The challenges of frugal AI

Summary

A CNRS article highlights the high energy cost of generative AI, presenting potential solutions to reduce it.

It notes that an increasing number of researchers are embracing frugal AI as a critical issue in today’s climate crisis.

The editorial perspective

The article quickly explores various solutions to reduce AI’s environmental footprint. The first approach discussed is edge computing, which aims to shorten the distance data must travel to execute an algorithm.

The article states:

"Humanity continues to follow the assumption that the bigger the model, the better it is. However, this is a highly inefficient approach. We also know that there is significant room for improvement in efficiency, even though we have yet to unlock the scientific breakthroughs needed to fully realize this potential."

The article’s conclusion is not particularly optimistic, but it does not ignore the challenges of scaling up frugal AI solutions or the societal questions surrounding AI’s growing presence in our daily lives. This critical perspective is valuable, especially in the context of rapid growth in generative AI usage.

One of the article’s key contributors is Denys Trystram, who co-authored the piece published in The Conversation (see referenced resource in this collection).