I’m trying to decide if I should be concerned about the safety of general-purpose computing with such technologies sneaking into our compute. Verifying compute workloads is one thing, but I can’t find information on what kind of regulatory compliance controls this addition enables. I assume it is mostly just operation counting and other audit logging discussed in AI safety whitepapers, but even that feels disturbing to me.
Also, bold claim: silicon fabrication scarcity is artificial and will be remedied shortly after Taiwan is invaded by China and the world suddenly realizes it needs to (and can profit from) acquiring this capability. Regulatory approaches based on hardware factors will probably fail in the face of global competition on compute hardware.
Reads as compliance controls being embedded into the code with integrated gates to halt execution, or verify controls are met at runtime - providing receipts with computed outputs. This is generally oriented toward multi-party, confidential, sensitive computing domains. As AI threat models develop, general compliance of things during training, or benchmarking, etc become more relevant as security posture requires.
Cryptographic proof that the model you’re using is the one you think it is along with proof it was not trained on biased or copyrighted data is the main feature here. Think certs when looking at webpages or ssh when looking at servers.
Comments
I’m trying to decide if I should be concerned about the safety of general-purpose computing with such technologies sneaking into our compute. Verifying compute workloads is one thing, but I can’t find information on what kind of regulatory compliance controls this addition enables. I assume it is mostly just operation counting and other audit logging discussed in AI safety whitepapers, but even that feels disturbing to me.
Also, bold claim: silicon fabrication scarcity is artificial and will be remedied shortly after Taiwan is invaded by China and the world suddenly realizes it needs to (and can profit from) acquiring this capability. Regulatory approaches based on hardware factors will probably fail in the face of global competition on compute hardware.
Reads as compliance controls being embedded into the code with integrated gates to halt execution, or verify controls are met at runtime - providing receipts with computed outputs. This is generally oriented toward multi-party, confidential, sensitive computing domains. As AI threat models develop, general compliance of things during training, or benchmarking, etc become more relevant as security posture requires.
ref:
https://openai.com/index/reimagining-secure-infrastructure-f...
https://security.apple.com/blog/private-cloud-compute/
https://arxiv.org/html/2409.03720v2
Thanks for the reading.
Cryptographic proof that the model you’re using is the one you think it is along with proof it was not trained on biased or copyrighted data is the main feature here. Think certs when looking at webpages or ssh when looking at servers.