Detailed MVP description: website with an interactive map that shows locations of high risk data centers globally, with relevant information appearing when you click on the icons on the map. Examples of relevant information: organizations and frontier labs that have access to this compute, the effective FLOPS of the data center, what time would it take to train a SOTA model in that datacenter).
High risk datacenters are datacenters that are capable of training current or next generation SOTA AI systems.
Why:
I’m unable to find a ‘single point of reference’ for information about the number and locations of datacenters that are high risk.
AFAICT Epoch focuses more on tracking SOTA model details instead of hardware related information.
This seems extremely useful for our community (and policy makers) to orient to compute regulation possibilities and its relative prioritization compared to other interventions
Thoughts? I’ve been playing around with the idea of building it, but have been uncertain about how useful this would be, since I don’t have enough interaction with the AI alignment policy people here. Posting it here is an easy test to see whether it is worth greater investment or prioritization.
Note: Uncertain as to whether dual-use issues exist here. I expect that datacenter builders and frontier labs probably have a very good model of the global compute distribution situation and this would significantly benefit regulatory efforts compared to helping increase the strategic allocation of training compute allocation.
Collections of datacenter campuses sufficiently connected by appropriate fiber optic probably should count as one entity for purposes of estimating training potential, even in the current synchronous training paradigm. My impression is that laying such fiber optic is both significantly easier and significantly cheaper than building power plants or setting up power transmission over long distances in the multi-GW range.
Thus for training 3M GPUs/6GW scale models ($100 billion in infrastructure, $10 billion in cost of training time), hyperscalers “only” need to upgrade the equipment and arrange for “merely” on the order of 1GW in power consumption at multiple individual datacenter campuses connected to each other, while everyone else is completely out of luck. This hypothetical advantage makes collections of datacenter campuses an important unit of measurement, and also it would be nice to have a more informed refutation or confirmation that this is a real thing.
Seems like a useful resource to have out there. Some other information that would be nice to have are details about the security of the data center—but there’s probably limited information that could be included [1].
Project proposal: EpochAI for compute oversight
Detailed MVP description: website with an interactive map that shows locations of high risk data centers globally, with relevant information appearing when you click on the icons on the map. Examples of relevant information: organizations and frontier labs that have access to this compute, the effective FLOPS of the data center, what time would it take to train a SOTA model in that datacenter).
High risk datacenters are datacenters that are capable of training current or next generation SOTA AI systems.
Why:
I’m unable to find a ‘single point of reference’ for information about the number and locations of datacenters that are high risk.
AFAICT Epoch focuses more on tracking SOTA model details instead of hardware related information.
This seems extremely useful for our community (and policy makers) to orient to compute regulation possibilities and its relative prioritization compared to other interventions
Thoughts? I’ve been playing around with the idea of building it, but have been uncertain about how useful this would be, since I don’t have enough interaction with the AI alignment policy people here. Posting it here is an easy test to see whether it is worth greater investment or prioritization.
Note: Uncertain as to whether dual-use issues exist here. I expect that datacenter builders and frontier labs probably have a very good model of the global compute distribution situation and this would significantly benefit regulatory efforts compared to helping increase the strategic allocation of training compute allocation.
Collections of datacenter campuses sufficiently connected by appropriate fiber optic probably should count as one entity for purposes of estimating training potential, even in the current synchronous training paradigm. My impression is that laying such fiber optic is both significantly easier and significantly cheaper than building power plants or setting up power transmission over long distances in the multi-GW range.
Thus for training 3M GPUs/6GW scale models ($100 billion in infrastructure, $10 billion in cost of training time), hyperscalers “only” need to upgrade the equipment and arrange for “merely” on the order of 1GW in power consumption at multiple individual datacenter campuses connected to each other, while everyone else is completely out of luck. This hypothetical advantage makes collections of datacenter campuses an important unit of measurement, and also it would be nice to have a more informed refutation or confirmation that this is a real thing.
Seems like a useful resource to have out there. Some other information that would be nice to have are details about the security of the data center—but there’s probably limited information that could be included [1].
Because you probably don’t want too many details about your infosec protocols out there for the entire internet to see.