Carbon Footprint of Artificial Intelligence: How It Is Accelerating the Risk of Climate Change


Artificial Intelligence or AI is an ability of a computer or a robot to execute the tasks of intelligent human beings. Artificial Intelligence has the character traits of information processing, capacity to discover meanings, ability to do reasoning, skill to generalize, and efficiency to learn from the experiences of the past. Now from solving mathematical problems to playing online games, digital computers carry out a lot of complex tasks.


For its excellent memory capacity and processing speed, the application of Artificial Intelligence has increasingly become a significant part of science. Artificial Intelligence is used in computer search engines. It is utilized for handwriting and voice recognition, music or movie recommendation, medical diagnosis, etc. All these tasks rely completely on the techniques of deep learning that train computers to identify the data patterns. This training is one of the most energy-intensive processes.




For this training, powerful digital computers and a tremendous amount of energy are used. Energy usage is always associated with the burning of fossil fuels and greenhouse gas emissions. Greenhouse gases are the main causes behind global warming and climate change. That is why Artificial Intelligence is often claimed to leave an enormous carbon footprint that accelerates the degradation of the environment of our planet and increases the risks of the climate crisis. Some reports show that from 2012 to 2018, the computing power that was utilized in the deep learning process increased almost 300,000-fold.


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To understand the climate impact of Artificial Intelligence better, here we should cite some examples. Some researchers have found that the amount of carbon footprint produced due to the training of one single Artificial Intelligence is approximately two hundred eighty-four tons of carbon dioxide, which is equivalent to 5 times the lifetime carbon emissions of one average car. One deep learning model known as Generative Pre-trained Transformer 3 or GPT-3 was designed to produce language like human beings. For one single training session, it needs a huge amount of energy that is equivalent to the annual consumption of nearly one hundred twenty-six Danish homes and leaves a carbon footprint that is equivalent to traveling almost 700,000 km by car.


If researchers of computer science become conscious of the environmental effect of their tasks, it would be easier for them to take concrete steps for minimizing the carbon footprint of Artificial Intelligence. One of the best strategies is to conduct deep learning training on digital computers located in regions, countries, or states that have sources of low-carbon energy. As an example, we can say that the amount of carbon that emits due to the training of a computer model in a country like Estonia is almost sixty-one times the amount of carbon emissions for training a similar model in a country like Sweden.




Researchers also have discovered an interesting fact. They have found that in many places, the carbon intensity varies determined by the different times of a day. Hence, for training, the computer model researchers or experts choose the time when carbon intensity is low. It will reduce the amount of carbon footprint produced by deep learning to a great extent. If researchers can design efficient algorithms, it will reduce the carbon emissions needed for the purpose of model training. To tackle emissions, they can also select energy-efficient hardware and keep their settings to best work with their energy usage.


Also Read: Facts about climate change you should know

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