Embedded AI evaluators have a scale problem, because it's expensive, time-consuming, and risky to bring too many people into each AI company's office/infra. Here's a solution to that problem This solution is based on ~9 yrs of R&D and pilots with X, DeepMind, Anthropic, Google, Microsoft, LinkedIn, Reddit, DailyMotion, the United Nations and others.

Most external AI evaluation programs hit the same ceiling: the access model wasn't designed to scale. PySyft can split evaluation into three roles. An embedded evaluator writes a reusable job against real model assets. An internal reviewer approves it. External researchers receive filtered outputs without ever touching the underlying data or going through a new approval cycle. Our approach: openmined.org/blog/scale-emb…

🚨 OBAMA ON AI: "If we are thinking about AI just in terms of how do we cure cancer or get better energy, you can do that without having agentic AI and having it just roaming free in the internet. The reason you are doing that is because you have to market a product that people will pay money for. That’s a misalignment between what our society needs and the commercial imperatives that these companies are facing, not because necessarily they’re trying to do bad things, but because they’ve got to justify these valuations. So, that’s one more reason why it is really important for us to have a competent government and a serious bipartisan conversation around this issue, and we have to do it fast. And I would encourage voters to pay attention to this. If somebody does not have a serious plan for how to deal with this, then they’re not meeting the moment, and you should probably look for somebody else."