FWIW here's how I resolve the article's questions currently:
1. Energy usage: Using electricity is not unethical. For many reasons we need to transition Earth to use clean energy. This is not a reason to not use new technology, nor is deep learning particularly wasteful like blockchains are.
2. Training data: Seems reasonable to see AI training as fair use. If you disagree, that's fine too, but the legal specifics of the origin of dataset isn't really fundamental to AI training so we can likely just train only on properly sourced data in some way. Many AI use cases don't have this problem anyway, like self-driving.
3. "Replacing people": I don't think you have a right to continue doing the same work your whole life if someone invents a more efficient way of doing it. We shouldn't build an economy around a busywork jobs guarantee. Plus, it's plausible this concern is a moot point, as Noah Smith argues here https://www.noahpinion.blog/p/plentiful-high-paying-jobs-in-... But I'm happy to do UBI or whatever, though UBI may not be as effective as one would hope.
4. "Incorrect information and bias". Doesn't strike me as a fundamental problem (models will get much better at this), nor an ethical question. Just know the information may be incorrect. Most of it isn't. And LLMs and AI have many use cases beyond retrieving reference information, even if most people just see Google search AI blowing it constantly.
5. Concentrating power: Seems like close-to-state-of-the-art LLMs will be commodified, etc. If it really becomes important I don't see why you couldn't just rustle up some donations for a truly open model or whatever.
1. Energy usage: Using electricity is not unethical. For many reasons we need to transition Earth to use clean energy. This is not a reason to not use new technology, nor is deep learning particularly wasteful like blockchains are.
2. Training data: Seems reasonable to see AI training as fair use. If you disagree, that's fine too, but the legal specifics of the origin of dataset isn't really fundamental to AI training so we can likely just train only on properly sourced data in some way. Many AI use cases don't have this problem anyway, like self-driving.
3. "Replacing people": I don't think you have a right to continue doing the same work your whole life if someone invents a more efficient way of doing it. We shouldn't build an economy around a busywork jobs guarantee. Plus, it's plausible this concern is a moot point, as Noah Smith argues here https://www.noahpinion.blog/p/plentiful-high-paying-jobs-in-... But I'm happy to do UBI or whatever, though UBI may not be as effective as one would hope.
4. "Incorrect information and bias". Doesn't strike me as a fundamental problem (models will get much better at this), nor an ethical question. Just know the information may be incorrect. Most of it isn't. And LLMs and AI have many use cases beyond retrieving reference information, even if most people just see Google search AI blowing it constantly.
5. Concentrating power: Seems like close-to-state-of-the-art LLMs will be commodified, etc. If it really becomes important I don't see why you couldn't just rustle up some donations for a truly open model or whatever.