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Joined 2 years ago
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Cake day: June 10th, 2023

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  • Don’t misunderestimate Dubya. Irregardless of his policies, he coined words and phrases like Shakespeare. How many of use of dreamed of human beings and fish coexisting peacefully? How many of us strive to put food on our families and buy most of our imports from overseas? Don’t we all want to know “is our children learning?” And who can forget that famous Texas proverb “fool me once, shame on - shame on you. Fool me - you can’t get fooled again.




  • My understanding of quantum computers is that they’re great a brute forcing stuff, but machine learning is just a lot of calculations, not brute forcing.

    If you want to know the square root of 25, you don’t need to brute force it. There’s a direct way to calculate the answer and traditional computers can do it just fine. It’s still going to take a long time if you need to calculate the square root of a billion numbers.

    That’s basically machine learning. The individual calculations aren’t difficult, there’s just a lot to calculate. However, if you have 2 computers doing the calculations, it’ll take half the time. It’ll take even less time if you fill a data center with a cluster of 100,000 GPUs.



  • It’s mostly the training/machine learning that is power hungry.

    AI is essentially a giant equation that is generated via machine learning. You give it a prompt with an expected answer, it gets run through the equation, and you get an output. That output gets an error score based on how far it is from the expected answer. The variables of the equation are then modified so that the prompt will lead to a better output (one with a lower error).

    The issue is that current AI models have billions of variables and will be trained on billions of prompts. Each variable will be tuned based on each prompt. That’s billions to the power of billions of calculations. It takes a while. AI researchers are of course looking for ways to speed up this process, but so far it’s mostly come down to dividing up these billions of calculations over millions of computers. Powering millions of computers is where the energy costs come from.

    Unless AI models can be trained in a way that doesn’t require running a billion squared calculations, they’re only going to get more power hungry.






  • You’re not alone! I worked 12 hours in 37°C (99°F), 47% humidity yesterday. However, we get essentially unlimited breaks in an air conditioned break room, have cooling vests filled with ice packs we can wear on the floor, and are supplied with sports drinks and feeezies. Your work can’t really make the world less hot, but they can work with you to avoid development of heat related illnesses!