Right Time to Prioritize Energy Efficient Green Artificial Intelligence

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Artificial intelligence systems address various conflicting goals: precision (consuming vast quantities of computing resources and electrical power) and usability (being lower in cost, less computationally intensive, and less resource-hungry). Alas, many of the developments in AI applications of today are economically unsustainable. Several variables, including more effective algorithms, more efficient computational systems, and more efficient materials, would drive improvements in artificial intelligence energy efficiency.

In its recent research, the Allen Institute for AI proposed that ‘Green AI’ programs centered on AI systems’ energy efficiency should be prioritized. The research was based on the wavering carbon footprints of several high-profile developments in AI. The amount of computation required for the most significant AI training runs has been grown massively by 300,000 times since 2012, according to an OpenAI blog post. This growth in technical criteria contributes to artificial intelligence’s negative environmental impacts.

What is Energy Efficient Green AI?

Green AI refers to a broader, long-standing view in AI scientific research that is environmentally friendly. In many cases, AI study can be computationally costly; moreover, each offers innovative upgrades opportunities. A central Green artificial intelligence approach is documenting the computational price tag of discovering, training, and running models of AI.

Source : https://onpassive.com/blog/right-time-to-prioritize-energy-efficient-green-artificial-intelligence/

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