Hmm, the only overlap I can see between your recent work and this description (including optimism about very-near-term applications) is the idea of training an ensemble of models on the same data, and then if the models disagree with each other on a new sample, then we’re probably out of distribution (kinda like the Yarin Gal dropout ensemble thing and much related work).
And if we discover that we are in fact out of distribution, then … I don’t know. Ask a human for help?
If that guess is at all on the right track (very big “if”!), I endorse it as a promising approach well worth fleshing out further (and I myself put a lot of hope on things in that vein working out). I do, however, think there are AGI-specific issues to think through, and I’m slightly worried that y’all will get distracted by the immediate deployment issues and not make as much progress on AGI-specific stuff. But I’m inclined to trust your judgment :)
Hmm, the only overlap I can see between your recent work and this description (including optimism about very-near-term applications) is the idea of training an ensemble of models on the same data, and then if the models disagree with each other on a new sample, then we’re probably out of distribution (kinda like the Yarin Gal dropout ensemble thing and much related work).
And if we discover that we are in fact out of distribution, then … I don’t know. Ask a human for help?
If that guess is at all on the right track (very big “if”!), I endorse it as a promising approach well worth fleshing out further (and I myself put a lot of hope on things in that vein working out). I do, however, think there are AGI-specific issues to think through, and I’m slightly worried that y’all will get distracted by the immediate deployment issues and not make as much progress on AGI-specific stuff. But I’m inclined to trust your judgment :)