While I enjoyed this post, I wanted to indicate a couple of reasons why you may want to instead stay in academia or industry, rather than being an independent researcher:
The first one is that it gives more financial stability.
The second is that academia or industry set the bar high. If you get to publish in a good conference and get substantial citations, you know that you are making progress.
Now, many will argue that Safety is still preparadigmatic and consequently there might be contributions that do not really fit well into standard academic journals or conferences. My answer to this point is that we should aim to make AI Safety become paradigmatic. We should really try to get our hands dirty into technical problems and solve them. I think there is a risk of staying at the conceptual level of research agendas for too long and not getting much done. In fact, I have anecdotic experience (https://twitter.com/CraigGidney/status/1489803239956508672?s=20&t=nNSjfZjqYfbUQ4hvmghoHw) of well-known researchers in a field other than AI Safety that do not get to work on it because they find it hard to measure progress or to have intuitions of what works. I argue: we want to make the field paradigmatic so that it becomes just another academic research field.
I also want to cite another important point against becoming an independent researcher: if you work alone it may take you longer to do any high-quality research done. Developing intuitions takes time, and supervision makes everything so much easier. I know that the community is short of supervision, but perhaps taking a good supervisor who does not directly work on AI Safety, but is happy for you to do so and whose research seems useful as tools might be a great idea.
So in summary: we want high-quality research, we want to be able to measure its high quality, and we want to make the field more concrete and grounded so that we can attract tons of academics.
While I enjoyed this post, I wanted to indicate a couple of reasons why you may want to instead stay in academia or industry, rather than being an independent researcher:
The first one is that it gives more financial stability.
The second is that academia or industry set the bar high. If you get to publish in a good conference and get substantial citations, you know that you are making progress.
Now, many will argue that Safety is still preparadigmatic and consequently there might be contributions that do not really fit well into standard academic journals or conferences. My answer to this point is that we should aim to make AI Safety become paradigmatic. We should really try to get our hands dirty into technical problems and solve them. I think there is a risk of staying at the conceptual level of research agendas for too long and not getting much done. In fact, I have anecdotic experience (https://twitter.com/CraigGidney/status/1489803239956508672?s=20&t=nNSjfZjqYfbUQ4hvmghoHw) of well-known researchers in a field other than AI Safety that do not get to work on it because they find it hard to measure progress or to have intuitions of what works. I argue: we want to make the field paradigmatic so that it becomes just another academic research field.
I also want to cite another important point against becoming an independent researcher: if you work alone it may take you longer to do any high-quality research done. Developing intuitions takes time, and supervision makes everything so much easier. I know that the community is short of supervision, but perhaps taking a good supervisor who does not directly work on AI Safety, but is happy for you to do so and whose research seems useful as tools might be a great idea.
So in summary: we want high-quality research, we want to be able to measure its high quality, and we want to make the field more concrete and grounded so that we can attract tons of academics.