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AI: Balancing Social Benefits with Environmental Concerns

AI holds tremendous promise for driving economic progress and improving governance. It can address issues like executive pay scrutiny, rising living costs, climate-related disasters, and rising CO2 emissions.

The Environmental Cost of Progress

However, the energy consumption of complex machine learning models is a growing concern. Some models' energy needs have skyrocketed, raising the specter that the AI industry's energy demands could become unsustainable.

Efficiency as a Solution

Fortunately, techniques like pruning and quantization exist to reduce the computational burden of AI models and minimize their energy footprint. Knowledge distillation, as used in your human capital model, offers a less energy-intensive approach.

The Need for Balance

The environmental impact of a single deep learning model can equate to the lifetime carbon emissions of multiple cars. It's crucial to weigh AI's benefits against the environmental cost. Careful design, energy-efficient algorithms, and a shift toward sustainable power sources are essential to ensuring a responsible future for AI.

AGI and its use for supporting strategies to improve on ESG areas


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