Hey, I’m the founder of Algoverse. There are two similar but different things here: our main AI research program, which is tuition-funded, and our AI Safety Fellowship, which is free for participants and funded by philanthropic organizations. The one on the roadmap is actually the free fellowship, but I’ll address the paid model, since that seems to be the underlying concern.
On the tuition-funded program, the reason we charge tuition is that we are providing structured mentorship, advising, and compute. This is loosely analogous to why a Master’s program costs tuition, but at a smaller scale. A lot of students who are interested in AI research do not have realistic access to university labs, especially high-school students, students at non-target schools, students at universities where AI labs are extremely competitive, and people from non-traditional backgrounds. Our goal is to give some of those students a real path into research rather than have access depend almost entirely on being at the right school, in the right geography, with the right informal network.
We also take financial accessibility seriously, so we have intentionally priced our program at a fraction of other research programs in the space (e.g Veritas AI) and offer substantial financial aid scholarships. We’ve helped numerous talented students from under-resourced countries like Egypt, Nepal, and Ethiopia, get opportunities in AI research that they would not have otherwise.
It’s also worth mentioning the baseline accessibility problem in AI research before our existence. From what I saw empirically back when I was at Berkeley, the students who got into the very limited research opportunities were disproportionately from financially privileged backgrounds, e.g private schools like Harker, Bellarmine, Exeter, and a strong concentration around SF Bay Area. I don’t know how to best quantify our impact on the full income distribution, but I’m confident that we’ve helped a significant number of students at the least-privileged end of the distribution get opportunities that they wouldn’t have had otherwise.
On venue quality: I agree this is a real field-wide issue. The recent explosion in submissions is enormous across the field. Our papers are a rounding error within it, which is also one of the reasons why I think the Guardian article’s narrative was misleading, among other reasons (happy to go deeper on that if there is interest). The bar for acceptance for our submissions was the same as the bar for everyone else. We do not control the acceptance bar, and I would be happy to see workshops and conferences raise standards across the board.
It is also worth noting that a large fraction of our paid-program research is still AI safety-related. Many students come in broadly interested in AI/ML, and we are often able to direct that interest toward interpretability, evals, and other safety-adjacent areas rather than pure capabilities work. I see this as a big win based on my beliefs on AI, which I think are pretty aligned with a lot of people here.
As for whether to trust the rest of the programs on the list in the original post, FWIW I can confirm from my experience in the field that this is an accurate list.
Hey, I’m the founder of Algoverse. There are two similar but different things here: our main AI research program, which is tuition-funded, and our AI Safety Fellowship, which is free for participants and funded by philanthropic organizations. The one on the roadmap is actually the free fellowship, but I’ll address the paid model, since that seems to be the underlying concern.
On the tuition-funded program, the reason we charge tuition is that we are providing structured mentorship, advising, and compute. This is loosely analogous to why a Master’s program costs tuition, but at a smaller scale. A lot of students who are interested in AI research do not have realistic access to university labs, especially high-school students, students at non-target schools, students at universities where AI labs are extremely competitive, and people from non-traditional backgrounds. Our goal is to give some of those students a real path into research rather than have access depend almost entirely on being at the right school, in the right geography, with the right informal network.
We also take financial accessibility seriously, so we have intentionally priced our program at a fraction of other research programs in the space (e.g Veritas AI) and offer substantial financial aid scholarships. We’ve helped numerous talented students from under-resourced countries like Egypt, Nepal, and Ethiopia, get opportunities in AI research that they would not have otherwise.
It’s also worth mentioning the baseline accessibility problem in AI research before our existence. From what I saw empirically back when I was at Berkeley, the students who got into the very limited research opportunities were disproportionately from financially privileged backgrounds, e.g private schools like Harker, Bellarmine, Exeter, and a strong concentration around SF Bay Area. I don’t know how to best quantify our impact on the full income distribution, but I’m confident that we’ve helped a significant number of students at the least-privileged end of the distribution get opportunities that they wouldn’t have had otherwise.
On venue quality: I agree this is a real field-wide issue. The recent explosion in submissions is enormous across the field. Our papers are a rounding error within it, which is also one of the reasons why I think the Guardian article’s narrative was misleading, among other reasons (happy to go deeper on that if there is interest). The bar for acceptance for our submissions was the same as the bar for everyone else. We do not control the acceptance bar, and I would be happy to see workshops and conferences raise standards across the board.
It is also worth noting that a large fraction of our paid-program research is still AI safety-related. Many students come in broadly interested in AI/ML, and we are often able to direct that interest toward interpretability, evals, and other safety-adjacent areas rather than pure capabilities work. I see this as a big win based on my beliefs on AI, which I think are pretty aligned with a lot of people here.
As for whether to trust the rest of the programs on the list in the original post, FWIW I can confirm from my experience in the field that this is an accurate list.