The results underscore another growing problem in AI, too: the sheer intensity of resources now required to produce paper-worthy results has made it increasingly challenging for people working in academia to continue contributing to research. “This trend toward training huge models on tons of data is not feasible for academics…because we don’t have the computational resources. So there’s an issue of equitable access between researchers in academia versus researchers in industry.”Hao, K. (2019). Training a single AI model can emit as much carbon as five cars in their lifetimes. MIT Technology Review.
The article focuses on the scale of the financial and environmental cost of training natural language processing (NLP) models, comparing the carbon emissions of various AI models to those of a car throughout its lifetime. To be honest, this isn’t something I’ve given much thought to but to see it visually really drives the point home.
As much as this is a cause for concern, I’m less worried about this in the long term for the following reason. As the author’s in the article stake, the code and models for AI and NLP are currently really inefficient; they don’t need to be neat and compute is relatively easy to come by (if you’re Google and Facebook). I think that the models will get more efficient, as is evident by the fact that new computer vision algorithms can get to the same outcomes with datasets that are orders of magnitude smaller than was previously possible.
For me though, the quote that I’ve pulled from the article to start this post is more compelling. If the costs of
From where I’m standing this makes it seem that private companies will always be at the forefront of AI development, which makes me less optimistic than if it were driven by academics. Maybe I’m just being naive (and probably also biased) but this seems less than ideal.
You can find the full paper here on arxiv.