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πŸ€“ RAAIS OpenMined Grants for privacy-preserving ML tools

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Announcing the RAAIS OpenMined Grants for open-source privacy-preserving ML technology.
 

nathan.ai newsletter

July 23 · Issue #36 · View online
A market intelligence newsletter covering AI in the technology industry, research lab and venture capital market.

Announcing the RAAIS OpenMined Grants for open-source privacy-preserving ML technology.

Hi everyone,
Two weeks ago, we announced that the RAAIS Foundation, a non-profit focused advancing education and research in AI for the common good, and OpenMined, the leading open-source community for privacy-preserving ML technology, have joined forces to launch the RAAIS OpenMined Grant programme (blog post here). The grants support 3 months of full-time paid work on open-source privacy-preserving ML technology applied to 1) genomics data, 2) encrypted machine translation and 3) federated data science. Applications are open to individual contributors anywhere in the world.
✍🏽 How to apply
To apply, send your resume/CV before Friday July 26th to andrew@openmined.org along with your Github username, which of the projects you would be interested in working on (you may pick more than one), and a description of why you are interested in the project.
πŸ™‹ Why is the RAAIS Foundation doing this?
A core belief of The RAAIS Foundation is that the real-world impact of AI will go well beyond solely for-profit applications. Moreover, investing to create opportunities for diverse participation in advancing AI work in the open-source is crucial to ensuring long-term value alignment and a healthy community.
The reason we created the RAAIS OpenMined Grant is because data privacy and AI model ownership are two of the most pertinent topics in AI today. Significant value has accrued to large organisations as they understandably act as centers of gravity for talent and capital. However, for the long-term benefits of AI to be enjoyed by everyone, its important to work on distributing tools, data and ownership more broadly.
The OpenMined system packages multiparty computation, differential privacy, federated learning, homomorphic encryption and novel governance mechanisms. It allows an AI model to be governed by multiple owners and trained securely on an unseen, distributed dataset. This contrasts with industry standard tools for AI that have been designed under the assumption that data is centralized into a compute cluster, the cluster exists in a secure cloud, and the resulting models will be owned by a central authority.
If you’re looking to get your hands dirty working on important AI problems for the benefit society more broadly, I encourage you to apply :-)
–
Nathan
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