The Carbon Footprint Of AI - Sustainable Software

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Will Buchanan

Program Manager

Artificial Intelligence (AI) has the potential to transform how we fight climate change. However, it also increasingly contributes to it: the carbon footprint of AI will grow exponentially over the next decade, and is projected to grow at a CAGR of nearly 44% globally through 2025.

The industry is trending towards bigger models (e.g. GPT-3): these require ever-growing datasets, compute budgets, and incur massive energy bills over the model lifecycle. Computational costs of AI models have been doubling every few months, resulting in an estimated 300,000x increase from 2012-2018. In the past two years, the number of parameters have grown 170X. Currently, training a single 213M parameter NLP deep-learning model through an architecture search can generate the same carbon footprint as the lifetime of five American cars, including gas.

One NLP model has the same carbon footprint of five cars, including gas.

Source: Emma Strubell, Carnegie Mellon University

Environmental sustainability should be considered as one of the principles towards responsible development and application of AI. The benefits of using such technology should outweigh its drawbacks, and it’s time to bring the conversation about the hidden costs of AI to the forefront.

‘Data Is The New Oil’

According to Gerry McGovern’s book ‘World Wide Waste’, 90% of data is not used – merely stored (which is cheap). He argues that because of this, the IT landscape is ~90% waste: 91% of pages analyzed got zero traffic from google, and more people have been to the top of Everest than the 10th page of search results. It’s no surprise that companies are seeking capitalize on this unprecedented amount of data collection. AI provides a way to make sense of massive amounts of data, but the current state-of-the-art requires a massive amount of data for training & validation. The more weights a model has, the more data it needs.

“The AI industry is often compared to the oil industry: once mined & refined, data, like oil, can be a highly lucrative commodity. Now it seems the metaphor may extend even further” – MIT Technology Review

There are promising, but emergent methods such as ‘few-shot learning’ and ‘less than zero-shot learning’ which will allow ML systems to learn & reason from a handful of examples. However, in the interim, the industry is currently trending towards increasingly large datasets, which require massive computational training budgets.

Bigger Is Not Always Better

The human brain is remarkably efficient; it can learn from a single example and apply this knowledge in a wide variety of contexts for the rest of its life. It requires relatively little energetic input: the brain only requires 20W to operate, and the global average of a human life requires around 8,000 lbs Co2/year.

Nature has done a significantly better job of engineering a brain than we have with AI. This represents significant potential for improvement: according to some of the brightest minds in the industry, ‘Artificial Intelligence’ isn’t yet close to being intelligent in any meaningful sense of the word. ML systems learn to perform a specific task by observing lots of examples, and pattern matching data by what can be viewed as a brute-force computational approach.

This requires staggering amounts of energy (data & compute resources) to perform pattern matching & superhuman statistical analysis. As a result, state-of-the-art approaches to AI are engaged in a computational arms race to achieve the next benchmark performance. A 2018 analysis led by Dario Amodei and Danny Hernandez of the California-based OpenAI research lab, an organization that describes its mission as ensuring that artificial general intelligence benefits all of humanity, revealed that the compute used in various large AI training models had been doubling every 3.4 months since 2012 — a wild deviation from Moore’s Law, which puts this at 18 months — accounting for a 300,000× increase.

Source: https://openai.com/blog/ai-and-compute/

It is noteworthy that there have been major efficiency gains in recent years: According to OpenAI, compute cost has been halving...