the servers running cool.
According to the International Energy Agency's 2022 report, data centers contribute roughly 1% toward global electricity demand, with the number expected to rise as AI technology continues to grow. Most data centers themselves run on electricity derived from fossil fuel and add to the amount of greenhouse gases emitted into the atmosphere.
Computing Infrastructure for Model Training
Training deep learning algorithms on large datasets is what it takes to create the AI models behind the music production. It’s within this training phase that considerable amounts of energy are consumed sometimes, even running into several thousand kWh for huge models. Training AI models once in themselves may mean nothing, but if replicated over a great number of projects and users, then it starts adding up rather quickly.
But that doesn’t stop many companies from their vigorous work of reducing the energy requirements for training AI models. One can actually optimize the algorithms, enhance model efficiency, and thereby reduce the power needed to build effective AI tools.
Greater Demand for Hardware
Companies like NVIDIA, the biggest chip manufacturer in the semiconductor industry, continue to see increased demand for its advanced GPUs and TPUs. This equipment utilizes rare earth minerals extracted in slave-labor conditions in third-world countries. If you’ve ever seen pictures of an open cobalt mine in the Congo, you’ll recoil at the working conditions.
These mines also don’t have the same international standards and safe practices in place. That means there’s tons of environmental pollutants going into the earth and groundwater systems every day. While companies like NVIDIA and Google are making moves to design more efficient equipment, they’ve yet to change the sourcing policies for the materials that go into this equipment.
Positive Opportunities in Mitigating AI-Related Environmental Impact
It’s not all doom and gloom when it comes to AI's impact on the environment. There are ways manufacturers and users can mitigate its impact and improve the sustainability aspects of the technology.
Green AI Music Production
AI-based mastering and mixing can enable producers to achieve great results on a laptop without the need for expensive and power-hungry equipment. Because AI allows collaboration of artists, producers, and sound engineers to work remotely, this means not as many of these individuals have to travel for studio sessions.
By saving transportation-related emissions, AI reduces the carbon footprint of producing music. By giving AI the ability to help produce music, the process will require fewer resources to maintain its high quality and be greener in some sense.
Optimizing AI Models for Sustainability
The biggest tech players and researchers alike know the environmental cost that befalls AI, so they’re investing in ways to make the models more efficient. Several strategies put in place already help decrease the carbon footprint of AI.
Since training consumes so much energy, developers and researchers looked into how to make their models more energy-conservative, reducing the resources they need to operate these AI systems. This term, “Green AI,” encourages the development of smaller models with optimized computational power, without dropping performance.
Renewable Energy Data Center Support
With data centers being such a huge drain on energy resources, companies like Google and Microsoft have pledged to do something about it. The tech giant says it will have all its data centers running off renewable energy by 2030, realizing its goal of net-zero carbon emissions.
AI for Environmental Awareness in Music
Many artists and creators have taken to AI-generated music as a medium to express the need for sustainability. By creating soundscapes related to or even emulating the beauty of nature, AI music is likely to spur its listeners to be more considerate of the well-being of the planet.
Projects like this really open up a dynamic about sustainability and are positive uses of artificial intelligence as an environmentally positive force in the arts.
The Future of Sustainable AI Music
While AI technology is improving, optimism can be cast on its role in the future of sustainable music production. The following current and developing trends lighten the carbon footprint of AI music towards the future. Smaller, more efficient AI models: There’s a study dealing with AI models with reduced parameters using reduced computational power.
There’s plenty of strategies we can use to make AI music more sustainable. A lot of what’s going to happen in the AI industry and its approach to sustainability will unfold at the development and research level in coming years. However, locally, we can all do our part by running efficient hardware and lightweight models that reduce our own need for resources to run these systems at a private, retail level.
It’s possible to envision a future for AI music where solutions are as astute, creative and resourceful as the technology itself, giving us the benefits of AI while causing the least harm to the planet.
Want to experience a sustainable AI Music Generator?