Part 8/12:
A paradox in deploying AI and data science is their own environmental impact. Large language models and intensive computation require vast data centers, contributing significantly to global emissions. As Raghunath notes, “every AI solution doesn’t deserve to be scaled fully”—necessitating strategic, judicious use of computational resources.
An effective approach involves optimizing algorithms and infrastructure for energy efficiency, conducting cost-benefit analyses comparing environmental impact against the value derived. An idea gaining traction is an "AI tax" or carbon tax, which would incentivize responsible AI deployment consistent with sustainability goals.
Emerging Technologies and Future Directions
Looking ahead, several innovative solutions are on the horizon: