Artificial intelligence (AI) is ready for business. In 2021, 35% of companies already reported using AI in their day-to-day business. Another 42% said they were researching AI for themselves. Knowing these numbers, it's no longer possible to assume it's just a trend: artificial intelligence has arrived in companies!
The number of use cases is steadily increasing and the issues to be solved are becoming even more complex. As a result, the size and number of AI models must grow in line with the emerging ideas in companies and the operation of these models must become more and more routine.
Since data-intensive models often have to be trained for days on specialized hardware, this creates an ecological footprint that should not be underestimated and that many are often unaware of.
Already in 2019, model training or model finding in the field of natural language processing generated as much CO2 as five American cars including gasoline consumption during their entire life cycle. And the trend is upward.
The continuing entry of AI into companies and the resulting increase in CO2 consumption has given rise to a movement that aims to bring a more efficient and ecologically conscious design of artificial intelligence to research and companies under the buzzword "Green AI."
The size of the models is continuously increasing. The foundations for sustainable design must therefore be laid sooner rather than later.
What does a "green" design of artificial intelligence mean for companies?
The term "Green AI" is based on the topic of Green IT. Green IT deals with the general environmentally friendly and resource-saving design of information and communication systems throughout their entire life cycle. In the case of sustainable AI in the ecological sense - keyword "Green AI" - the focus is primarily on modeling and model operation. Computation, whether in training or prediction, should be made as efficient as possible and energy consumption kept to a minimum.
In contrast to research, model operation is a big driver for greater environmental sustainability in the company. Making predictions or performing analysis causes about 90% of the model's energy consumption, according to a projection by AWS (Amazon Web Services).
Making AI systems more environmentally friendly does not necessarily have to come at the cost of performance or involve high investments. Even an algorithmically reduced execution time leads to a better ecological footprint and a gain in time as well as resources. Thus, when models are optimized, performance often benefits as well.
Sustainability is always an awareness issue. If the recording of energy consumption or the CO2 equivalent caused is introduced, awareness of the topic increases. With organizational frameworks and standards for the development process, responsibility no longer hangs individually with the developers. Measures are thus implemented uniformly.