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AI has the potential to help us tackle the problems associated with climate change and the warming of our Earth. The closer we get to the precipice, the greater the urgency. This has helped fuel tremendous growth in AI projects throughout government and the public sector, where AI is being used to make more accurate climate change predictions or to intelligently power the infrastructure that could support lower emissions on a global scale. 

Amidst all this enthusiasm, the one thing often being left out of the conversation is the carbon cost of these compute-intensive solutions. At best, the adoption of AI might be slowed down because people hadn't adequately considered the cost (financial or environmental) of the solution required. At worst, it could accelerate the warming of our planet. 

This is why it is so important to develop a Green AI technology: a technology that takes into account energy-efficiency as an important evaluation metric.

Quantum computers have the potential to be exponentially faster than traditional computers, revolutionising the way we currently work. While we are still years away from general-purpose Quantum Computing, Bayesian Optimisation can help to stabilise quantum circuits for certain applications. This blog will summarise how Mind Foundry Optimise did just that.

Even with low cost widely available cloud computing, it can take significant time and compute power to train machine learning models on large data sets. This is expensive and is often at odds with the net-zero carbon goals of many organisations today. Throwing more data at a problem isn’t always the best answer, and by using AI that is responsible by design, we can reduce these problems while maintaining performance. Active learning is one of the methods we use at Mind Foundry to achieve this.