To test its new approach, Hugging Face estimated the overall emissions for its own large language model, BLOOM, which was launched earlier this year. It was a process that involved adding up lots of different numbers: the amount of energy used to train the model on a supercomputer, the energy needed to manufacture the supercomputer's hardware and maintain its computing infrastructure, and the energy used to run BLOOM once it had been deployed. The researchers calculated that final part using a software tool called CodeCarbon, which tracked the carbon emissions BLOOM was producing in real time over a period of 18 days. Hugging Face estimated that BLOOM's training led to 25 metric tons of carbon emissions. But, the researchers found, that figure doubled when they took into account the emissions produced by the manufacturing of the computer equipment used for training, the broader computing infrastructure, and the energy required to actually run BLOOM once it was trained. While that may seem like a lot for one model--50 metric tons of carbon emissions is the equivalent of around 60 flights between London and New York--it's significantly less than the emissions associated with other LLMs of the same size.

The Shadoks were "anthropomorphic creatures with the appearance of chubby birds, with long, filiform legs, tiny and prehensile wings, and original hair." Living on a planet with uncertain contours, their main life goal was to build a rocket to land on the earth. To achieve this, they invented the "Cosmopump" intended to pump the "Cosmogol 999" to fuel their rocket. Let's forget the Shadoks for a moment and focus on the effort of reading that led us to these lines. Our brains consumed energy during this turmoil.

Training artificial intelligence is an energy intensive process. New estimates suggest that the carbon footprint of training a single AI is as much as 284 tonnes of carbon dioxide equivalent – five times the lifetime emissions of an average car. Emma Strubell at the University of Massachusetts Amherst in the US and colleagues have assessed the energy consumption required to train four large neural networks, a type of AI used for processing language. Language-processing AIs underpin the algorithms that power Google Translate as well as OpenAI's GPT-2 text generator, which can convincingly pen fake news articles when given a few lines of text. These AIs are trained via deep learning, which involves processing vasts amounts of data. "In order to learn something as complex as language, the models have to be large," says Strubell.