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Comment by rvz | Hacker News Reader

> We found a way: nuclear fission reactors.

Nope. I'm talking about efficient methods in training, inferencing and fine-tuning these AI models that doesn't require lots of data centers, TPUs, GPUs, etc. You're talking about something else.

Petrol and diesel cars are already burning the planet, but the main difference is, that there are efficient alternatives available today like electric cars to use instead.

AI (Deep learning) however, does not have any viable and efficient methods in training, fine-turning these AI models, at all [0] [1] and wastes a tremendous amount of resources, all to keep up with scalability.

So that problem is still NOT solved after a decade of using GPUs, the wastage is getting worse.

[0] https://gizmodo.com/chatgpt-ai-water-185000-gallons-training...

[1] https://www.independent.co.uk/tech/chatgpt-data-centre-water...

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> efficient methods in training, inferencing and fine-tuning these AI models that doesn't require lots of data centers, TPUs, GPUs, etc.

Exactly the types of problems future AI models could solve.

Dire climate alarms are based on the predictions made using models. As modelling advances as a field, both predictions, and solutions become more and more voluminous and accurate, along with revealing mistakes and failures of prior models.

Anyone concerned with climate should rally behind this kind of general progress. Further, it simply is progressing, and fields that don't embrace it, will be left behind. We're in the midst of an unprecedented revolution which touches all.

> efficient methods in training, inferencing and fine-tuning these AI models

Which can also be archived by training more with the same amount of spent energy.

Why learn about training ("make training more efficient") on old hardware, which is more energy inefficient?

It goes more fundamental than that in the algorithms and it should not take tens of billions of dollars with multiple data centers to train, learn, fine-tune and do inference with these AI models. A decade later, there are no viable alternatives to solve that instead of the costly replacement of hardware with more expensive hardware.

Add that towards scalability and you will realize that training AI models scales terribly with more data as it is very energy and time inefficient. Even if you replace all the hardware in the data centers it still wouldn't reduce the emissions regardless and replacing them also costs at most billions either way. That is my the entire point.

So that does nothing to solve the issue. Only ignores and prolongs it.

> A decade later, there are no viable alternatives to solve that instead of the costly replacement of hardware with more expensive hardware.

I mean, that's the root of scaling as a principle, right?

You could viably start training an AI on your cell phone. It would be completely useless, lack meaningful parameter saturation and take months to reach an inferencing checkpoint, but you could do it. Nvidia is offering a similar system to people, but at a scale that doesn't suck like a cellphone does. Businesses can then choose how much power they need on-site, or rent it from a cloud provider.

If a product like this convinces some customers to ditch older and less efficient training silicon, I don't see how it's any more antagonistic than other CPU designers with perennial product updates.

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