I don’t know what dialect this is written in, but can anybody translate it to engineer? What type of problem are they trying to solve and how are they going about it? (Is this DRM for AIs?)
"Are you running the AI that you thought you were running, or a rip-off clone that will sneakily insert adverts for Acme, your one-stop-shop for roadrunner-related explosives, traps, and fake walls, into 1% of outputs? Here's how you can be sure."
(your case is not the direct point, but the measures are a part of strengthening the supply chain[1]. Other application include strengthening privacy [2])
Verifiability measures are designed to transform privacy and security promises from mere assurances into independently checkable, technical guarantees. _Generally achieving_: verification of claims (from governance/regulation, to model provenance), cryptographic attestation ensuring code integrity, enforceable transparency through append-only logs and tooling, no blind trust-but verifiable trust, a structured environment for ongoing scrutiny and improvement.
People are starting to worry about generative AI/LLM training data - copyright, bias, knowledge gaps, bad/outdated info. They are also worried about the black box of hardcoded "alignment" layers on top - which can and has introduced similar issues. Cryptographic proof of a model's training data provenance and its compute would be reassuring to large enterprises, governments, and conscientious users who are hesitant to embrace AI.
You don't want to accidentally use part of a biased social media chat LLM to summarize legislation or estimate results of a business plan.
Verifiable Compute is a new AI framework that uses hardware-based crypto to verify AI models and data. It lets companies audit and control their AI systems to make them more secure and compliant. Intel and Nvidia are supporting it.
You lose some benefits around decentralized trust & temporal anchoring. But not all. DLT are established in software supply chains and are being adapted to AI supply chain (see below). It's not indicative of a "crypto" play.
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I don’t know what dialect this is written in, but can anybody translate it to engineer? What type of problem are they trying to solve and how are they going about it? (Is this DRM for AIs?)
Allow me:
"Are you running the AI that you thought you were running, or a rip-off clone that will sneakily insert adverts for Acme, your one-stop-shop for roadrunner-related explosives, traps, and fake walls, into 1% of outputs? Here's how you can be sure."
Allow me to help extract the general value:
(your case is not the direct point, but the measures are a part of strengthening the supply chain[1]. Other application include strengthening privacy [2])
Verifiability measures are designed to transform privacy and security promises from mere assurances into independently checkable, technical guarantees. _Generally achieving_: verification of claims (from governance/regulation, to model provenance), cryptographic attestation ensuring code integrity, enforceable transparency through append-only logs and tooling, no blind trust-but verifiable trust, a structured environment for ongoing scrutiny and improvement.
[1] https://www.rand.org/pubs/research_reports/RRA2849-1.html
[2] https://security.apple.com/blog/private-cloud-compute/
People are starting to worry about generative AI/LLM training data - copyright, bias, knowledge gaps, bad/outdated info. They are also worried about the black box of hardcoded "alignment" layers on top - which can and has introduced similar issues. Cryptographic proof of a model's training data provenance and its compute would be reassuring to large enterprises, governments, and conscientious users who are hesitant to embrace AI.
You don't want to accidentally use part of a biased social media chat LLM to summarize legislation or estimate results of a business plan.
Had to use bullshitremover dot com for this one. This is the translation:
I got:
yeah is that what "Hedera Consensus Platform" is?
Wonder what happens if that fails.
You lose some benefits around decentralized trust & temporal anchoring. But not all. DLT are established in software supply chains and are being adapted to AI supply chain (see below). It's not indicative of a "crypto" play.
"Sigstore’s public signing ledgers form the foundation of trust for SLSA provenance" - https://storage.googleapis.com/gweb-research2023-media/pubto...
https://docs.sigstore.dev/logging/overview/
https://github.com/sigstore/rekor
software developer's rule #32 - "you are not Google"
Some have speculated they want to command regulations so complex that only they can adhere to them.