The Hidden Cost of AI: What's the "Green" Price of Generative AI?

Artificial intelligence (AI) has been becoming key part of day to day lives of many of us. It can create images, write stories, and even translate languages. But like many things, there is a hidden cost. We often hear about the cost of development, but what about the environmental cost? This is the "green" cost of generative AI.

Think of it like this: AI models, especially the ones that generate images or text, need massive computing power. These powerful computers consume a lot of electricity. And where does that electricity come from? Often, it comes from power plants that burn fossil fuels like coal, which release greenhouse gases. These gases contribute to climate change. ?

So, every time we use AI to generate a cool image or a catchy slogan, we are indirectly contributing to this energy consumption. The more complex the AI task, the more energy it needs. Training these AI models is especially energy-intensive, sometimes requiring the equivalent of several homes' electricity consumption for a whole year! ?

Now, this doesn't mean we should stop using AI. AI has tremendous potential to help us in many ways. But it's important to be aware of the environmental impact. Just like we try to conserve energy at home, we should also think about the energy used by AI. ?

What can we do? Developers are working on making AI models more efficient, so they use less energy. We, as users, can also be mindful of how we use AI. Do we really need to generate ten different images when one or two will do? Simple changes in our usage can make a difference. ?

The "green" cost of AI is something we need to consider as we move forward. By being aware and making conscious choices, we can enjoy the benefits of AI while minimizing its impact on our planet. It's about finding a balance between technological advancement and environmental responsibility.

References

1.????? Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP.?Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 3645–3650.?https://doi.org/10.18653/v1/P19-1355

2.????? Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L. M., Rothchild, D., So, D., Texier, M., & Dean, J. (2022). The carbon footprint of machine learning training will plateau, then shrink.?Computer, 55(7), 18–28.?https://doi.org/10.1109/MC.2022.3142984

3.????? Luccioni, A., & Bengio, Y. (2020). AI and climate change: How they are connected, and what we can do about it. Harvard Data Science Review, 2(4). https://doi.org/10.1162/99608f92.5a2e1f1d