If it can be done at scale this would be very good news.
Good perspective on how the generative AI space evolved in 2024. There are good news and more concerning ones in there. We'll see what 2025 brings.
This is not all bad news, there are a few things to rejoice about.
The water problem is obviously hard to ignore. This piece does a good job illustrating how large the impact is.
We always think about the energy consumption, but large data centers gobble billion liters of water too. This would need to be improved.
This is definitely a good idea, I wish we had the same in France. This is too bad that they plan to raise the price, it's going to limit the impact of the measure.
If you run the number, we actually can't afford this kind of generative AI arm race. It's completely unsustainable both for training and during use...
Interesting analysis... I wonder if and how Jevons paradox will get in the way though.
Excellent piece, we're a civilisation whose culture is built on shifting sands and... toy plastics. Guess what will survive us?
Need to illustrate how much the current AI arm race is an ecological and social problem? Here is a very pathological case. This is what you get when you let the tycoons behind this completely unchecked.
Funny experiment at drawing parallels between engineering leadership and how you should behave when hiking in nature. This works surprisingly well.
I'm rarely on the side of a Goldman Sachs... Still this paper seems to be spot on. The equation between the costs (financial and ecological) and the value we get out of generative AI isn't balanced at all. Also, since it is stuck on trying to improve mostly on model scale and amount of data it is doomed to plateau in its current form.
This arm race should be stopped... This is becoming an ecological disaster, so much wasted energy.
More work about eco-design of software. This is definitely welcome. I found this work a bit weak on the state of the art and the interview parts (10 people in the same company). But the field is so nascent that it's to be expected I guess, PhD students have to do with what they have access to. Unsurprisingly this shows a great lack of proper tools to tackle the measurement problem. This thesis shows interesting prospects to reduce variations in measurements though, some of the proposed guidelines might help but cannot offset the hardware heterogeneity completely... The parts focusing on practical advices around Java use and deployment are interestingly easy to apply though. You need to take into account the context of your application to make the right choices of course.
This is one of the main problems with using those generative models as currently provided. It's time for the legislators to step up, we can't let a couple of players hoard energy and water for themselves.
Interesting paper evaluating a Life Cycle Assessment (LCA) method to estimate the power consumption and environmental impact of generative AI services. This is illustrated on a single service, hopefully we'll see more such assessments.
Very interesting paper about the energy footprint of the latest trend in generator models. The conclusion is fairly clear: we should think twice before using them.
What are the hardware improvements good for if then it's all locked down through software? This is wasted.
Sure they're pulling some effort on the way their hardware is produced and cheap. But don't be fooled by the grand claims, this can't be carbon neutral.
Lays out the ethical problems with the current trend of AI system very well. They're definitely not neutral tools and currently suffer from major issues.