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Are the innovations emerging from machine learning sufficient to offset the environmental costs of computing and beyond?

In time, I believe machine learning and artificial intelligence in general are going to make significant contributions to almost everything, but the big questions is whether we’re going to see anything significant in the short-term to make a difference.

Computing, both personal and professional, has become an invisible contributor to our collective carbon emissions footprint.

While reading an article about the possible switch to memristors to help make neural networks more efficient, I come across a staggering fact:

Existing AI is extremely energy-intensive—training one AI model can generate 284 tons of carbon dioxide, equivalent to the lifetime emissions of five cars.

While on Reddit, in r/energy, someone shared an article discussing the environmental implications of global bitcoin mining:

A study from the Cambridge Center for Alternative Finance released on Monday estimates that the global bitcoin mining industry uses 7.46 GW, equivalent to around 63.32 terawatt-hours of energy consumption. The study also notes that miners are paying around $0.03 to $0.05 per kWh this year.

… and I was prompted to share a thought I’d had:

Are the innovations emerging from machine learning sufficient to offset its own cost to the environment? It’s an interesting question when we take into account the fact that the fossil fuel sector is using algorithms derived from artificial intelligence to seek out new sources.

We know that the fossil fuel sector has been on the take for such a long time, it’s almost been forgotten … almost:

Fossil fuel companies are benefiting from global subsidies of $5.3tn (£3.4tn) a year, equivalent to $10m a minute every day, according to a startling new estimate by the International Monetary Fund.

Compounding the possible positive impact of machine learning is the lack of interest in the real world impact it could have on the part of the researchers:

If the community feels that aiming to solve high-impact real-world problems with machine learning is of limited significance, then what are we trying to achieve?

In time, I believe machine learning and artificial intelligence in general are going to make significant contributions to almost everything, but the big questions is whether we’re going to see anything significant in the short-term to make a difference.

Photo by Billy Huynh on Unsplash

By Wayne Smallman

Wayne is the man behind the Blah, Blah! Technology website, and the creator of the Under Cloud, a digital research assistant for journalists and academics.