11 ms·
If we assume this is true, then it's a massive problem for AI. Building energy infra is slow and hellishly expensive. Near and medium term use cases just aren't
by alangibson 2y ago
If we assume this is true, then it's a massive problem for AI. Building energy infra is slow and hellishly expensive. Near and medium term use cases just aren't compelling enough to start upgrading the power grid.
- coffeebeqn 2y agoI wonder how feasible it would be to place a data center in somewhere like Arizona or Nevada and just run it during daytime hours on a massive solar array and then pause the work for the night time. That would probably take a few decades less than trying to permit and build a nuclear plant next to it
- ArchD 2y agoYou are right that nuclear power tends to have a very long lead time. China has already conveniently installed vast amounts of solar power.
- awongh 2y agoIf you look at the arguments for/against resource consumption for bitcoin, a lot of the same arguments hold true- that actually we don’t necessarily have a deficit of power production. We have an energy capacity problem and distribution problem. I wonder how much of this over supply can be taken up with AI training and how much that would change the calculations.
- macrolime 2y agoIt's not really all that useful for AI training. The reason why energy is a problem for AI training is that you need a lot of compute at one place, distributing backpropagation doesn't work that well. When the energy problems hit, it's when the datacenter need exceeds the capacity of nearby power plants. If we want an alternative I think we'd need to try alternative training algorithms that are easier to distribute. Predictive coding for example can approximate backprop, but requires more compute. Something in that direction might work https://arxiv.org/abs/2006.04182 https://arxiv.org/abs/2006.04182