48 hours a week on job/ea/planning/email/entrepreneurship/programming is pretty good. EAs that I know have expressed that they’re very impressed with how much you seem to get done, so you can be proud of that.
I think you’re going to have trouble being a successful entrepreneur using only three hours, unless you’re somehow able to outsource all of the jobs effectively. You still might find ‘dipping your toe into the waters’ to be valuable though.
I think this is smart, because the only way I can reliably know whether they’ll work is to actually do them—I’m not really able to tell in advance.
Makes sense. Although considerations of cause prioritisation can help with part of the question. To me, guiding tech development in light of risks seems important. Animal welfare lobbying not so much. Building infrastructure for EA projects—quite unclear. So the Survey/Blogging/.impact might be useful if you think building this infrastructure is useful, although 6.4 hours per week is a lot. Helping charity science looks similar—potentially decently useful.
One thing I did this year that I expect to pay large dividends was that I completed Andrew Ng’s Machine Learning course on Coursera. I would recommend this (or if you have already, then proceed to complete the more detailed Stanford Course notes) to reinforce your stats knowledge, help you apply for higher paying jobs and perhaps give some insights into next steps for getting involved with guiding tech development.
Apart from thinking about cause prioritisation and the ML course, my other question is—have you considered which location is optimal for you to be in? SF is good to visit or live. Presently, two of my Melbourne friends who are EAs have moved there and three more are in the process of doing so. Brayden, Chris, Frazer, Tara and Helen. It has unsurpassed EA connections (GW, tech entrepreneurship, LW, MIRI, CFAR, Thiel, Leverage), higher pay, progressive overall culture and good social groups. Obviously, where to live (or visit) is very person-specific but from the outside, it looks valuable to go there to visit or live.
48 hours a week on job/ea/planning/email/entrepreneurship/programming is pretty good. EAs that I know have expressed that they’re very impressed with how much you seem to get done, so you can be proud of that.
Thanks!
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I think you’re going to have trouble being a successful entrepreneur using only three hours, unless you’re somehow able to outsource all of the jobs effectively. You still might find ‘dipping your toe into the waters’ to be valuable though.
I agree on both parts of this. It doesn’t take too much time to MVP a product, but I would like to ramp up my time here. (I’m proposing to myself to aim for eight hours a week, though I don’t have super-fine-grained schedule control.) I think the low time investment is part of the reason why we didn’t launch anything.
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considerations of cause prioritisation can help with part of the question. To me, guiding tech development in light of risks seems important. Animal welfare lobbying not so much.
I agree with cause prioritization being important, and definitely owe more thought to the importance of guiding tech development and what role I can personally play in that.
As to animal welfare lobbying, I think my interest will rise or fall quite dramatically based on the outcome of the veg study. I personally think that launching that study will be a quite powerful contribution to cause prioritization, which was my goal.
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Building infrastructure for EA projects—quite unclear. So the Survey/Blogging/.impact might be useful if you think building this infrastructure is useful, although 6.4 hours per week is a lot.
I agree that I’ve hit diminishing marginal returns here and these things are not as high-impact as other things I can be doing and I aim to try to walk away from them.
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One thing I did this year that I expect to pay large dividends was that I completed Andrew Ng’s Machine Learning course on Coursera. I would recommend this (or if you have already, then proceed to complete the more detailed Stanford Course notes) to reinforce your stats knowledge, help you apply for higher paying jobs and perhaps give some insights into next steps for getting involved with guiding tech development.
This plays in quite well with some educational goals that I already have with regard to my day job that I’ve started recently. My three point plan is as follows:
Boost R knowledge with Advanced R. I didn’t know R well enough the first time around to fully appreciate this book, but now I think I do.
Boost math knowledge with Khan Academy. I wanted to learn linear algebra because it helps with machine learning, but in order to learn linear algebra I needed to brush up on my Calculus II. Well, my Calculus II wasn’t that good, so I need to brush up on my Calculus I. Calculus I also didn’t go so well, so now I’m at Algebra II. …I’ll have to level up my math knowledge from there.
...Currently I’d aim to spend two hours a week on that, but you’re right I might want to spend more. Maybe instead of increasing in my day job as much, I can spend some more time on this.
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Apart from thinking about cause prioritisation and the ML course, my other question is—have you considered which location is optimal for you to be in? SF is good to visit or live.
I’d love to visit SF for sure (I already meant to a few times, but never got around to it) and I would not be too surprised if I worked there at some point in the future. Right now, the job I have is in Chicago, though, and I think the benefits outweigh the costs for leaving my job at the moment, and I foresee that being true for at least the next six months.
Great, it seems like our views are pretty harmonised.
Boost machine learning knowledge with Introduction to Statistical Learning and the associated Stanford online course. This is what my boss at work recommended to me. I’m not sure if it’s better than Andrew NG’s course.
Yeah, I read Introduction to Statistical Learning in R (ISLR) and then went on to Ng’s course. It was when I viewed the latter that it really clicked. I still think ISLR was good though. Ng uses Matlab (bad if you’re getting used to R), provides better explanations for the math (good), has quizzes (good) and focuses more on modern approaches including neural networks (good). So I would suggest at least enrolling in the Coursera course in order to have it available as a backup.
Re math prerequisites, Khan Academy is also how I got to being able to understand machine learning. I also did a Coursera course in Calculus, which might help. This Jim Fowler is a good teacher, similarly to Sal Khan.
To start learning machine learning, you’ll need basic linear algebra—how to multiply vectors and matrices basically. For the introductory stuff, you can apply it without understanding calculus, although it’s fundamental to how machine learning works and you’ll need to learn some calculus sooner or later. But if it helps with motivation, if you know the basics of how to multiply vectors and matrices, you can definitely start Ng’s course, and learn some of the rest in tandem. In the long-run, to get good at ML clearly requires proficiency with linear algebra, calculus and statistics, as well as a general comfort with math that seems to come from practice, so it seems like we have to put in a bunch of solid hours into getting these foundations. More hours per week seems good.
I’d love to visit SF for sure (I already meant to a few times, but never got around to it) and I would not be too surprised if I worked there at some point in the future. Right now, the job I have is in Chicago, though, and I think the benefits outweigh the costs for leaving my job at the moment, and I foresee that being true for at least the next six months.
I think that the several hundred dollars it costs you to make a flight there, and a week of lost salary would pay itself off in expected future earnings from the option of working there later, new professional contacts, new insights into cause prioritisation, fun, new friends, extra passion for doing good, etc. The new Global EA Summit will be coming up too, which might be a reason to get down there. You meeting people there just feels like all-round good news to me.
Yeah, I read Introduction to Statistical Learning in R (ISLR) and then went on to Ng’s course. It was when I viewed the latter that it really clicked.
Makes sense. I think I’ll try that in the same order as well.
Re math prerequisites, Khan Academy is also how I got to being able to understand machine learning. I also did a Coursera course in Calculus, which might help.
I don’t actually know the basics of multiplying vectors and matricies (I learned them in college but forgot soon afterward), so I should learn that first.
You’ve convinced me of two changes to make:
First, I should go in sequence with my learning rather than parallel. I think I’ll aim for Khan Academy Algebra → Khan Academy Calculus I → Khan Academy Calculus II → Khan Academy Linear Algebra → Introduction to Statistical Learning → Angrew Ng’s course. (I think I’ll still do Advanced R --> Learn Hadoop in parallel, though, because my R skills are somewhat unrelated to my MR skills.) (To-do for self: re-arrange learning list.)
Second, I should spend more than 2hrs/wk on this. I can probably cut out more EA time. (To-do for self: think on this more.)
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The new Global EA Summit will be coming up too, which might be a reason to get down there. You meeting people there just feels like all-round good news to me.
Yeah, I’ll come out for the Global EA Summit and you’ve convinced me to try to make a full week of it. We have a pretty flexible vacation policy here, so I shouldn’t even lose salary. I just have to reconcile this with other vacation I plan on taking. (To-do for self: plan out vacation for 2015, watch for SF EA Summit dates.)
I’m thinking of also going to SF when Joey and Xio get around to visiting SF, but I don’t know if that’s going to be in 2015.
Sweet. My thoughts:
48 hours a week on job/ea/planning/email/entrepreneurship/programming is pretty good. EAs that I know have expressed that they’re very impressed with how much you seem to get done, so you can be proud of that.
I think you’re going to have trouble being a successful entrepreneur using only three hours, unless you’re somehow able to outsource all of the jobs effectively. You still might find ‘dipping your toe into the waters’ to be valuable though.
Makes sense. Although considerations of cause prioritisation can help with part of the question. To me, guiding tech development in light of risks seems important. Animal welfare lobbying not so much. Building infrastructure for EA projects—quite unclear. So the Survey/Blogging/.impact might be useful if you think building this infrastructure is useful, although 6.4 hours per week is a lot. Helping charity science looks similar—potentially decently useful.
One thing I did this year that I expect to pay large dividends was that I completed Andrew Ng’s Machine Learning course on Coursera. I would recommend this (or if you have already, then proceed to complete the more detailed Stanford Course notes) to reinforce your stats knowledge, help you apply for higher paying jobs and perhaps give some insights into next steps for getting involved with guiding tech development.
Apart from thinking about cause prioritisation and the ML course, my other question is—have you considered which location is optimal for you to be in? SF is good to visit or live. Presently, two of my Melbourne friends who are EAs have moved there and three more are in the process of doing so. Brayden, Chris, Frazer, Tara and Helen. It has unsurpassed EA connections (GW, tech entrepreneurship, LW, MIRI, CFAR, Thiel, Leverage), higher pay, progressive overall culture and good social groups. Obviously, where to live (or visit) is very person-specific but from the outside, it looks valuable to go there to visit or live.
Hope those thoughts help!
Thanks!
-
I agree on both parts of this. It doesn’t take too much time to MVP a product, but I would like to ramp up my time here. (I’m proposing to myself to aim for eight hours a week, though I don’t have super-fine-grained schedule control.) I think the low time investment is part of the reason why we didn’t launch anything.
-
I agree with cause prioritization being important, and definitely owe more thought to the importance of guiding tech development and what role I can personally play in that.
As to animal welfare lobbying, I think my interest will rise or fall quite dramatically based on the outcome of the veg study. I personally think that launching that study will be a quite powerful contribution to cause prioritization, which was my goal.
-
I agree that I’ve hit diminishing marginal returns here and these things are not as high-impact as other things I can be doing and I aim to try to walk away from them.
-
This plays in quite well with some educational goals that I already have with regard to my day job that I’ve started recently. My three point plan is as follows:
Boost machine learning knowledge with Introduction to Statistical Learning and the associated Stanford online course. This is what my boss at work recommended to me. I’m not sure if it’s better than Andrew NG’s course.
Boost R knowledge with Advanced R. I didn’t know R well enough the first time around to fully appreciate this book, but now I think I do.
Boost math knowledge with Khan Academy. I wanted to learn linear algebra because it helps with machine learning, but in order to learn linear algebra I needed to brush up on my Calculus II. Well, my Calculus II wasn’t that good, so I need to brush up on my Calculus I. Calculus I also didn’t go so well, so now I’m at Algebra II. …I’ll have to level up my math knowledge from there.
...Currently I’d aim to spend two hours a week on that, but you’re right I might want to spend more. Maybe instead of increasing in my day job as much, I can spend some more time on this.
-
I’d love to visit SF for sure (I already meant to a few times, but never got around to it) and I would not be too surprised if I worked there at some point in the future. Right now, the job I have is in Chicago, though, and I think the benefits outweigh the costs for leaving my job at the moment, and I foresee that being true for at least the next six months.
-
Sure did! Thank you for your time!
Great, it seems like our views are pretty harmonised.
Yeah, I read Introduction to Statistical Learning in R (ISLR) and then went on to Ng’s course. It was when I viewed the latter that it really clicked. I still think ISLR was good though. Ng uses Matlab (bad if you’re getting used to R), provides better explanations for the math (good), has quizzes (good) and focuses more on modern approaches including neural networks (good). So I would suggest at least enrolling in the Coursera course in order to have it available as a backup.
Re math prerequisites, Khan Academy is also how I got to being able to understand machine learning. I also did a Coursera course in Calculus, which might help. This Jim Fowler is a good teacher, similarly to Sal Khan. To start learning machine learning, you’ll need basic linear algebra—how to multiply vectors and matrices basically. For the introductory stuff, you can apply it without understanding calculus, although it’s fundamental to how machine learning works and you’ll need to learn some calculus sooner or later. But if it helps with motivation, if you know the basics of how to multiply vectors and matrices, you can definitely start Ng’s course, and learn some of the rest in tandem. In the long-run, to get good at ML clearly requires proficiency with linear algebra, calculus and statistics, as well as a general comfort with math that seems to come from practice, so it seems like we have to put in a bunch of solid hours into getting these foundations. More hours per week seems good.
I think that the several hundred dollars it costs you to make a flight there, and a week of lost salary would pay itself off in expected future earnings from the option of working there later, new professional contacts, new insights into cause prioritisation, fun, new friends, extra passion for doing good, etc. The new Global EA Summit will be coming up too, which might be a reason to get down there. You meeting people there just feels like all-round good news to me.
Makes sense. I think I’ll try that in the same order as well.
I don’t actually know the basics of multiplying vectors and matricies (I learned them in college but forgot soon afterward), so I should learn that first.
You’ve convinced me of two changes to make:
First, I should go in sequence with my learning rather than parallel. I think I’ll aim for Khan Academy Algebra → Khan Academy Calculus I → Khan Academy Calculus II → Khan Academy Linear Algebra → Introduction to Statistical Learning → Angrew Ng’s course. (I think I’ll still do Advanced R --> Learn Hadoop in parallel, though, because my R skills are somewhat unrelated to my MR skills.) (To-do for self: re-arrange learning list.)
Second, I should spend more than 2hrs/wk on this. I can probably cut out more EA time. (To-do for self: think on this more.)
-
Yeah, I’ll come out for the Global EA Summit and you’ve convinced me to try to make a full week of it. We have a pretty flexible vacation policy here, so I shouldn’t even lose salary. I just have to reconcile this with other vacation I plan on taking. (To-do for self: plan out vacation for 2015, watch for SF EA Summit dates.)
I’m thinking of also going to SF when Joey and Xio get around to visiting SF, but I don’t know if that’s going to be in 2015.
Excellent,
Good luck!