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openmined

3 captures, most recent first.

Michiel Bakker @bakkermichiel

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Michiel Bakker [verified]
@bakkermichiel

Super important work from @AVERIorg @openminedorg , @GoogleDeepMind and @MLCommons that deserves much more attention, I think! It's the first double-blind eval of a closed-weight model.

A quick post on why I think this is important.

Normally, there are only two options: 1) an evaluator shares their evals with a lab, meaning the lab could game those evals or 2) the lab shares its model weights with the evaluator, creating huge IP and security risks.

In this work, both the evals and model weights remain private inside a "secure enclave": The lab (GDM in this case) can't see the eval prompts, and the evaluator can't see the model weights. Cryptographic attestation verifies that the agreed model and evals were actually used.
Note from Claude Sonnet 5

Michiel Bakker tweet describing a joint AVERI / OpenMined / Google DeepMind / MLCommons double-blind evaluation of a closed-weight model, run inside a secure enclave with cryptographic attestation so the lab cannot see eval prompts and the evaluator cannot see weights. Text is cut at the bottom edge of the screenshot (continues beyond the capture).

ai evaluationsecure enclavedouble-blind evalclosed-weight modelscryptographic attestationgoogle deepmindopenmined

@koenvanderveen

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Koen van der Veen @koenvanderveen · 1h
Today, we released our work on double blind eval of frontier models using PySyft. To the best of my knowledge this is the first of its kind. Over the last ~10 months we rebuilt PySyft from the ground up with the OM team. Still a lot to do but very happy with these results!
Note from Claude Sonnet 5

A short X post by Koen van der Veen announcing a double-blind evaluation of frontier models built on a ground-up rebuild of PySyft, done with the OM team.

pysyftdouble-blind evaluationprivacy-preserving evalsopenminedai governance

Miles Brundage @Miles_Brundage

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Miles Brundage @Miles_Brundage · 1h
It was an honor and a ton of fun to work on this unique collaboration between AVERI, Google DeepMind, OpenMined, and MLCommons.

Getting AI governance right will involve both technical and institutional innovation, and both played a role here!

[quoted post]
AVERI @AVERIorg · 1h
Today we're announcing a historic milestone:

The first ever double-blind evaluation of a proprietary language model. This was made possible by a unique collaboration between ...

[2 replies, 7 reposts, 34 likes, 1.6K views]

Miles Brundage @Miles_Brundage
This diagram summarizes the different roles played by each party.

See our blog post and the technical report for a lot more detail!

[attached diagram, cut off at the bottom of the screenshot; visible portion]
GOOGLE DEEPMIND (lock icon)
Proprietary model weights
  Encrypted by Google DeepMind (arrow down)

MLCOMMONS (lock icon)
Never-before-seen benchmark prompts
  Encrypted by AVERI (arrow down)

SECURE ENCLAVE
ENGINEERED BY OPENMINED
[further contents of the diagram cut off]
Note from Claude Sonnet 5

Miles Brundage announces a collaboration between AVERI, Google DeepMind, OpenMined and MLCommons that produced the first double-blind evaluation of a proprietary language model, with a diagram of the roles: DeepMind's encrypted model weights and MLCommons' never-before-seen benchmark prompts (encrypted by AVERI) flowing into a secure enclave engineered by OpenMined. The diagram is cut off at the bottom.

ai governancedouble-blind evaluationsecure enclavethird-party evaluationaverigoogle deepmindopenminedmlcommons