How to make AI greener and more efficient

Wirth Research, an engineering company that specialises in computational fluid dynamics, has become increasingly concerned with environmental sustainability.

It initially focused on the design of racing cars, allowing clients to replace expensive wind tunnel work with computerised modelling, but in recent years it has designed equipment that reduces the aerodynamic drag of lorries, and a device which reduces cold air escaping from open-fronted supermarket fridges, cutting energy use by a quarter.

The Bicester-based company also wanted to reduce the energy used by its detailed computerised modelling, which for car aerodynamics simulates around half a billion tiny cells of air. It had already adjusted the resolution of cells within each model, with a finer sub-millimetre mesh used near sharp edges.

Then, during the pandemic when it realised staff could work effectively from home, Wirth moved its computing from its own site to a renewable energy-powered datacentre in Iceland run by Verne Global. The new hardware has cut energy requirements by three-quarters and the power used is carbon neutral.

Engineering manager Rob Rowsell says that the total cost of the new equipment spread over several years, plus use of the Icelandic facility and connectivity, amounts to less than the old UK power bill. On top of that, as it plans to continue with hybrid working, the company has moved to smaller offices in a eco-friendly building.

Wirth wants to make its computing processes still more efficient. It can already halt iterations of virtual models when they stabilise rather than running them a fixed number of times, but it is looking at how it can use artificial intelligence (AI) trained on previous work to use a handful of iterations to predict a stable version of a model that would normally take much longer to reach.

The prediction would not need to be entirely accurate as the company would then carry out a few more iterations to check the model was stable. “You would end up being able to do 15 or 20 iterations rather than 100,” says Rowsell.

There is much potential to use AI to tackle climate change, says Peter van der Putten, director of decisioning and AI solutions at Massachusetts-based software provider Pegasystems and an assistant professor at Leiden University in the Netherlands.

But in recent years, AI has increasingly meant using deep learning models that require large amounts of computing and electricity to run, such as OpenAI’s GPT3 language model, trained on almost 500 billion words and using 175 billion parameters.

“Until recently, it was fashionable to come up with yet another model which was bigger,” says van der Putten. But environmental considerations are highlighting the benefits of making AI more efficient, with rising electricity costs increasing economic justifications. “Both from a financial point of view as well as from a climate point of view, small is beautiful.”

Another reason is that simpler, more efficient models can produce better results. In 2000, van der Putten co-ran a challenge where participants tried to predict which customers of an insurance company would be interested in buying cover for caravans, based on dozens of variables on thousands of people.

This featured real-life noisy data, which can lead complex models astray. “You might start to see patterns where there are none. You start to overfit data,” says van der Putten. This problem occurs when training data is not quite the same as the data for which predictions are required – such as when they cover two different sets of people. Simpler models also work well when there are clear relationships or when there are only a few data points.

It can also be difficult and expensive to revise big models trained on vast amounts of data. For evolving situations, such as allocating work to a group of employees with lots of joiners and leavers, lighter “online learning” models designed to adapt quickly based on new information can be the best option.

Van der Putten says that as well as being cheaper and having less environmental impact, these models are also easier to interpret. There is also the option of using classic machine learning algorithms, such as support vector machines, used to classify items, which tend to be lighter as they were developed in times of much more limited computing power.

Van der Putten says that AI specialists divided into tribes favouring specific techniques from the late 1980s and early 1990s, but practitioners then learnt to use different approaches in different situations or combine them. “Getting back to more of a multi-disciplinary approach would be healthy” he says of things now, given the alternatives to big data-driven deep learning generally use much less computing power.