Generative AI and Carbon Footprints
Channel: EcoFAM
Date: 2023-05-27

This video is about the Generative AI and Carbon Footprints
Environmentalists frequently stress over the environmental impact of developing new AI models. The more advanced the AI, the more power it needs to function. 
Complex results like sentences, paragraphs, pictures, and even short videos are all within the capabilities of generative AI. 
It has long been used in autocomplete to propose search queries and in applications like smart speakers to create audio responses. 
But it only recently learned to generate natural words and photorealistic images.
It's hard to put a price on all the energy that goes into producing a single AI model, from building the computers to running it in production. 
In 2019, experts determined that the amount of energy needed to generate a generative AI model dubbed BERT with 110 million parameters was equivalent to a single human flying nonstop throughout the United States. 
The larger the model, the more accurate it is expected to be, and this is directly related to the number of parameters. 
The creation of the far bigger GPT-3, which contains 175 billion parameters, is projected to have used 1,287 MWh of electricity and produced 552 metric tonnes of carbon dioxide equivalent, which is the same as the annual emissions from 123 gasoline-powered passenger automobiles.
Carbon emissions can also be predicted by other factors besides size. 
Similar in size to GPT-3, the Big Science project in France developed the open-access BLOOM model, which uses 433 MWh of power to generate 30 tonnes of CO2eq. 
According to Google's research, a 100-1,000 fold reduction in carbon footprint is possible by adopting a more efficient model architecture and processor in a greener data centre, all else being equal.



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