If we just postulate that the systems have a high error rate, I wonder why they are being adopted. They seem extremely easy to test, so I don't see why doctors or hospitals or governments should be getting tricked into buying them if they suck.

Isn't this an unrealistically black-and-white mode of thinking? Humans are complicated and have many values and perceived responsibilities. It's not healthy for them to throw them all out and act as if they only have one responsibility that needs to be maximally upheld at all costs. They should balance their actions thoughtfully.

(That logic breaks down somewhat in the case of explicitly negotiated surveillance sharing agreements.)

> Leela Chess Zero’s networks, which are trained with self-play and RL, achieve higher Elo ratings without using explicit search at test time than our transformers, which we trained via supervised learning. However, in contrast to our work, very strong chess performance (at low computational cost) is the explicit goal of this open source project (which they have clearly achieved via domain-specific adaptations). We refer interested readers to [https://arxiv.org/abs/2409.12272] (which was published concurrently to our work) for details on the current state-of-the-art and a comparison against our network.

And I don't think the criticism of their writing is on point either. I don't think they are secretly implying that their engine is better than Stockfish. And it's 100% plausible for human masters to rigorously analyze many positions with engine assistance and correctly establish whether Stockfish's evaluation is right or not.

https://www.youtube.com/watch?v=EWvNQjAaOHw

This behavior represents a contemptible lack of respect for users' privacy, but it's important to distinguish it from Nebula selling access to users' genomes.

https://www.classaction.org/media/portillov-nebula-genomics-...