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The need for responsible AI is clear.
So how can we, as an industry, meet the growing demand forAI toolswhile remaining environmentally conscious?
Establishing greener training models
Training large-scale AI systems is extremely power-intensive.
For instance, trainingChatGPT-3 consumed an estimated 1.287 GWh of electricity alone resulting in significant carbon emissions.
But its not all doom and gloom.
With up to 40% improvement in efficiency, this optimization is significant.
Open-sourcecollaborationplays a key role here.
Yet the development of new data centers can often be restricted due to their environmental impact.
Avoiding the AI sinkhole
The desire to innovate is central to any successful business.
Is the business using AI in a way that drives real business returns?
Are there other, more energy efficient means that can be implemented?
And when the technology is utilized, is my business working with the most eco-friendly AI compute providers?
Each of these questions requires a level of personal, and business-wide, accountability.
But responsible AI use is crucial if we are to avoid an AI sinkhole and minimize wasted resources.
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