I have been toying with the idea of starting a similar org (with more of a focus on OR) so excited to see this.
One suggestion I have (that I might be able to help with) is to offer a larger “free tier” that taps into the talent pool within academia. Three reasons why this is good:
I imagine most orgs have a long tail of data science projects which aren’t important enough to go through the hassle of hiring a consultant, but that would still add some value. Meanwhile, students are in constant search of important real world problems to work on for their research or clubs (I was in Cornell Data Science) but generally don’t have a good idea of what would actually be useful. Having a place where orgs can just write down such problems and students/academics can find them seems like it would potentially unlock a lot of value.
Based on feedback of pitching a similar idea at EAG, most of the value isn’t actually in the object level work, but in identifying altruistic technical talent and getting them more engaged in high impact cause areas (and eventually into the hiring pipeline). Having lots of undergrads and PhD students working on EA style data problems seems like a good way of doing this.
Normalize X-risk and other more niche topics within academia.
I think both your ideas can have a lot of impact. If you start doing data for good competitions or challenges, please notify me, I think there are lots of computer science, industrial engineers and data science student in my area in Germany, that would like to participate in such competitions (at universities in Karlsruhe, Mannheim, Heidelberg and Darmstadt).
I imagine most orgs have a long tail of data science projects which aren’t important enough to go through the hassle of hiring a consultant, but that would still add some value. Meanwhile, students are in constant search of important real world problems to work on for their research or clubs (I was in Cornell Data Science) but generally don’t have a good idea of what would actually be useful. Having a place where orgs can just write down such problems and students/academics can find them seems like it would potentially unlock a lot of value.
I definitely agree. Optimally SEADS will provide this list of impactful projects.
Based on feedback of pitching a similar idea at EAG, most of the value isn’t actually in the object level work, but in identifying altruistic technical talent and getting them more engaged in high impact cause areas (and eventually into the hiring pipeline). Having lots of undergrads and PhD students working on EA style data problems seems like a good way of doing this.
Hiring for EA is also on our list of “Possible directions for the future”. Working hand-in-hand with talented and motivated volunteers seems like a good way to gauge someone’s suitability for a long-term position.
I have been toying with the idea of starting a similar org (with more of a focus on OR) so excited to see this.
One suggestion I have (that I might be able to help with) is to offer a larger “free tier” that taps into the talent pool within academia. Three reasons why this is good:
I imagine most orgs have a long tail of data science projects which aren’t important enough to go through the hassle of hiring a consultant, but that would still add some value. Meanwhile, students are in constant search of important real world problems to work on for their research or clubs (I was in Cornell Data Science) but generally don’t have a good idea of what would actually be useful. Having a place where orgs can just write down such problems and students/academics can find them seems like it would potentially unlock a lot of value.
Based on feedback of pitching a similar idea at EAG, most of the value isn’t actually in the object level work, but in identifying altruistic technical talent and getting them more engaged in high impact cause areas (and eventually into the hiring pipeline). Having lots of undergrads and PhD students working on EA style data problems seems like a good way of doing this.
Normalize X-risk and other more niche topics within academia.
@wesg: I really liked your OR post.
I think both your ideas can have a lot of impact. If you start doing data for good competitions or challenges, please notify me, I think there are lots of computer science, industrial engineers and data science student in my area in Germany, that would like to participate in such competitions (at universities in Karlsruhe, Mannheim, Heidelberg and Darmstadt).
I definitely agree. Optimally SEADS will provide this list of impactful projects.
Hiring for EA is also on our list of “Possible directions for the future”. Working hand-in-hand with talented and motivated volunteers seems like a good way to gauge someone’s suitability for a long-term position.