Artificial intelligence (AI) operates most efficiently when it is commoditizing intelligence and decision making. We are starting to see proof of the scientific and business benefits that come from this streamlining and processing of data, and the use of AI modelling is playing out across a wide and growing spectrum of market sectors. However, as with any form of progress, there is a cost. Training just AI models can lead to significant carbon emissions. Studies have shown that state of the art models can result in hundreds of tons of emissions, and researchers and companies alike are individually training the models for their own purposes. As a result, we now have the power of a new technology that invites an exponential growth in emissions without awareness and action.
Higher compute power means higher energy consumption
When you look across the financial service and healthcare industries, we see examples of how machine learning applications are revolutionizing products, services and research. Within the financial service industry, machine learning is changing quantitative investing from a set of algorithms based on historical data to a set of models that capture and actively react to the fluctuating changes in the market. With these new tools, focus shifts from past to future and thereby the potential to dramatically reduce the friction required to achieve the next advantage on the market. For healthcare, AI modelling is enabling better disease diagnosis and prevention, all the more urgent in the current atmosphere of a global pandemic. The amount of compute power required for today’s applications when applied at scale is orders of magnitude greater than previous generations. The greater the network depth and the greater the data quantities for input, the greater the compute complexity, all of which requires high-performance computational power and longer training times.
Prioritise efficiency as a criterion for

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