> Why should we not expect a computer vision model to outperform humans on reading medical images?

Humans can identify. A computer vision model can return a statistical value. Both can make errors, but these errors are orthogonal to how we work and what is being asked of them. I think a CV model can absolutely provide value as augmentation. Identifying possible misses or a different diagnosis worth considering, but that is not what is being asked of them here. The pitch by Altman and Amodei is not to say, “This tool that might cost $1,000/month can help increase the accuracy of your diagnoses by 10%,” instead it’s, “This tool can allow you to keep 10% of your workers to monitor it and you can fire the rest. Also, the workers carry all the liability.”

> The human experts are literally just a trained biological neural network. In this domain they are not capable of anything a computer can't already do.

People need to stop making this baseless claim. Human beings are not stochastic computing devices, we are not neural networks. We don’t fully understand human cognition and intelligence. I have the highest confidence we will figure it out one day, though.

Yes, neural networks were based on a superficial view of the human brain, that’s it. For instance, it is biological impossible for the human brain to do backpropagation, which is kind of important for a modern neural net.

This really rubs me the wrong way because it's objectively false, but people keep bring it up because I think people want it to be true rather than accepting generative AI for what it is: a tool with a bunch of caveats.