My impression is EAs (especially 80k) think you will make an impact through research only if you are in the top few percent of researchers in the world. I think that is especially hard to achieve in biology (especially wet-lab biology) because:
Success in biology is incredibly resource constrained. So getting into a rich lab is key
Success in biology is much more luck-dependent than other fields. Intelligence is secondary.
Other reasons to not do biology:
Biology postdocs/PhDs work longer and are paid lesser than CS
Feedback cycles in biology have long time windows. This means it can take years to know your project failed. Personally, I found this incredibly demotivating but people’s tolerance for this can differ
Option value for other jobs is worse. If you have a CS degree and decide to leave academia it’s easier to get an industry job than it’s for bio
Thank you for answering, some of the points are new that I haven’t considered about.
What do you work in ML? AI safety? I want to know what EA-related things I can work in CS besides reducing AI x-risks.
If I insist in working on those bio-related topics, is it still worth getting a CS major and fewer bio?(for bioinformatics and other CS-related skills)
I work at a startup designing synthetic proteins using deep learning: https://www.evozyne.com/. Even though the products my company works on are impactful, due to counterfactuality, I think my impact is through ETG.
You don’t need a bio background to work in bio-related ML. Getting a CS degree with some bio-related courses/self-study the side seems enough. Also bioinformatics != bio-ML.
As a person who was a biologist and now does ML:
My impression is EAs (especially 80k) think you will make an impact through research only if you are in the top few percent of researchers in the world. I think that is especially hard to achieve in biology (especially wet-lab biology) because:
Success in biology is incredibly resource constrained. So getting into a rich lab is key
Success in biology is much more luck-dependent than other fields. Intelligence is secondary.
Other reasons to not do biology:
Biology postdocs/PhDs work longer and are paid lesser than CS
Feedback cycles in biology have long time windows. This means it can take years to know your project failed. Personally, I found this incredibly demotivating but people’s tolerance for this can differ
Option value for other jobs is worse. If you have a CS degree and decide to leave academia it’s easier to get an industry job than it’s for bio
Thank you for answering, some of the points are new that I haven’t considered about. What do you work in ML? AI safety? I want to know what EA-related things I can work in CS besides reducing AI x-risks. If I insist in working on those bio-related topics, is it still worth getting a CS major and fewer bio?(for bioinformatics and other CS-related skills)
I work at a startup designing synthetic proteins using deep learning: https://www.evozyne.com/. Even though the products my company works on are impactful, due to counterfactuality, I think my impact is through ETG.
You don’t need a bio background to work in bio-related ML. Getting a CS degree with some bio-related courses/self-study the side seems enough. Also bioinformatics != bio-ML.