Climate changes in the world

Barely a week passes without another dramatic report about humanity and the planet reaching a climate change tipping point. The latest reports were a heart-stopping analysis from the World Meteorological Organization and arresting criticism from the UN Secretary-General. Both were shared in the final days of April.

Artificial Intelligence will determine whether we blow through the tipping point or row back from the brink.

AI is one of the significant tools left in the fight against climate change. AI has turned its hand to risk prediction, the prevention of damaging weather events, such as wildfires and carbon offsets. It has been described as vital to ensuring that companies meet their ESG targets.

Yet, it’s also an accelerant. AI requires vast computing power, which churns through energy when designing algorithms and training models. And just as software ate the world, AI is set to follow.

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AI will contribute as much as $15.7 trillion to the global economy by 2030, which is greater than the GDP of Japan, Germany, India and the UK. That’s a lot of people using AI as ubiquitously as the internet, from using ChatGPT to craft emails and write code to using text-to-image platforms to make art.

The power that AI uses has been increasing for years now. For example, the power required to train the largest AI models doubled roughly every 3.4 months, increasing 300,000 times between 2012 and 2018.

This expansion brings opportunities to solve major real-world problems in everything from security and medicine to hunger and farming. It will also have a punitive impact on climate change.

The cost of high energy

Computing goes hand-in-hand with high energy costs and a larger carbon footprint, which tap the accelerator pedal on the world’s climate change.

This is especially true for AI. The huge number of GPUs running machine learning algorithms get hot and need to be cooled; otherwise, they melt. Training even one large language model (LLM) requires an eye-watering amount of energy with a large carbon footprint.

For example:

As we move into the GPT4 era and the models get larger, the energy needed to train them grows. GPT-3 was 100 times larger than its predecessor GPT, and GPT-4 was ten times the size of GPT-3. All the while, larger models are being released quicker. GPT-4 arrived in March 2023, nearly four months after ChatGPT (powered by GPT-3.5) was released at the end of November 2022.

For balance, we shouldn’t assume that as new models and companies emerge in the space AI’s carbon footprint will continue growing. Geeta Chauhan, an AI engineer at Meta, is using open-source software to reduce the operational carbon footprint of LLMs. Her latest work shows a 24-fold reduction in carbon emissions compared with GPT-3.

However, AI’s popularity and its exponential power undermine much of the climate action in force today and call into question its potential to be part of the solution.

We need a solution that allows AI to flourish while arresting its carbon footprint. So, what do we do?

Tempering the carbon addiction

As always, technology will drag us out of this predicament.

For the explosion of AI to be sustainable, advanced computing must come to the fore and do the heavy lifting for many tasks that are currently performed by AI. The good news is that we already have advanced computing technologies that are primed to execute these tasks more efficiently and quickly than AI, with the added benefit of using much, much less energy.

In short, advanced computing is the most effective tool we have to temper AI’s carbon addiction. With it, we can slow the creep of climate change.

There are a number of different technologies in advanced computing emerging that can solve some of the problems AI is currently tackling.

For example, quantum computing is superior to AI in drug discovery. As humans live longer, they are encountering, in ever greater numbers, new diseases that are complex and untreatable. This is called the “better than The Beatles” problem, where new drugs have modest improvements on already successful therapeutics.

So far, drug development has focused on rare events within a dataset and making educated guesses to design the right drugs to target and bind to the proteins that cause disease. LLMs can be efficiently used to help with this task.