The carbon footprint of NLP and why I prefer extractive methods to create meta descriptions

In a recent study, researchers at the University of Massachusetts, Amherst, performed a life cycle assessment for training several common large AI models with focus on language models and NLP tasks. They found that training a complex language model can emit five times the lifetime emissions of the average American car (including whatever is required to manufacture the car itself!). 

While automation is key we don’t want to contribute to the pollution of  our planet by misusing the technology we have. In principle, using abstract methods and deep learning techniques offers a higher degree of control when compressing articles into 30-60 word paragraphs but, considering our end goal (enticing more clicks from organic search), we can probably find a good compromise without spending too many computational (and environmental) resources. I know it sounds a bit naïve but…it is not and we want to be sustainable and efficient in everything we do.

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