I originally helped design the course and I ran the first iteration of a similar program. I’m not really involved with the course now but I think I’m qualified to answer. However, I did AGI safety fundamentals a long time ago and haven’t done MLAB, so my knowledge of those could be wrong (though I don’t think so).
In comparison to AGI Safety Fundamentals, this course a lot more technical and less conceptual. AGISF is not going to include the latest in machine learning on a technical level, and this course doesn’t include as many conceptual readings.
In comparison with MLAB, this course is more focused on reading papers and understanding research, and less focused on teaching particular frameworks or engineering skills.
There’s a bit of overlap between all, but it’s pretty minimal. I think anyone who has done any of these programs would learn something from doing the others. It mostly depends on what people want to take out of the course: knowledge of a lot of different conceptual research directions (AGISF), skills in engineering with Pytorch (MLAB), or knowledge of the frontier of ML safety research and paper reading skills (Intro to ML Safety).
Thanks!
Is there a list anywhere of ways to upskill in ML for AI Safety Engineering, such as MLAB too?
Do you have an opinion on when someone should pick your course over some other course?
(I’m asking because I often hear from people trying to upskill in ML and I’m not sure what to tell them, I hope someone can comment here and help)
I’ll refer someone to this post right now
I originally helped design the course and I ran the first iteration of a similar program. I’m not really involved with the course now but I think I’m qualified to answer. However, I did AGI safety fundamentals a long time ago and haven’t done MLAB, so my knowledge of those could be wrong (though I don’t think so).
In comparison to AGI Safety Fundamentals, this course a lot more technical and less conceptual. AGISF is not going to include the latest in machine learning on a technical level, and this course doesn’t include as many conceptual readings.
In comparison with MLAB, this course is more focused on reading papers and understanding research, and less focused on teaching particular frameworks or engineering skills.
There’s a bit of overlap between all, but it’s pretty minimal. I think anyone who has done any of these programs would learn something from doing the others. It mostly depends on what people want to take out of the course: knowledge of a lot of different conceptual research directions (AGISF), skills in engineering with Pytorch (MLAB), or knowledge of the frontier of ML safety research and paper reading skills (Intro to ML Safety).